tag:blogger.com,1999:blog-72219564191341798302024-03-19T01:22:00.728+02:00אשרי אדם מפחד תמיד Happy is the man who always fearsDual language postsUnknownnoreply@blogger.comBlogger155125tag:blogger.com,1999:blog-7221956419134179830.post-54609611906823646992024-03-09T19:21:00.001+02:002024-03-09T19:21:18.175+02:00You're stupid! <p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTLxMX73nifH3_mGoWV2EblXMXxGwRiI0BjisJg_VScE2pQLixbzf-4aAHP-IywO-dtV4Opu8RJwmEyaG8WpyjFQAjK_QWocISens9As79WhHzKZwhd5eDL3xk3Cz-2TtkyFt55-H-zf7M8Shp7vF5W3kUj4heqtXlKqQHQVJ_5P08OrfCHdVoTfvYTyI/s1024/stupid.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTLxMX73nifH3_mGoWV2EblXMXxGwRiI0BjisJg_VScE2pQLixbzf-4aAHP-IywO-dtV4Opu8RJwmEyaG8WpyjFQAjK_QWocISens9As79WhHzKZwhd5eDL3xk3Cz-2TtkyFt55-H-zf7M8Shp7vF5W3kUj4heqtXlKqQHQVJ_5P08OrfCHdVoTfvYTyI/s320/stupid.jpg" width="320" /></a></div><br /> <a href="https://always-fearful.blogspot.com/2024/03/youre-stupid-heb.html" target="_blank">עברית</a><p></p><p>you are stupid, and the sooner you'll accept it, the better. <br />But don't worry, your colleague is stupid as well, and so am I. And while it's true that some people seem to be stupider than most others, I want to focus on the regular level of stupidity.</p><p>Recently, I've been mentoring some teammates that suffer from the same problem - it takes them forever to complete tasks, especially those that involves coding. They can do it, but getting started takes forever, and code review frequently requires a large portion of rework. When we dove into it, we found that the reason was similar as well - they were over analyzing (and therefore not sure how to start), and were getting lost several times during coding. Its just that there are so many details to take care of. </p><p>To deal with it, we're starting working on what is probably the most fundamental programming skill - top-down design (which should not be conflated with TDD, even though one might argue that test-driven design is a specific form of top-down design). I asked them to practice consciously and force themselves to implement their code in a pure top-down approach, and commit every time a level was "done". So they did, and when we got to review it, I saw the problem - they were making shortcuts, adding something they "knew would be needed" and making a mental mess in their head. </p><p>The power of top-down approach is that you first decide on a way something will be used, and only afterwards understand what needs to be done in order to actually make it happen. It's not a panacea, but for this kind of problems, its just what the doctor ordered. There are a lot of tiny details that needs to be taken care of, and working within an already existing framework complicates manners even more as you need to adopt the mindset suitable to it as well. </p><p>When we stop to think about it, this approach benefits are not limited to coding. It can help with all sort of analysis and planning problems as well. Just like with recursion - there's some sort of magic in that we solve our problems by pretending they are smaller. It almost doesn't matter how complicated is the task we are trying to perform, it can probably be broken down to no more than five high level steps, and those steps can be broken down as well, until we get to a problem that is already solved.</p><p>as I was writing it, I started wondering whether this sort of thinking is required as a precondition to TDD, as the mindset seems pretty much similar. Perhaps it's the other way and TDD can be a way of adopting top-down thinking, but whatever the case, I believe it's an important tool to have in your toolbox. After all, we are too stupid to keep the entire problem we're working on in our head. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-63087909441949399512024-03-09T19:21:00.000+02:002024-03-09T19:21:10.840+02:00את טיפשה! וגם אתה!<p dir="rtl" style="text-align: right;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlsIrLEHnHGXCOziV2UlhinBWfZOmP9YJRsxHqjljl-Lnuw6BwSGQ9WjHlbEdVkhLwU9XwvtCQKyeMxdIGoGFGqcTgDKonwYd95Ys5HW3KuczU6_lRzJqpUuI3ByUHeCAi7GH_5TPafFXcr2cyf8DyvQT41AAZjTOQV906l9j2X2YM2g5THWl-MrhlvOY/s1024/stupid.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlsIrLEHnHGXCOziV2UlhinBWfZOmP9YJRsxHqjljl-Lnuw6BwSGQ9WjHlbEdVkhLwU9XwvtCQKyeMxdIGoGFGqcTgDKonwYd95Ys5HW3KuczU6_lRzJqpUuI3ByUHeCAi7GH_5TPafFXcr2cyf8DyvQT41AAZjTOQV906l9j2X2YM2g5THWl-MrhlvOY/s320/stupid.jpg" width="320" /></a></div><br /> <a href="https://always-fearful.blogspot.com/2024/03/youre-stupid.html" target="_blank">English</a><p></p><p dir="rtl" style="text-align: right;">אתם טיפשים, וכמה שתקבלו את העובדה הזו מהר יותר, ככה עדיף לכולם.<br />אבל זה לא נורא. גם הקולגות שלכם טיפשים, וגם אני. וכן, יש אנשים שהם טיפשים יותר מהאחרים, אבל בפוסט הזה אני רוצה לדבר על סתם טיפשים רגילים. </p><p dir="rtl" style="text-align: right;">לאחרונה יצא לי לחנוך כמה קולגות שסובלים מאותה בעיה - לוקח להם נצח לסיים משימות, במיוחד כאלה שמערבות קוד. וזה לא שהם לא יודעים לכתוב קוד, זה פשוט שלוקח להם נצח להתחיל, ואיכשהו, עד שהם מסיימים את המשימה אז בזמן סקירת הקוד יש מיליון הערות ששולחות אותם לעשות חצי מהעבודה מחדש. כשצללנו לעומק, גילינו שהסיבה דומה - הם חשבו יותר מדי ונתקעו בניתוח אינסופי של הבעיה עד שהם כבר לא ידעו היכן להתחיל. וגם אם התחילו, תוך כדי קידוד הם היו נתקעים בניסיון להבין איך נכון לכתוב את הכל כך שיתחבר לכל המארג השלם. המון פרטים שצריך לדאוג להם כל הזמן, פלא שהם הולכים לאיבוד?<br />כדי להתמודד עם הבעיה, אנחנו מתרגלים את אחד מכישורי התכנות הכי בסיסיים - Top down design (ולא, זה לא מה שמתייחסים אליו כשכותבים TDD, למרות שהעקרונות דומים באופן מפתיע). ביקשתי מהם לבצע את משימת הקידוד הבאה שלהם באופן מודע - לנצל את גיט ובכל פעם בה הם מסיימים לכתוב "רמה" של הקוד, להכניס את השינוי. ככה יכולנו לעבור על רשימת הcommits ולראות איך הם ניגשו לבעיה. זה הפך את תמונת המצב לבהירה יותר - למרות הסיוע שהם קיבלו ממשימה מוגדרת שמכריחה אותם לחשוב "מלמעלה", הם כל הזמן עשו קיצורי דרך, כי "הם יצטרכו את זה בהמשך". כמובן, כשהם הגיעו להשתמש במה שהם כתבו, צורת השימוש הייתה עקומה להחריד וכפתה עליהם לכתוב קוד לא נוח כדי להתמודד עם מה שהם כתבו קודם. אבל, הנזק המשמעותי יותר היה שהם כל הזמן עבדו עם מודל מנטלי של כל המשימה ועשו לעצמם סלט בראש.<br />היתרון המשמעותי של כתיבת קוד מלמעלה הוא שקודם כל מחליטים מה צריך לעשות ואיך משתמשים בזה, ורק אחר כך צריך להבין איך נכון לגרום לזה לפעול. זה לא קסם, וזה לא יפתור את כל בעיות העולם, אבל זה בדיוק הפיתרון הנכון לסוג הזה של בעיות - על ידי מיקוד בצעד הנוכחי בלבד אנחנו יכולים להתרכז במשימה שלנו, ולא לתת לאלפי הפרטים הקטנים שבאים יחד עם תשתית הבדיקות שלנו להפריע. ככה אנחנו גם יודעים שהפיתרון שלנו שלם ולא שכחנו משהו בתוך כל הבלאגן. בכונוס אנחנו גם מקבלים קוד שנראה יפה יותר, עם חלוקה טובה יותר של תחומי אחריות, אבל זה רק בונוס. </p><p dir="rtl" style="text-align: right;">כשעוצרים לחשוב על זה, צורת המחשבה הזו רלוונטית לא רק לבעיות קוד, היא יכולה לעזור לנו עם מגוון רחב של בעיות תכנון ואנליזה. כמו ברקורסיה, יש כאן קסם - כל מה שאנחנו עושים הוא לחשוב על "איך להפוך את הבעיה שלי לבעיות קטנות יותר?" ואז חוזרים על התהליך שוב ושוב ושוב עד שאיכשהו, הבעיה פתורה. </p><p dir="rtl" style="text-align: right;">תוך כדי כתיבה, התחלתי לתהות אם סוג החשיבה הזה הוא תנאי מקדים לעבודה בTDD, כי, כמו שהזכרתי כבר, העקרונות דומים למדי. ואז, רק כדי לבלבל, עלתה האפשרות שאולי אחת הסיבות בגללן TDD היא אפקטיבית כל כך היא שהיא כופה תכנון מלמעלה ובעצם מאפשרת לנו להתאמן גם על הכישור הזה. בכל מקרה, בין אם כך ובין אם להיפך - זה בהחלט כלי שכדאי שיהיה בארגז הכלים שלנו. אחרי הכל, אנחנו בהחלט טיפשים מכדי להחזיק בראש את הבעיה שאנחנו מנסים לפתור במלואה. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-28435738751184418932024-01-01T09:17:00.000+02:002024-01-01T09:17:06.087+02:00The tracability fraud<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMApVaX_AmviMj_74hPyjfo_OYcMK5uwhia8x19V9oftZ4uRL9fT1jVaD1l1FyAxo9Jh0_EjuyEMErCbMelkwATjIJaCW7R3kHvtbnRS9z_RG3IEnYx_PF-cUiF3erb2AWe7p3JvJrK8WarGPm6Td_d_Pdt3fVirZ71s9FMMT8VMM6mxL29X-FnxpebNQ/s1024/vault.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMApVaX_AmviMj_74hPyjfo_OYcMK5uwhia8x19V9oftZ4uRL9fT1jVaD1l1FyAxo9Jh0_EjuyEMErCbMelkwATjIJaCW7R3kHvtbnRS9z_RG3IEnYx_PF-cUiF3erb2AWe7p3JvJrK8WarGPm6Td_d_Pdt3fVirZ71s9FMMT8VMM6mxL29X-FnxpebNQ/w320-h320/vault.jpg" width="320" /></a></div><div style="text-align: right;"><a href="https://always-fearful.blogspot.com/2024/01/the-tracability-fraud-heb.html.html" target="_blank">עברית</a><br /></div><div> <p></p><p>When you talk about testing, it's just a matter of time until someone will mention the "gold standard", the one thing testers should be striving towards - full traceability. Perhaps by coincidence, the likelihood of someone mentioning it is negatively correlated with the amount of testing experience they have. The story is a compelling one - by some testing magic, we can map each test to the original requirement it came from, and when a new feature is introduced, all we have to do is to go to that requirements tree and find all the tests that are affected or that have relevance to that feature.
Nifty, right? What is rarely mentioned by said people is the cost of creating and maintaining such a model, compared to the expected benefit, which, in the current, speedy development cycles, is even lower than before.</p><p>The first difficulty rises when we think about structuring our requirements - It probably have some sort of hierarchy in order for us to be able to find anything, and choosing the "right" kind of hierarchy is quite difficult. For instance, let's imagine a simple blogging platform, such as the one hosting this very post. It seems reasonable to assume that we would split the functionality
between the blog management and visitors, as they provide a very
different experience. Perhaps something like this:<br /></p><p style="text-align: center;"><img alt="" height="205" 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" width="320" /> </p><p> Now, where, in our model would we place the requirements for comments? They can be seen and written by visitors reading each post, so it makes sense that it would reside there, perhaps even under the "view post" section. However, they also can be read and approved by the blog admin, so why not put it there? Should we have two "comments" categories? one for each branch of the tree? perhaps we should have a third category of "cross responsibility features", but that would only confuse us. We can also restructure the requirement tree to have the various components as root elements, like this:</p><p></p><p></p><p></p><p></p><p></p><p></p><p style="text-align: left;"></p><p style="text-align: center;"><img alt="" height="147" 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sWr7yaGLvqc3HcxdAmLizdTgAIUoAAFKEABClCAAhSgQCiBkut6yVE7Bc7bMqe/DOUcvVVaK29Z/VWKkRw/Hur1Jv56Mfb9+AXW/Hs11m/cgIIte13Grboju1dfDMjphOx4bSvyqNasCeXULBMbx3ZoDF1i68nWKEABClCAAhSgAAUoQAEKpLdAURFKendzGVitqPLvz50/x+JS9/0GHDkSi6bYRqQCdetCOaVBpE+n3XMMXdJuyjlgClCAAhSgAAUoQAEKUIAC8RNQD/wPpX+91vkCpfFpyJz3dsxeph46BBySE4R4JU1AadAQqFMnae9PtRczdEm1GWN/KUABClCAAhSgAAUoQAEKGFhA3bEdpXcMdIUuZ5yJzJdmx6y3qra1SNtixCtpAkrT02U/U2bS3p9qL2bokmozxv5SgAIUoAAFKEABClCAAhQwsIBj8ybYRtzn7KF2TLR2XHQsr7QvphtLzHDbqnGCrF5qHO5TaX0/Q5e0nn4OngIUoAAFKEABClCAAhSgQGwFHBvWwfbISFfoclF7ZEyaFtsXHDkMdd++2LbJ1nQJOAMXCV546Rdg6KLfindSgAIUoAAFKEABClCAAhSgQAiBuIcu2vuloK7KgrqJ/SxKHRdnPRdeYQkwdAmLizdTgAIUoAAFKEABClCAAhSgQGUCCQldHA44txmVlHAyEiCg1KgBSFFkXuELMHQJ34xPUIACFKAABShAAQpQgAIUoEAQgYSELtq7S0uh5u1l8BLnT6IzcDk1S/aKWeL8JnM2z9DFnPPKUVGAAhSgAAUoQAEKUIACFEiKQMJCF2102oqX338HpM4Lr9gLKHXrAqc0iH3DadQiQ5c0mmwOlQIUoAAFKEABClAgtgJqaQkUNbZtsjUKxFzAogAZiTviN6GhixtLPXoUyP8TKCyMOV9aNniCFMuteyIU7WdeUQkwdImKjw9TgAIUoAAFKEABCqSTgOPrL6GuXwvHD99B/fmndBo6x5riAsoJNaGclg3l7HNhufhSKK3bxG1EyQhdygYjW46cwUtxMVSb/LeDqaiuiXYHc0q1anI6UfWEhnS6+pfCNzF0SeHJY9cpQAEKUIACFKAABRIj4Fi9EvYFb0D9L4OWxIjzLfEWUFqeDevNA2Fpf0nMX5XU0CXmo2GDFIhOgKFLdH58mgIUoAAFKEABClDA5AK26VPh+OgDk4+Sw0tXAev1N8F617CYDp+hS0w52ViKCzB0SfEJZPcpQAEKUIACFKAABeInYHtsDBxrV8fvBWyZAgYQsHTtgYxHH49ZTxi6xIySDZlAgKGLCSaRQ6AABShAAQpQgAIUiL2A7ZmJcCz9d+wbZosUMKCA5ao+yLh/VEx6xtAlJoxsxCQCDF1MMpEcBgUoQAEKUIACFKBA7AQc/3ofthnTYtcgW6JACghkPDQWlp5XRt1Thi5RE7IBEwkwdDHRZHIoFKAABShAAQpQgAIxECguQslN1wJHDsegMTZBgdQRUE5pgMy33o+6wwxdoiZkAyYSYOhiosnkUChAAQpQgAIUoAAFohewv/c27K+8EH1DbIECKShgfWA0rDnXRNVzhi5R8fFhkwkwdDHZhHI4FKAABShAAQpQgALRCZTeNxTq1u+ja4RPUyBFBSwXXISMZ56PqvcMXaLi48MmE2DoYrIJ5XAoQAEKUIACFKAABaIQ0LYW9eoWRQN8lAIpLpCZiSqfrIxqEIYIXUpLocoPBWpUY0n4w4oFyMgAZB54mUOAoYs55pGjoAAFKEABClCAAhSIgYC6/b8oHTooBi2xCQqkrkDmP9+FcmpWxANIWuhy5AhUrRZTYSHgcETcf0M8aLVCqXECULs2cIL8zCtlBRi6pOzUseMUoAAFKEABClCAArEWcHyzEbaRw2LdLNujQEoJZL70OpQzWkbc54SHLoXHoB44CMhKNVNe1atDqX8yID/zSj0Bhi6pN2fsMQUoQAEKUIACFKBAnAQcm76CbdT9cWqdzVIgNQQyZr4KS8uzI+5sIkMX9dAh4JAELulwnXIKlLonpsNITTVGhi6mmk4OhgIUoAAFKEABClAgGgGGLtHo8VmzCKRK6KIePAD88YdZ2HWNQzn5FOBEBi+6sAxyE0MXg0wEu0EBClCAAhSgAAUokHwBhi7JnwP2IPkCKRG65OdD/d/vycdKQg+URqcCtWol4c18ZSQCDF0iUeMzFKAABShAAQpQgAKmFGDoYspp5aDCFDB86KKdTLRrZ5ijMtHtWpHdpqcDFjnpiJfhBRi6GH6K2EEKUIACFKAABShAgUQJMHRJlDTfY2QBo4cu6v59wGE5pSidrxPrQTlZiuvyMrwAQxfDTxE7SAEKUIACFKAABSiQKAGGLomS5nuMLGDk0EW12YCdO4zMl5i+ySoXpfn/JeZdfEtUAgxdouLjwxSgAAUoQAEKUIACZhJg6GKm2eRYIhUwdOhSUAD8vj/SoZnqOSWrMXDCCaYakxkHw9DFjLPKMVGAAhSgAAUoQAEKRCTA0CUiNj5kMgFDhy5a4KIFL7ygnHQScFJ9ShhcgKGLwSeI3aMABShAAQpQgAIUSJwAQ5fEWfNNxhUwcuiCvL1Qjx0zLl4ie1arNpRGjRL5Rr4rAgGGLhGg8REKUIACFKAABShAAXMKGCN0aQTLTYNg6dEBliz5l+wqXtYF+6B+txT2Ba/C8ZM558Bco2ohc3ku8E4uHCk0MCOHLuqeX4CiohTSjGNXZWuRc4sRL0MLMHQx9PSwcxSgAAUoQAEKUIACiRRIduiidH0QGcP7QqkZYtT2o1AXT4Nt+lKoiQTiu3QK5CDjpTtgaS6h2fGNsF8zDHadTxrhNoYuRpgFHX1g6KIDKfm3MHRJ/hywBxSgAAUoQAEKUIACBhFIZuii3DQDGUPaQrF6YWgrW/4odv2GtZb8q7Z8E1/29RKonzyC0inrDKLHbpQLjEHm8hwo2m8cZeiSMWlazD4cXOniRcnQJWafq3g2xNAlnrpsmwIUoAAFKEABClAgpQSSFrp0kG/SH5Nv0j1bif74HvbZ02D/5Gdfv6yesD54B6ytPXUcjsLx8uWwLUwp5jToLEMX2yMjnfNsuag9zBi67FsyHOMeXoZGk1fhyV5JKmbL0CUl/i5h6JIS08ROUoACFKAABShAAQokQiA5oUsHZMyfCktD9wjzVsE+5hHY84KP2DJqPjKuaOK6Yf8q2AY8klI1QxIxl8l9B0MXs4cuBVty8e7GIzi57XXo06p22cet4KtZmPDEVlw2ezpyGsT5U8jQJc7AsWmeoUtsHNkKBShAAQpQgAIUoIAJBJISutwyC1UGnePSs++GY8wA2DaGwmwE67S5sDb/DfbJg2EPsMNIad0P1sF9YWkhq2I8K2jsJUDed3B8Mg/2dzYGrAeTMWctLNna+92raPLuRcYdV8nvuQvNHD0Edd082Cbnlj2vXDseGTddDOXk4PeUjUgCoyqewGjzTJSM2A3rpHthbSshkrZ1SqtXs/0/cEx/AnZPseAz+kqtm4GuGinB7glAplw7Btaru0hBYumXZ1tWibT/s7T/klf73s9W6N9GWB8eA0uHFuW1dgIYOJu4fgaq3NW2ksmT+e0m81t2hxRNvmsErD1kW1kd9yRpc6S1/5P0ce5z5QahPhIx/DprukSO6VoBA/T5jKFL5IrmepKhi7nmk6OhAAUoQAEKUIACFIhCIBmhi/WFz2E9y/UNt/rlJJSOWaRzBHK6UYd1cFQIXCSQeewFWDuHOEp2ey5sE56Dw29FjU/o8s5KKNd6bXvy7tmuRbDdNgmq1KLJvCNI0OC+x+fkHp9Q4y046kkwlO19RJP7JSUSUEyQgGJPP2S8cC8sdQKweO6pYNBCQqmpsg1LQppglxbufPQ4Smf6PewXutgxSNoJUtnYf3xhhS7u4CxY285+H4LjuathW6zzIxGj2xi6RA7J0CVyO7M+ydDFrDPLcVGAAhSgAAUoQAEKhC2Q+NBFAoV/SaDg/J5evsGeLN9gfxZ2t30e8Nl6pH3luKya2H/EdU/NU2U1ilfAsUeCk8GTfLYmlYcu2ooLuVfr24HdUI/Kz9XrQ2noCSCkkO8HHwO95bQlrcn9cs9x/3fIPQv7o/TlfeV99A41jkqjNaW9sj5WhZItYZFnVYoEQ3ZcJSt65AVHpajwASkq7F9QePtbKB0602fVjmXiR8jo4BW4eBckrtdEVpV4uiOreV4dBNs7Qfq3X36/ofTH27ChPF+9fDjqJ8OkmLF7aVIP2VZ0k7ZqSYoeN3W/Xwt39hx0P/ALHLfJ1jHtV94rnLRfe/y0/64n8+Rc+eK/Mia6z4bep1M+dMlfhgmdhmPPPQvw+tDzvIa9DfNuvB5fdPgHXn6gHTL9QDbN6oyXXuyMG1ZPwOV1v8VLrfpjk9bGbVnY8MYMzM/NRZ27XTVcPDVd2rz5A+5uJQ1tmYXbbp5Rgdi/5kvpoQ147+UFWL9+GQr3yO3N2yH78v64oV93tCzfpaRvqri9SJ9Tku9i6JLkCeDrKUABClCAAhSgAAXiJ6AWFso3zJIEHNd+LoTvr+X35euOY/KNf+Ex4NgxqL/thfrt5vh1qELLXrU/jkvx3Jyh0R0t3HY8Mp/t6To1BxJ4rJkJ2/jybUDa7yq3TEVG/w5lRXt9QgP5enno4m7D74SkCqGObENS35EVI696Voz4reDIWwrb354oD3a8QxftFf7Bj39RYe2erW/Bdt/Msja0k57KV9fsg2P09eVbsnwM/PumNSZbeh4Wgx5NXLPhf7pQqP5BavDMebp8dU7Amjqha7pYn/1UtlS5Aix1zUiUjvddcePcHnb1SbLap3zcifpgZvz9JVjOPCvi1zm+2gDbuIecz1subIeMJ5+JuC3/B9VfJakoKpIPakYlbRbji+fbYN5HQ3G7hGLtqpbfuu2f12PalHYY+NUodPT6fcAdyJw5HdMmdEcduEOXoc+iT96zWJzfE+demIVT3TVcKoQue1bjteU7cfy/i7BFFqtl35aDRrXgU/OlUIKZccNnoADN0Ojq65BdV/qVvxWbPlqMUvRGn/nPIufUMKgYuoSBlbxbGbokz55vpgAFKEABClCAAhSIhYDNJisJfoH6yy75IastdsvP7l/Hovm4ttFjKjIflgAk0Df/EbzYOk2+kfdsV9k6D6X3vRqwboty1xxkXt/C9YY/1sF2w8iyQMMndJGVJqVDn/Nrw3t1jjwf8D33InNpP/fx136rNbxDDfvPsA+WmjR+W5x8xqGFUUMljPK/Z9ZaWQHjGoK6+BKUPuf6b+9VLsG3a0kwNGuh+3kJp/55GUrnusF19A9tn5Zwq7M73Aq0GkVH6OI1V+pn16N0stdqmwjmPpaPqIoC+UYxlk3GtC3LOa1gvef+Stss/WoG7hoyC81mrMOYLl5LSH6ci2E3TkHzWZswrINX6uL+/TplpxG5QxfUR/bT8zEmp7HPypgKoYu7N0G3FxVuwIzet2JLy1EYNnkQvGrvSvAiX7tXvlZ7Asa81FciGZ0XQxedUMm9jaFLcv35dgpQgAIUoAAFKEABvQLFxa5QRQtXPCHLbvl13q96WzDefTENXaTY7PsPumuf+K3+qDDyHGS8J8Vh62lf8N3W5B26eIcZ3k341H15VY6sfsf/BW3lRKYZ7hOZKgldpCZKidSFqXBVKGb7VoVbLI8tRIa7bo36iYQuU7RbGiHjjYVSOFf7b9k6NFP69kGQafd+x8bnUDI613Wj9+8H6x+8g6fIQhdI8FXFE3yVyBwsfhX2mYsChmSJ/uCaIXSpuHLFoxh4i9GO3Fsx6YlaXgVw3aHLmRKSvCshid8khBu6FKx8CiOGfYOO7y7EwDMrzmjB8scw4oFVaPfWKtx+ts4ZZ+iiEyq5tzF0Sa4/304BClCAAhSgAAUoEERAlXof6gY5wWXNajh+/lFqXhhnJUDsJi2W24tCr67w7rf3ahJ1oYQWL7u+WuH0ooUVR1t+T/CaI0Hv0RGoeAcf5YGKXz+82im/x28VjnUCiRkAACAASURBVN6JktU6JbIqqELo4jxdqWLg4xvuRBi6QIKv+SMkmPKqsaOdXHTgZymOvBKOD6TIcCXHhusdWiT3xTx0yfSvnqKzV6WlAW/Us9JFe9C1lSjL7yQhd+jy4yCvLUZ78d7Qy/GpdQIek5UmzsO7PNuLbnwZ08Z2ku1Gvle4ocumWWdLvZiWZduOKgwsbwM2vLsa/jVgKpVi6KLzg5Tc2xi6JNefb6cABShAAQpQgAIU8BJQ9/4KdeNXcKyVoEV+TshVpQqUGjWAajWgKrKlYl8iw51YFtKNInQpWymSpqGLFOMtkWK8+kMXb6dIQxd5W1ZPWB+8Q7aEBThpSivAu3gabNOXJnz1S8oX0vX8xbEnFw/nPAZM+BST+zR2/q5rxckC+a/6aDV7KYZdKFuM3PeVjv8U0/q67isLXSoU43V9NbLQJfTfaAxdQhul2h0MXVJtxthfClCAAhSgAAUoYDIBdev3cGz6Guq6tXD8uDW60VmtsPzfGVCat4DSoKGctiPHzJT9kGDF/d+W6tp/l//a89LEn14kh/FEfGR0IyhZcqJP2WqIKEIXs650yZPtZ7J4JOSl1aV5bqnrNj0rceS2ylf7hDcXOKMDrD3k6OwO58rpUlJc13N6k1YMebEU2X3OfTpSyIHE5gbThC44iEUjO+PDI1IrZZZWK0VWtNwtK1rOfBQdf3gKX5wtpxgNa4eDS4Zj3MNHcPmif+AG1zIXubxOL/I5Acn11chCl+5+q26inC+udIkSMDGPM3RJjDPfQgEKUIACFKAABSjgEZCTR1QJWRzfyA855cR5Gkkkl6xOKQtYmv8flP+ToKWp7hKUAd+YjNDF5+jgElk1MXZA+Uk8lbhYHpaaJq13wz5ipLvIbKxrukhNlJelJkpKbS+SMOT1tbA01eC046AD1ZsJ8WFLRuji3SVt9cvDI2A9y300t1+h40j+qIT7jHlCF1nZ4qyVstVVSwVaEd1FaCv/fekPUsNlZms53ag/DjwqwcyeURghtVtalmHFNnRxrbBZilazZHWNdwHfcCfH+36GLtHoJexZhi4Jo+aLKEABClCAAhSgQHoLOIOWFZ9JvYq1UPP/DA+j7omugEULVrSARVvJkuXZBhBeU5XdnZTQRTuCeP5Ud9FZ6d0eOWJ5rByxXEk9D+UWOTJ5UFvXUORI5lI5klk7aya2pxelZuhieVTCqK7u7TqVnOAU9HOQoNBFyWokq5SCbWULVag3dp/5QC2ZKXRBsZwMdOGt2P7AAtxwZBjm/TjMVbdFthSNyJmBv0wejfyHR+PgAwsxY3B55BLdShf/VTOiXCwhzi39sanKUAycPgwdT/KXL0bBYaBObZ9zrCufaIYu8f2DEKPWGbrECJLNUIACFKAABShAAQoEEJBCmI7lS11hSzg1WmqcAMslHZ0/lJZnQ6l/ckJ4kxO6yNA6yHaUx3KgeGqq/iHHJM+eBvsnP/uNuwWsTzwN66WeGiCy/WRhfymC6/7mva20M0nacW5Pka+tmQnb+FyfmiDKLVOR0V+OqXa/S/1kmJz8U759JbUL6cqws+S46tfluGrn+AIbuFBbwDKkL5S8SeLsxRyT0MUvNPmbrF7yCdHkdKc35HQn+zrY35FTi/znucN4ZD7R0zWPRzfCfs0w2BPyJ8D1ElOFLijGhhk98Zp2itUhlNdx0bYaacVzf67v/P02b67C3T5HFEW20sVzVDW6DEK78+XvrabdcXsXV0BcKMdST7hrCg4eaow6N/bGmVnuo6zzd+K75bkovGoBXg+wlSno1DN0SeCfishfxdAlcjs+SQEKUIACFKAABSgQTCBvL2zLPoUqYYtWHFfX1bARLBe7ghZL6za6Hon1TUkLXWQgyk0zkDGkrTswcY+sQGq2/FHs+oW1lqzukX8eL6v3ASk6LKHK6Ld8QhWLhAYZVzQppzl+COr+I65f1zxVaoZ4nZazZxFsgyfB4QWZ8qGLjKWCQYkUpc07WD7K6vXLaqeo655A6Vh3PRftjpiELt5HZkubf0htmQJtDuUo78GyHeyKqcgcJcGXp0c+/asKJVtCNc88f/8qSobPi/VHvdL2zBW6yFAl7Bh24xQU+h3/7DomegNw0jDc/flQ+P6tE1noAhzGpjkT8dobi1F6qL7M8yt4+RavFTSH9+KLRfOwdMlq7Nuy1zkPma26I7tXXwzI6YRsdw6ja8IZuuhiSvZNDF2SPQN8PwUoQAEKUIACFDCRQNkWIlndopaErmCqtDjDFbS0vQjKWWcnXSKZoYs2eKXrg8i4V1Zf+J9P6y+jnWyz4lXYJvuuYnHdJuHV8KeRcXWLyj1/yoXtqecqbGMyQ+hSZtBbDLxCqoAg3sdFazfEJHSR4OdBCb96N/F7pfuko8pOLfJ+okBWudwnq1wSfHS06UKXpP/NEqcOMHSJE2xsm2XoEltPtkYBClCAAhSgAAXSTyDMLUTK+W1hubCdK2iR+ixGupIdupSFJjcNgqVHB1i0lS1eC1NwVFatbF8nBWJlS8xPlcsprfvBOlhOxGkhqyY8bdglCMv7Do6PZIXMB/5bl1ztmSN0cducIcWF77hOPmeywqemF6RzZclPUD+bJ9t7NvoeyRyj0EULfqwPPy0nEkn9IXdNXJT8DPuVg8u3Ckn/rIN6wnKGdo/0zxMQafO8dSnsM2dWWtsnXn92GLrESzbG7TJ0iTFofJpj6BIfV7ZKAQpQgAIUoAAFzC8gK1nsb7/prNkSaguRc0VLjyudYYtyWtmZrIYzMkboYjgWdijNBBi6pMiEM3RJiYli6JIS08ROUoACFKAABShAAWMJOFatcAYu6s+VL7fQwhZr72tgybnGWAMI0huGLikxTexknAUMHbrslToohcfiLJAizdeqDaWRp6h2ivQ5DbvJ0CUNJ51DpgAFKEABClCAApEKqL9ITQoJW+xL/11pE6kWtngGw9Al0k8GnzOTgJFDF/zvdzlyPt9M3JGP5aSToJwkpy/xMrQAQxdDTw87RwEKUIACFKAABYwjoK1s0X7giPsknABdS9WwhaGLcT5n7EnyBQwduhwukNO49icfyQg9yMqCcoKnYJAROsQ+BBJg6MLPBQUoQAEKUIACFKBApQLq+rWu2i3fbQl6X6qHLQxd+IeAAuUChg5d7HaoO7ZzuqxWKM2a0yEFBBi6pMAksYsUoAAFKEABClAgGQLqvt9cYcuif5k+bGHokoxPGN9pVAFDhy4aGrcYybYiOdmMW4uM+kfIp18MXVJimthJClCAAhSgAAUokFgBxwfvwf6WFMo9dDDgi7Wjnq1XXZsyBXL16rGmi14p3mdmAcOHLrZSqLt3Aw6Hmach+NgyM6E0aQIolvQcf4qNmqFLik0Yu0sBClCAAhSgAAXiKeDY+gMcc16DFj4Eu6zX3QDL34ZAqVUrnl1JStsMXZLCzpcaTMDwoYvmdfiw1HbZZzC5xHRHOTULqMlaLonRjv4tDF2iN2QLFKAABShAAQpQwBQCDjmRyPbi34GjRwOOR2l6OqwStlg6dTHFeAMNgqGLaaeWAwtDICVCFxmPeugQEGQ1XhjDTa1bTzkFSt0TU6vPad5bhi5p/gHg8ClAAQpQgAIUoIAmYJ/9CuwL/hkUw3rNda7VLXXrmhqMoYupp5eD0ymQKqGLNhz1zz+AAwd0jizFb2vQEEqdOik+iPTrPkOX9JtzjpgCFKAABShAAQr4CNgefwSOL1YFVFFOy4Z10G2wdOmWFmrq5k0oHXFfWoyVg6RAMIHMl2dDaXFmxECODetge2Sk83nLRe2RMWlaxG3pevD4cagHJXiRn814acdCq1I4V6lWzYzDM/2YUjJ0WfXnLqwr+BU/Hz+IAyXH4IBq+oniANNTIAMWNKxaC+ec0ACX1Wvq/JkXBShAAQpQIFYC6m95sI17SApS7grYpDXnGtfqFu2UjDS51J07UHr739JktBwmBQILVJm/EGjYKGKehIcu7p6qx2Rr5BH5UXgMsNki7r8RHlSkWK5ao4bUzqoNyM+8UlcgZUIXu+rA5F9WY9ber/BrcUHqirPnFIhCoGPdJrg/+2Jcd/JZUbTCRylAAQpQgAJy6MeaVbA9MTbg6R9akUbn6pZuPdOOSi0pQemVl6XduDlgCpQJVK2GKkuWRwWSrNDFp9Na6GK3QXWk2D/QKwq0wAVWa1RzwIeNI5ASocuSQz9j2E+LsOO47NfjRQEKoH/D8zDrzGtQ01qFGhSgAAUoQIGwBWzzZsPxxj8CPme5orczcFFOPiXsds3yQOn9d0P97luzDIfjoEBYApZ2HZDx9NSwnvG/2RChS1Qj4MMUiJ2A4UOXV/K+xF0/fhS7EbMlCphE4JyaDbDovFvwl2rmLmhokuniMChAAQoYRsA2Xuq3yCqXCldGJjJGPARLzysN09dkdcT+wULYZz6frNfzvRRIqkDGqEegha/RXAxdotHjs2YTMHTo8sb+bzDwh1yzmXM8FIiZgBa8rG17B2pnVI1Zm2yIAhSgAAXMK1B6y43Q6rhUuKRmQMbD42Bpf7F5Bx/OyOx2lPa71nUcLS8KpJGAktUYmW+8E/WIGbpETcgGTCRg2NBly9H9OH/DTCmSy4sCFKhM4MYG5+Kdc24iEgUoQAEKUKBSgZIeHQPWb8EpDVyBy3nnU9BLwP7vj2GfOpkmFEgrgYyxT8ByWfeox8zQJWpCNmAiAcOGLr03vwGtlgsvClAgtMD8s29w1nnhRQEKUIACFAgkUHLN5cBROdHD71KaNEXG+IlQspsQLoCA/fkpsC/6kDYUSAsB6/U3wXrXsJiMlaFLTBjZiEkEDBm6rPhzJ7ptClzczSTuHAYFYirQttap+Pqiu2PaJhujAAUoQAFzCDi3yfzvfxUGY2l5NjKmvQBU5RbVymba9tTjcKz4zBwfBo6CAkEELFfmIGPkmJj5MHSJGSUbMoGAIUOX27Z9gNm/bTQBL4dAgcQJrLtwKNrXPi1xL+SbKEABClDA8AKlgwdA3bO7YuDS5gJkTJlu+P4bpYO2V2TL+3tvGaU77AcFYipgHTAQ1lvviGmbDF1iysnGUlzAkKHLX9ZOxZ6i/BSnZfcpkFiBic2649EmXRL7Ur6NAhSgAAUMK1B6z+1Qf9xaMXC5pCMyJrBWSbgTp361AbYFb0DdsjncR3k/BQwpYGlzISwD/gZL6zYx7x9Dl5iTssEUFjBc6HKwtBAnr346hUnZdQokR4AFdZPjzrdSgAIUMKKAbdRwODZ9XaFr1t5Xw/rgQ0bscsr0Sd36PRzr10L94Xuoe38FCgqglpYAGRkpMwZ2NM0EbDYgMxNK3ROhnJYN5exzoXS4BJYzWsYNIqmhS3ER1MJCoFj+XNpKAVWN2zh1NawogFX+ftC2ctaoDqVadV2P8SbzCBgudPn+2O84d73sL+ZFAQqEJdCxbhOsbntbWM/wZgpQgAIUMJ+A7bExcKxdXTFwke0D2jYCXhSgAAXiLZCU0OXIYah//gkUFcV7eNG1X60alBPrAbVqRdcOn04ZAcOFLpuO/Ia2X76UMoDsKAWMItCuzmlYf8FQo3SH/aAABShAgSQI2CaMg2PVigpvznhkPCzdeiahR3wlBSiQjgIJDV20lTz/+x1qgBPajGyv1KoNNGwIaCtheJlagKGLqaeXg0snAYYu6TTbHCsFKECBigLBApdMKZirSOFcXhSgAAUSJZCw0KW4GGpenmsbUSpesuVIOTXLuf2Ll3kFGLqYd245sjQTYOiSZhPO4VKAAhTwEggWuFjvHwXrVX1oRQEKUCChAgkJXWSFi7pnT+oGLp4Z0YIXqbUDiyWhc8SXJU6AoUvirPkmCsRVgKFLXHnZOAUoQAHDCtgnTYB92acV+me9sT+sQ+8xbL/ZMQpQwLwCiQhd1Ly9wLFjpkBUatdxbTXiZUoBhi6mnFYOKh0FGLqk46xzzBSgQLoL2N+cC/uc1yowWC7pJMdCT0p3Ho6fAhRIkkC8QxdVTg3D7/uTNLr4vNa5zahmzfg0zlaTKsDQJan8fDkFYifA0CV2lmyJAhSgQCoIOFZ/DtsTYyt0VWlyOjJn/zMVhsA+UoACJhVwbN4E24j7nKOztG6DjGkxPp32l91QpZ6LmS6lRg2g8WlmGhLH4hZg6MKPAgVMIsDQxSQTyWFQgAIU0CGg7toJ25gRUA/8z/duqQ2QuSAXSt0TdbTCWyhAAQrER0Dd/jNKhw52Nq40b4HMWXNi96Ljx6H+KrVcTHgpTZoCVaqYcGTpPSSGLuk9/xy9iQQYuphoMjkUClCAApUJ2O2wPTwCjk1fVbgr86XXoZzRkn4UoAAFkiqg7t+H0gHXu0KXho2QOX9hzPqjHjoIHDoUs/aM1JBySgOgbl0jdYl9iYEAQ5cYILIJChhBgKGLEWaBfaAABSgQfwH7C8/D/mHFb2Ayxk+EpdNl8e8A30ABClAglMCxoyi5+nLXXRkZqLJ4ufPnmFz7foN65EhMmjJaI4oWuGjBCy9TCTB00aZz21rgvpXBJ7ZefeCiZkCnC+RnIySPNmDNBuCdzdL3fOm39Klzc+Dydgbpn6n+jKTMYBi6pMxUsaMUoAAFIhbQwhYtdPG/tFOKtNOKeFGAAhQwikDJtb2Aw1LwVq7M6S9DOadVTLrm3FokW4xMeUkhXWdBXV6mEmDooid08Z7y1hJsjOwuR3ol8XOwWQKikRIU+V89+gIPnZnEjvHVyRRg6JJMfb6bAhSgQPwFtO1E2rYiyPYi78vapy+s9z0Y/w7wDRSgAAXCELA98Sgcq+X7FrliGQyre34BiorC6EkK3XrCCVCyGqdQh9lVPQIMXTQl75UuF8lqltZ+q1ny9wHLfgD+cJPWOxt4sQ9wsh7iWN8jq1xefQZ4191uZwmBzqglv5AldidK33sYYSVOrMfM9vQIMHTRo8R7KEABCqSmgFYw11k4Vwroel+WDpciY6L87wJeFKAABQwmYH//XdhfnO7slXJ6c2S+Ni8mPWToEhNGNpJAAYYu/qHLoEHAzQGWdNllG8/z84FPtO08ct04ALijSQKnyvOqo8CT8pfXKu3XErgskVU3LHCdhHkw3isZuhhvTtgjClCAArES0I6G1o6I9r6Ups2QOWmq/CPQKbF6DduhAAUoEDMBLSwu7Scr8VWHs82MKdNhaSP/SBzllbTQ5ffFGNdjNDB5FZ7sJeUn5Nq3ZDjGPbwMjbx+L6rhcaVLVHxGfZihizYz3itdgoUu2n3H84D75wI7tF/IXxgfSnGomomeWq/Q5VJZbfO4rLrhRQERYOjCjwEFKEABcwrY57wG+5vyvz/8rowJk2G5pKM5B81RUYACphCwTRwPx+fLnGNxrnZ5+XVJXzKjGpuRQpeCLbl4d+MRnNz2OvRpVbtsXHsWjcakac1w++dD0Sac0TJ0CUcrZe5l6KJNld7QRbs3dzbw8n7XBL/wKOA5ldG7jUeGA10ljdn8NfDx97IqRbb/vCUpr892JNmH+KV87VPt6xLmaFeWrLBpIw1ecx7QpJrvhyhUsV/t7kCBUd52YOG3wKYfAedr3EV3L2sLXOpKaCtcbz4FzNV+V+rDaP2uJ0HPx1JDZrm0dUoXYJxf0POHNLxEivqulK/vlnu1JKq1FPbtLvf1aCKbOAO8I5hX7kZgnRwD5+nnVZdIWyGSLbu8c5WM8dP/Ahvdlk3kvV1aA72kD/UCD9P5u5H03dPcbjFdKP39crdr65k2f+fKHsz25wAdpOhPoHFX0pVov8TQJVpBPk8BClDAeAL2ZZ/CPmlChY5Zb/grrHfeZ7wOs0cUoAAFvATU7f9F6dBBZb9jvaGf/N11b1RGRgpdgg1k06yz8dKLw3D3FoYuUU22SR5m6KJNZDihy4pc4Gn5Zlu7goYuQ4HfFklw4Q4APOGFJ3Q5Kr//uBz1uFkLKAJdEjL07Q3cJcGF5wo7dJFQ5y3p6+zdwT+qwYoCe4cuc9oDz84VI3cznWV1jXfosmKxeEjgEuzKkuDmsWuAZn5HxPmELvfI0yulHambU+ESiyHXA/2CVPHeIcHWhE/dgVKgTshzT0qfOwSodRNp37XXrPgwSH/dfagngc9rMod1Evc3BUOXxFnzTRSgAAUSIaD+cQi2B+6BuvdXn9cpLc5A5rSZQI0aiegG30EBClAgKgHbtMlwLPm4rA3rkDth7X9LxG0ydImYjg8mSYChiwYfTuiyROq6PLfbNV3BQpeW8o3+NglWtJUPl8jKlbpVZcWFfBPuXLAhq2Tuk9UynhAjS4KVy5rI/3CSLxVKvZjPJUTwZDXeK1e0FStrD8lNcjzacll1om1xaiarOLo1Kv/onHZGebiQO1dW5HgaksDhKrm3UXXX85sl3PjSXZumZRcJVWQ1ifYlz1UWushqjZYSDG2THy2l/x1lZUxtWclxhTsAWSdhxzjpr/OSwXWVdzTXivrKtV2CmBXaihW5tMLDMyT48D7xydv8RqlN864cgV1WxNivj9qDLwyUPlQS3Gjv8S6CnC/vXit9cBJIf1+42ff5aPqeJ+0OlLDJeYntjbLVzJPp7JGJ/UReWk9c3xXXBF4MXRKIzVdRgAIUSICA/cW/w/7+exXelPHUFFjaX5yAHvAVFKAABWIjUDp0MNTtP5c1poXHGfePhnJG+Cevhhu6lB7agPdeXoD165ehUE6bRvN2yL68P27o1x0ty3cElfXNdf9cfLF8NUoPNUaNPv0wYPggtLMFr+nS5s0fcLeciO2p8eKv5vl6SE1uLwpJlIo3MHTRZk136OIdmPjVdPFfidLvJtnuI4GK/xaTT94BpkqAol39pBjvkCa+nxutYO9Uqez9mbYKJlDY4FXTxX/Viacl71CgpXzjP7FLxRUXn8lKjWfcK0v8tyWVhS5agxJYPC0rTS7y2+JTslsKDksApW2r0UKVaTnAaX6hyPcSDt2/0tUr/776e1XYGiWnNL35ZvlqIf/CxSUyFyM84ZX08XEJdS71W82iWb4s41wt+0YfkFUnntUu0fZ9lbT5pNvu6YfExm/cJTJH38mPtok9V/wv1etibJMuqGmtIj+q4gRrpvyo4vphKf/vGvL7vChAAQpQwNgCjq/Wu46H9rssN/VHxh3aClFeFKAABVJIID8fpY+MhPqT51+eXX23XNoZ2nZJpYWEL1X0nQ4STuhSuGUWxg2fgQI0Q6Orr0O29u1C/lZs+mgxStEbfeY/i5xTyx0D3V+6ZzU2rayPPs91xoaBgQvpekIVT42Xg19NwY41ndDsgXbQCjo06zYIXbN1zBdDFx1IqXcLQxdtzvSELlrdkLmyJegt9+oR/8DEu41mcqLQLFm9UeGSoGCoBAXOVSrB7pGvFcj2pb6yNUi7KpySpCN0Kas7I9/0zxoi7wr0wZTtR1OnyaoM7Wt+pyB5hy53yPM3BggPvFeKPC7/o/BSvxo0nle+K+N9VauB0wSYJyGTZ5eQHi8tHOklwY52+a8cWSMrTR53b2u6S/rYN1jAIcHLfvnb1fvL0fbde4vZ36Wuj5RwMcSlKFIdXtXVleoSwrjCGfmR4Qpm6mZUQ5PqJ+IvVevKz/Kj2onOXzeskvBq0brGwJsoQAEKmFmgVNtWtMV3+67S8izXtqKqsoKWFwUoQIFUEygugu2px+FY+0XAnitZp0E5Vb5ZqFMHSh353+8SQAS61IICKPVOguUiKYNQ2VW4ATN634otLUdh2ORB8KpzK8GLfO1e+VrtCRjzUl/Xt0vF3+KlW/pj04HeyJEwpo9XGFPwjYQ3Dy5A4aGDPicVeVa2+K9kYU2XVPtwxre/DF00X+8AwHuLisfef9tPlqxymSEnF3nX6/BuY4iEAP0ChAD7ZXXEzbJKQruC3eP8olewUmGbSqjQRUKGx18E1kgzoba4lIUH8k31S1L8t4V7wGWhi9/ve38W50qxXVmIAsgql3dllUmwYrXeLuPkHZ3d38Dr8fJ2QBdgmWe7jgRG0yUwcm4NlW1PuWHWTom27ztkK9RQVxV2tJTPwv2dJdgKEjrF98+vb+thhC7hdKuaJcMZvjSppoUxDGXCseO9FKAABSIRsL/9JuyvvVzh0YzJ02C5MMQ3GZG8kM9QgAIUSKCAY9GHsL/3doV6VeF0wXLuebDeLd9bVHIVrHwKI4Z9g47vLsTAALuYCpY/hhEPrEK7t1bhdvmWxnX/AmQ/vw6PdfPfd1SMTbMGSHHcbQxdwpko3usUYOiiKegpUuv5wAQrPhvoNB7/D1k47yl71m8VincQEXB7kazE6T43/I/3M7JNpq17m4z/6UU+py5pTXsFP+G86UFZDt3LvQVIj5fWdllA0sUrdJGVMzfKChpta1PYx2bHoO+Q0OfVtyVs8tTMkX446/dIANRNtpQ1S9LKkDiFLqGm2BPKnFGjPjrVbYKOdf+CC7XaP7woQAEKUCBsAXXndtjul1Uux3yL7WtFJ7Xik7woQAEKmEXAsX4tHF+sgvrDd1B/1Yqt6L/0hC6u1SYtkX1bDhq5y076vCFvAza8u7osRHHdL1uOPpctRycF6Ms3M3DbwFkMXfRPE+90CzB00SBChSH1ZCfeRbLorJOsarjIr26I56OkJ0QI9Z6AH0u/2jHxCl2elC1CHdyrNeIVunhvA9LjpXmU9aWLV+jiFSwFq2sT9I94hKFLhS1MEryskOVEr8iqFy388b6aSFQ++gpZOZTg1S9JCl0CUVeXujEd6/wFnU5sgva1T8NFdRqjltSZ4UUBClCAApUL2CaOh+Nz92pK962Wc1rBOnUGlEzW5OLnhwIUMKnAkSNw7PgvcLhA/n33KFT5NYrkcI0Al97tRa4QJbRXo8mr8GSv+rKSJcQxz7+HLqTreVvItoJ1izVdQk9YCt7B0EWbNL0BQGUTrKeNUNuYArYvMeuNXkdHhxW6yHN3NNH3sbxEVtR46q2EFbrINqp+UtQkUHrs/2ZZBoiW7iBCj5f2fMDQPPq/VwAAIABJREFUJVYrXSLsu/e47FLwd7cURl4uhXrKTkvSbhDMl+TEpBZ+RXb1zUZEd7WtnYXcc/vhmL1EfpTiqL3Y+bPz144SHLVpv+/6ms+v5Wva7+0tKsDO43+gVHVE9P5QD2kBzMV1snFhrSznSpjTqiXwPO1QnePXKUABChhAwL7037A/M7FCTzKm/B2WNhcaoIfsAgUoQIHkC+gtpOsKPrqjz2fTkdMgdL9d9w/CwK9GoWOgfyv8cS6G3TgFddwhjdYia7qEduUd3F7k+gzoDQAq+8ToaeMPKZB7o7tA7s2D5HQjT8oRzkcxVE0X+foz0+X0I61NqYGyrEs4jbvuDRm6yD1vyTtma0ufZYPkm319C9XqeaMeL5++yDhiVdMl2r4HHZ+sflkilYmfc59sdJWcYDXcOzDTAxP5PbE6Mnpv8WHsLPwDu4r+dIYwu47/WfbfvxXLvzrE6Gpb61RZAXOaBDBZzlUxzWsEWscZo5exGQpQgAJGF5DCkKX33wXnNxNel/WWwbAOus3ovWf/KEABCiRMQG/o4qrRshStZi3FsA6hV1y7ApTNQe/fkXsXJj1Rvh1JGzBDl4RNe0q/iCtdtOnTGwBUNtW62pAit4/IGrcvpaF6srJknpxgVD3cz0+o0EXaKzuWWmqLPCN1VDy1WvS+Sk/osnklMFKOhNYu71otet+hy0saC7jSRX4/nNOLfhUH7+Oso+17pWP0KmTsU/xXL0zk98UqdKmsB8UOuyuI0QIZr2Dm+2P/w/bCQxF33iJbo3qfdAZ615cfJ7VAY66CidiSD1KAAqkpYH/lBWdhSe9LadVaTit6Qc5UtaTmoNhrClCAAnEQ0Bu6lJ1GVGUoBk4fho4V/n2vGAWH5WyU2u5AJn81Jl17F3Zkyf0z5X6vqhKFssplwl1zcDCs04sqWTUTzIXbi+LwiUl+kwxdtDnQGwBUNl962/A+rriHnLpzjxRfDVR3tUTCld2ydaWFfw0ZHaFLgWx3uf0dV62RelJf5FmpL9IkUH0RaX/bQdny43fSkp7QBbLF5z4pZrtNQ5EVO09fL/VughSQ/VVqsMhqhqCnPT0ilce7Bnk2WOhSIu8f4fX+x+UEpUv9rOwSgLwsp0VpB0b5fD2avovZbHlvVTmx6CpZ5eO/Q+a4jPX+ua5jwcMu8hvdXwiJCF0q6+GB0mNYX/Br+Y/Dv6JQti2Fe9WQejBlAYyEMPUza4TbBO+nAAUokFICjk1fwTbq/gp9znxuJpTzzk+psbCzFKAABeItoDt0kY64wpIpEpY0Rp0be+PMLPepRPk78d3yXBRetQCvD5USCO6rcMssPHzzDBSe1AyNrr4O2fLtRakU3P1ueS30fq4zNgwcDejYXrTnw7sw4bHVqNFnKM5tWhsnt70OfXzOrA6ixNAl3h+fpLTP0EVj1xuYVDZFutuQIOCZebL9x30qgVak95IzgEaeJS9SMOqn3cAq+ea9mayEmSUrYnwuHaGLdv8KSRqedm9z0VKdDhIQnOsVSuzbC2yS7U55ErjMkiOunYfTuy9doYufm/ZoSwmQOsp4PFe+BDpfSgC0W+uzV6HecMyDhS7+bWi/9j7uO3+fbEeS8TuL3Erg84LUV2npVV/Fv6ix3r4XiFlf9xaxCmOWrTdL5Z3aeLVrpKwyuiJI4eU4/HFPdujiPyQVKr4+/Bu+PpKHLwv2Yr2EMD8eOxDWyOtlVnevfnGtgqlprRLW87yZAhSgQCoI2EYOg+ObjT5dtVzTFxnDHkyF7rOPFKAABRIqEE7o4uzY4b34YtE8LF2yGvu2yPdAcmW26o7sXn0xIKcTsv1Ohy7Yvgzz3/gHvvvwW5SisQQn/TBg+CC0s+kvpIvivVj00lNYPGe1s41mM+ZjTBev75OCiTF0SehnKVEvY+iiSesOTCqZlnDa0FZgvLoYyN1d+TxnSeDyiv8WJJ2hi9byGjn9YEaA03V83iqhwDNDfbcg6Q1dtHZ+/hp46lMJbyobioQ+Dw6Q46K9/qLR61VZ6KK9coe8f0Jl75fA5UlZBdMhQPgRUd8rObXIm6CHvPMhWWWUwMtooUugoWv1YbQQ5isJYb4+IoHM4TwckYK/eq5Tq9aW4KVF2SqYDIXL7fW48R4KUMDYAvZ3F8A+y+94jRo1kPni61Cy/2LszrN3FKAABZIgEHbokoQ+RvxKhi4R0xn5QYYu2uzoDQAqm8lI2siTVSCLfwK+2y19kCBGu7IkJDi3sRxPLatG2kpIYfV/aRihi/boUWl3jQQTn0uqu9GTjEgA0bYJcJmssOkghV79t8iEE7po77BLn1bJCo+1Mh5tLM7VJRK0tJZxXCQrbLT3nOx3io9er1Chi8/7Za+TtkJIu5rI+LqIYS8JPupVMnGR9N3zznUy3vUybm3MzteKa2fxvKytbC3SkWTH+G+GVAhd/Id8qLQQHx34ER8f+tH5s13nyUmnV6/nXPkysNH50Ary8qIABSiQkgJ/HELpnbdClRoB3pe1/99gHSL/IMKLAhSgAAUqCDB04Yci1QQYuqTajLG/FAgikIqhi/dQtEK8Hx/8CR8d3IaVf+7SPc93ZF0I7QfDF91kvJECFDCIgG3ebDhkCbv3pdQ/GRkvvgbtZ14UoAAFKFBRgKELPxWpJsDQJdVmjP2lgElDF+9haduOtPBFC2E2H5H6PDouhi86kHgLBShgHIFgq1xkhYu20oUXBShAAQoEFlB/3QMclzqYZrxq1oRyqux84GUqAYYupppODiadBVJ9pUuwuVv2x46yAGa31IQJdTF8CSXEr1OAAkYQCLjKRWq4aLVcIDVdeFGAAhSgQBCBfb9BPSIHWJjxqlsXyikNzDiytB4TQ5e0nn4O3kwCZg1dPHNU7LCV1X+Zv28zHCEmj+GLmT7dHAsFTCYQZJWLdlqRdmoRLwpQgAIUqETg0KEKtbBM4yWBiyLBCy9zCTB0Mdd8cjRpLGD20MV7ar+RLUev//Y1Xsv7CqUhiu8yfEnjPxQcOgUMKhBolYvlzLOctVx4UYACFKBACAHZWuTcYmTCS2nSFKhSxYQjS+8hMXRJ7/nn6E0kkE6hi2favjv6e1n4clxWwlR2aeHLXVkXoXWtRiaadQ6FAhRIOYFgq1weGgtLzytTbjjsMAUoQIFkCKi/7AaKi5Px6vi9U7aWKo1Pi1/7bDlpAgxdkkbPF1MgtgLpGLp4BLcdO+AMX7Qfh23B/x9wnYxqGNe0C0ZkXxpbfLZGAQpQQKdAwFoubS5A5pTpOlvgbRSgAAUooB4uAPbvNxWEktUYOOEEU42Jg3EJMHThJ4ECJhFI59DFM4X/lWOnPeHLH6XBq9pfeVILPHb6ZWhfm/+aYJKPP4dBgdQQCLbK5YmnYbm0c2qMgb2kAAUoYBAB9bc84OhRg/Qmum4odeoADRpG1wifNqwAQxfDTg07RoHwBBi6lHvtLsp31nvRApj/lRwLCFlFsTqDl0ebdAkPmndTgAIUiFAgYC0XCVsyJHThRQEKUIACYQrY7YDUdlFLSsJ80Fi3K9WrA6dlG6tT7E1MBRi6xJSTjVEgeQIMXSra7y0+7AxfZvy6Dvm2ooCTc9mJpzu3HGk/86IABSgQN4Egq1y0bUWKbC/iRQEKUIACEQiUlgJ5e1M2eFGqVQO0bUVWawSD5yOpIsDQJVVmiv2kQAgBhi7Bgb49uh8Tdn2O9//3Q9CbtBUv2soXbQUMLwpQIP0EVFX1GbSiKDFFCLjKRQrnZkgBXV4UoAAFKBCFgMMB9Xep73LkSBSNJOHR2rWhaFuKYvz/b5IwEr4yhIDhQhetJkOLdc9z4ihAgTAFetRrjqXnDwrzqfS6febe9c7w5UCQLUdajRcteNFqvniuX375DYcO5cMh/w+dFwUoYG4Bu7ZU3euyev3Lo3coY7FYKkD4hzY+Nxw7Cvv8eVCP+W53tPa7BUrDyE9Uq169GrKyTkHdurXNPTEcHQUoQAE9Alp9l/w/oRYW6rk7afcockqRWvdEKDVrJq0PfHFiBQwXupQ67Kjy+fjEKvBtFDCBgHYk8qwzrzHBSOI7hK3H/ocndq3Au79/H/BFmbItuP+3dZD3+Y/4z3++QWFh4G1J8e0lW6cABVJBIFD4koyAtnnzbHTv1gF/vakXOne+MBXo2EcKUIAC8RPQarxowUtJMVTZfhTbdYuRdFt6kCErqatWBarXcP3MK60EDBe6aPodvp6F9QW/ptVEcLAUiFZgdstrceupbaNtJm2ef2Xvl5iw+3PsKy5finreJ0ewa/Y6HC4wRyX8tJlMDpQCFCgTuPKKjnhq4v04//yWVKEABShAAQpQwAAChgxdJv2yGo9sX2oAHnaBAqkjsL/jw2hQhcsUw5mxnwsP4omdK/D+L9+i9Yt5WP/pl+E8znspQAEKGFbg1VlP4PbbbjBs/9gxClCAAhSgQLoIGDJ02S//8py15llISaR0mQeOkwJRCQyVrUWvcGtRRIYFBUfQ9Yoh2PRl4O1GETXKhyhAAQoYQODvz4/B8GG3GKAn7AIFKEABClAgfQUMGbpo06HVXHhc/gWaFwUoULlAhmLBzx0eQNPqJ5IqAoGcq+7C4iWrIniSj1CAAhQwvsB77z6P6/tebvyOsocUoAAFKEABkwoYNnTRvLtu+gc+/3OnSek5LArERuClM6/GXVkXxaaxNGtl0uRX8cijf0+zUXO4FKBAOgk0aHASfty6RE44qpVOw+ZYKUABClCAAoYRMHTo8lvxYVy+eR6+P/q7YcDYEQoYSeChv3TC5OY9jdSllOnLwYN/onH2ZSgulgr3vChAAQqYWGDkiMGY8uwoE4+QQ6MABShAAQoYV8DQoYvGlifBS//v38Xq/N3GVWTPKJAEgcdP74rxTbsm4c3meOXkZ17DmEeeN8dgOAoKUIAClQjUrFkDfxxcj8zMDDpRgAIUoAAFKJBgAcOHLh6PsTuW4andKxPMw9dRwHgCLU84GZOa9cQ1J/M40Ghmp/3Ff8WGDVuiaYLPUoACFEgZgfdzZ+DaPt1Tpr/sKAUoQAEKUMAsAikTumjgvxYVYPZvG/Hhga349uh+s8wBx0EBXQLdTjwd/Rudh1sbtdV1P28KLqBtKapWozWJKEABCqSNwKiRt+LZZ0amzXg5UApQgAIUoIBRBFIqdPFG+9N2HP8tPISDpYWwqw6jeLIfFIipQKZiRYMqNXGWrG6pauGy8Fjhbtu2E2edkxOr5tgOBShAAcMLXHdtD+QunG74frKDFKAABShAAbMJpGzoYraJ4HgoQIHECaxf/y06XNIvcS/kmyhAAQokWaDrZe2wfNmcJPeCr6cABShAAQqknwBDl/Sbc46YAmkvsG7dZlx8af+0dyAABSiQPgIMXdJnrjlSClCAAhQwlgBDF2PNB3tDAQokQIChSwKQ+QoKUMBQAgxdDDUd7AwFKEABCqSRAEOXNJpsDpUCFHAJMHThJ4ECFEg3AYYu6TbjHC8FKEABChhFgKGLUWaC/aAABRImwNAlYdR8EQUoYBABhi4GmQh2gwIUoAAF0k6AoUvaTTkHTAEKMHThZ4ACFEg3AYYu6TbjHC8FKEABChhFgKGLUWaC/aAABRImwNAlYdR8EQUoYBABhi4GmQh2gwIUoAAF0k6AoUvaTTkHTAEKMHThZ4ACFEg3AYYu6TbjHC8FKEABChhFgKGLUWaC/aAABRImwNAlYdR8EQUoYBABhi4GmQh2gwIUoAAF0k6AoUvaTTkHTAEKMHThZ4ACFEg3AYYu6TbjHC8FKEABChhFgKGLUWaC/aAABRImwNAlYdR8EQUoYBABhi4GmQh2gwIUoAAF0k6AoUvaTTkHTAEKMHThZ4ACFEg3AYYu6TbjHC8FKEABChhFgKGLUWaC/aAABRImwNAlYdR8EQUoYBABhi4GmQh2gwIUoAAF0k6AoUvaTTkHTAEKMHThZ4ACFEg3gcu6XIQVy+em27A5XgpQgAIUoEDSBRi6JH0K2AEKUCDRAgxdEi3O91GAAskW0EKXpZ/ORkaGNdld4fspQAEKUIACaSXA0CWtppuDpQAFNAGGLvwcUIAC6SaghS6fLf0HrFZLug2d46UABShAAQokVYChS1L5+XIKUCAZAgxdkqHOd1KAAskSUBSg62Xt8eknrzN0SdYk8L0UoAAFKJC2Agxd0nbqOXAKpK8AQ5f0nXuOnALpKOAJXZZ99o90HD7HTAEKUIACFEiqAEOXpPLz5RSgQDIEGLokQ53vpAAFkinA04uSqc93U4ACFKBAOgswdEnn2efYKZCmAgxd0nTiOWwKpLEAQ5c0nnwOnQIUoAAFkirA0CWp/Hw5BSiQDAGGLslQ5zspQIFkCVgsFvTo3gGf/Pu1ZHWB76UABShAAQqkrQBDl7Sdeg6cAukrwNAlfec+PUY+GMsPjULXut6j3YnZ1hzclh4AHKWfAEMXfiQoQAEKUIACyRNg6JI8e76ZAhRIkgBDl/jDj1i+AVO71PJ60RGsGNEO3f4e/3fzDQxd+BmoKHB5z0u40oUfDApQgAIUoEASBBi6JAGdr6QABZIrkI6hy+s/bMWQM8N0LypG/v5t+M+izzDthTlYsV3/8wxd9FvF/k6GLrE3Tf0WWdMl9eeQI6AABShAgdQUYOiSmvPGXlOAAlEIMHSJAM92BNveno4BAxfgGx2PM3TRgRS3Wxi6xI02hRtm6JLCk8euU4ACFKBASgswdEnp6WPnKUCBSAQYukSi5nqmaO9qjO12J6aFWPXC0CVy4+ifZOgSvaH5WmDoYr455YgoQAEKUCA1BBi6pMY8sZcUoEAMBRi6RIm5fRF6nzEaSypphqFLlMZ8nAIxFmDoEmNQNkcBClCAAhTQKcDQRScUb6MABcwjwNBFm8tKCtte0An925+P3jf0xnXtG6NaRsW537XwTpx+0+qgHwqGLub588KRmEOAoYs55pGjoAAFKECB1BNg6JJ6c8YeU4ACUQowdAkRunj7XjAUyxcPR9f6fuj5GzDypMGYFmQuGLpE+SHl4xSIsQBDlxiDsjkKUIACFKCATgGGLjqheBsFKGAeAYYuYYQu2q05z2Jnbg6a+qx42Yv3L++JvssCfy4YupjnzwtHYg4Bhi7mmEeOggIUoAAFUk+AoUvqzRl7TAEKRCnA0CXM0EVun7j2GzzavqqXfCXbk+SuWIUu5w8ajpG3dEKHc05Ho7pVfbY6FR09gvzdW7Fi0SJMfTRX16lKwT86jdF15FA8ektntGleH3Wrue+0acdm5+E/H87B2OHe7whQrDbE6h//d2tjGzu4N7q2Phl1a7pt5X1F+QfwzSdvYeyTvsd0Vzz2eydmW3NwW4BBhXNvqLlq2mUwJj7RD70uaFzuIu/U/PdtXo8Fc17B2LnbIv5TGU+HiDtlwgcZuphwUjkkClCAAhRICQGGLikxTewkBSgQSwGGLuGHLhW/MY/vSpemd47F/FF90aGJd9BTyaeg6CC+WfgKhug80tq7paZ/HYXXn+qPrqHelb8NL94zHPe+vVcejyJ0uaA/5s8ejv7n1Kr8Y120F0smDUfvia5AI5wgJZx7g4cujXHPO//A1Oulrk+IP4D53y/C2L6j8WKIU618mkmAQyz/3kj1thi6pPoMsv8UoAAFKJCqAgxdUnXm2G8KUCBiAYYu4YcuFVa6HN2Mp+r0x9ggsxBq9UTwyWuMEf/6BybmhP5GP1Ab2pHWE/veiae+1vfxOH/sHCwZ1w4NAxQLDtiC7SBWPNof3ab2wPJDo9C1rtddOla6NP3rs1g8OwctQ6UYZc0WY9vc4ThryOoEhy59seTKBZja3b+YTyWu+6XOT2ep86MjeEmUg75PQXrcxdAlPeaZo6QABShAAeMJMHQx3pywRxSgQJwFGLqEGbo0H4tNP/TH+V7BRNGa6ajeeVbQmYosdJHAZXkupnYJsQIk1Ofj4GZMG9IfIxdVfmPTkXOw6Zl28M5NQjXt/HqRbOu5KRdN54UZuuQ8ia3v9A0jcPH0RrZyPdQXuwYvxZAzvXsYx+1Fc7eizaDwbYrWz8JZl0zHrsogE+igaz7T5CaGLmky0RwmBShAAQoYToChi+GmhB2iAAXiLcDQJYzQRbaA5M4fheuae2/zka1F10gR3UpCjUhCl16zFyF30OkVt7LYjmDbmlV4/4PPsOIT+bFd6q9c3wldr+qB63LaoWWg1GT/atyWdSdmB/swdZcA5F+BA5Ci/duwZG4uZn+wAEu+bole9/bEAO347EvLV98UbV6N/zSRPuhe6SLbkX6XkCbQwpH8nVix8GO8+NZivL8SMjZ51+CrMKD76ajrCbpkPEvyO6FXQkIXqWOTXxV1nWMrxq41izD7hUVYsHADdjVvh+uu74Eh/a5Cr4Dbow5iyZBO6D03GHxiHeL9d0kqtc/QJZVmi32lAAUoQAEzCTB0MdNsciwUoIAuAYYuIUIX7Rvr9q3RS77x73up1zf+Tt3y7S6VYYcduuRMx85/9UBTv0ZDbxdqiXtyp2Nqn4rbkfYvGodG1+QG6GZjKQz8sV9hYHfAsHAKut20IOBKjZBbYirZXtTrnaVYLLVR/K/89XMw4JIpWBIIUzuu+19yXHfDYNLxWunieZ+ssBk/CN3cNWX8e9HrmQWYP7J1hZVC+z8R996B3IFEO+j6CyFNbmLokiYTzWFSgAIUoIDhBBi6GG5K2CEKUCDeAgxdIhSWwq4rZk5Et4dWh2wgvNClMaZu/BgjWvsWzS36cRGGXDMaC3TUCAm4SsYmhW/P7ot7/Z/vLkdgfypHYPuNIn/lFLTpNifyrTFBQ5eh+E/BcHSo6fvCoh9z0ffscYEDF8+tzWVlyNogK2QQz9Al0nBNOr77M3RrNhwrKnxKEu8Q8oOaRjcwdEmjyeZQKUABClDAUAIMXQw1HewMBSiQCAGGLhEoH92J92fOwUSdRzOHFbo0l60+P8lWH+9u2fZiQV/Z1hOiLkv5I+2k0Owcv5onwK63B+P0ARt8BjxksRSlvcJvn48EBb0lKAi44sSPq+m0XGy9v2XFbVBBQpem03Ox816f0QFaIeK2UohYR6CEQa9g3+xOqLjgJY6hS6jtWR6T7rJC6VO/FUpBiiwnwyGCT7ppH2HoYtqp5cAoQAEKUMDgAgxdDD5B7B4FKBB7AYYuUZgeldUur4Re7RJO6NJUarnslFou3leoQr0BRxAonJDVJKfLapLywq45WJz3LHr5JBjFWDfxKlw8XjsKWs/VCbk7XsF1TfzuDRK6TN24VVbx+N67a+GdOP2m0CuGXE8F2w4Vv9Bl10IJq27yDasCy/SXWjVj/WrVBO5XMhz0zGa63MPQJV1mmuOkAAUoQAGjCTB0MdqMsD8UoEDcBRi6REtcjP2LZuDia4JvxQkndAl470Pt5FjmcPspW3H8j3GWLUbTqvbFyLKmZFWN3W9VjazMGCvHXz8VxusCBUUIGLoE6BNkFc/lsopnWRgvvH8O/pzmf5pQvEIX7bQk/f6v/7BVx6lKyXEIQ9j0tzJ0Mf0Uc4AUoAAFKGBQAYYuBp0YdosCFIifAEMXzVa+sR4h31j/PbBz0y490OGcs9D1ynboekFrNK1w6k7lNT/CCV30fdOu7/Pw+rcSAJzjfa8EHGdIwOHZxhMovNg8B0rbKfpe4Lkr0LaagKFLgJBHtu4MkJOVFoT1xuFSF2aoX12YOIYulXw2/Lutb/6S4xAWsclvZuhi8gnm8ChAAQpQwLACDF0MOzXsGAUoEC8Bhi6hQxdf+8boP+1ZTLu3NRp6jjB23hD86Gj9oUuA7T6VnAIU6jMR8r0jZcXIM74rRvKXTcSJl4cXgQABVm4E6ndzKdr7k1/R3u9zoZw3LtRQ/L7eGPN/Wor+zb1/O4VClyQ5hIls6tsZuph6ejk4ClCAAhQwsABDFwNPDrtGAQrER4ChS7ihi2seAhWQDVZ7JWT4UTa1CQ5dpH6M6lc/Ztvcs3DWkHA/azpDlwAra7RTkk6UU5LCvfStKHG1Gs69+ucqcI91vStJDuEam/l+hi5mnl2OjQIUoEAwARVqSSFgKwIcpfLDniZUCmCxAlY5GTOzOpQM3xMyE43A0CXR4nwfBSiQdAGGLpGFLkBfKUL7pG8R2iAn1YTzjbyub9p1fmoi2V60682eOH2g3iK6no7oDF1QcVtNWoYuSXLQ+bFJi9sYuqTFNHOQFKAABVwCEq6ox/+EWlwAqGraqygZ1aBUPxGockJSLBi6JIWdL6UABZIpwNAl0tAl0AqKg1gysBN6v+k7o+GELhXv1U4TOl9OEwr3UxIgCPEPhe6V7UXT/bYXRbTyJECNFb01XeREJUVOVArvaicnJs3xOzEphbYXBQhdkACH8IzNfTdDF3PPL0dHAQpQwCOgFh2GWniAYUuAj4RStRaUmqfIV2QlTAIvhi4JxOarKEABYwgwdIll6BK4IG84oQtmyZaf22JwZPTYBTj+RGtU8/6YVTgyehQ2FQ/G+d61aXZ/hm7NhmNFOB/Pm1/Bvnmd4HPydMDQJcD2qSCrgyp/fYB+I5VCl+Q4hDOlZr+XoYvZZ5jjowAFKCA5S+EfssLlD1JUJiBbjpTajaBYfAoVxtWMoUtcedk4BShgRAGGLpGGLgG+cUb0K13QXLbg/OR3jHMlRXoDf6baSR2TOX5HFwO73h6M0wds8HqknRSkneNXkDb8I5yHLF6N16/wO9IpSAHgR1d9g4mXeu8llpU842Ulz0T9fzqaTs/Fzntb+j2QSqELkAwH/cLmv5Ohi/nnmCOkAAXSW8C5najwUHoj6By9otV5qZ2l8+7ob2PoEr0hW6AABVJMgKFLZKFLUzn5Z5PfyT+IQU0XoDEmrv0Yj7b3K3ImK1DuvXw4XvQc91zJ56yXFMjNlQK5PqtcbNvw4tl9ca/f813nL8Xyvzb2aS2sOis5ciJRrpxI5P8PJMFOXQqyAqebbDHStbqmuWyb2jACXMzfAAAgAElEQVQKXev6A6RW6IIkOKTYX01x7S5Dl7jysnEKUIACSRVQS45BPbIvqX1ItZcrVWu7txrFv+cMXeJvzDdQgAIGE2DoEn7o0vSvz2Lx7By09Ek1gKKv56B6uykVZjjsOi0507HzXz3Q1K+lor2rMbHvnXjq62Afopa4J3c6pvZp7Bu4yO37F41Do2tyKz7YXOqxbByKDjW9vyTbpB7qi25TQxTU1QKQtRKA+C1ycbYU9KjrTlKP5RW/eizF2DZ3uJyatDrEn45OsoJnuqzgCVR1P8VCFyTewWB/9SS1OwxdksrPl1OAAhSIq4Ajfw9gL4nrO8zYuFL7VCiZNeI+NIYucSfmCyhAAaMJMHTRE7q0RK+bm+H8C3uga/f26HpmrQDTKFuLhkgR3bkVv9R03lLsvNl3NUnR9s8wdugUTFsZONgIuFpFa9p2BNvWrML7H3yGFZ/ID1m5cn5Of/S9oQeuy2mHlhVWgMgzskqmt9RpWRLkw9dV6sgsljoyPhlS0V68P+JW9H0lSPBywVAszr0TvRoHOXYwaOginQhwZDJwBOumjsLFDwUJXprnYOY743BP60D22sBSLXRJvIPR/u5JZn8YuiRTn++mAAUoED8BtfgI1KO/x+8FZm458wRYpL5LvC+GLvEWZvsUoIDhBBi6xGZK8pdNwYmXzwnc2BNS1HasX1Fb953Bt/I0xojluZjaJVjIoLPfBzf/P3tnAt9klfbtf9KVshSwCAgimwoKsuhQdHxBWRWqI1ZRW0eo6IjKUEeWGT9A3xEclMWxKAoDWPQVVKRuFEc2FUQF1IIDWnQoIMhmK7QFStM2yXc/SVqSNGme7Nv/+ONXbM56naeluXrOfWP+uAxMzm+ovrOxdDj+zRYs+7885L20BTvl6tOgO0Yi4+FbkH59ZzS3XCmq3LsdhW1S0cda+DQkXWQqzqRSqfSV938rsXK1lVDKSse4m7qjTa0VKtmOT0pTMair9ZrCULoEmIPKJyYqqlG6RMU2c5EkQAJRSMBQfhSorojClftmyZrmHaGJ8W9QXUoX3+wVeyEBEggjApQu3m/WcREu14lwOeC0K8m2c1ayBNldR1KqNxw/pT0efftVzLuj/nUhNbN2fR3JupcBeOnbuQ2cJHEy4vHtmDxwC0bYx1lxIV2U2DWTPliJWWkp9a5CNbi2SpErd6UBz/1gFyg4PKVLIDmoeWaipQ6lS7TsNNdJAiQQVQSMRhhOFkXVkn29WCWFtBLfxZ+F0sWfdNk3CZBASBKgdPFiW+QKzieLFuCBSfkNCBdz/05PdnwmJ2QGOzkhY5lap7unYOkzGRjU0clVHvslVJZg5+pFGDdmpZxOcae0R8b8mZg1PhWdHAgi2550OPDZStMVqZX7JLbLb3bBbeU0yoTWWVjoYvg+E+Zgxd8lPo6ja1F2bUv35GP6uKlYKDFtln4fKdLFvMhAcHDnSYj0upQukb7DXB8JkEBUEpA4LqZ4LiweE9A0ag5NkqNgfR53Wa8hpYvvWLInEiCBMCFA6eLmRlWexvGDRfjy/TzMmpbnltToM2Em5j08DNd1bIpEi9RwJ1NQn7HZmPzHAbi2R2e0bZ6ARKvTn5VnTqP04A/4JD8f89ycVz0CXVMx6c9ZSE/rgz5tzs8VNTpUlhajcM92rJi52CoejaS51tulud6bB41kJFJXumPcM1nISOuPvl1T0NxK+FSWluDAnp14N3cRpi8vtHSXgU0nptsG8G3gZI07gqZ+0GMJKjwpFYNfULcSd8aq36N/OahbQXTUonSJjn3mKkmABKKMgFwrMl0vYvGcQHwTaJu28by9ipaULiogsQoJkEBkEYhG6RJZOxgCq5mQi1M5qbAJ6aLiBI/nM/dW8ng+cmi1JAdP94PSxVNybEcCJEACIUxAUkUbmCraqw3SxDeGpql/g+lSuni1RWxMAiQQjgQoXcJx10JrzuPWbsHSm2yPou584Qr0neSfeXbKycP+Cd1tOj+eP1VSYjcYLdg/kwlir+TgOXxKF8/ZsSUJkAAJhCwBShevt4bSxWuE7IAESIAE6hOgdOFT4RWBNDlt8bZcLbKOAVNTiIVXpmOCpLP2eekq8WO+kPgxNo7Hebpun48fKh2Sg1c7QeniFT42JgESIIHQJBDp0kUv2GP8i57Sxb982TsJkECUEqB0idKNt1/29JlY22o/5r+Yi0/UypK0KfhyWRautYu3Vrk1B40GLm4Y7L1TkDe0Ch+9mINlEhhXVemagby1U3B7V7uAwvvyMfjyqfhEVSchVokcgrIhlC5Bwc5BSYAESMC/BCJVupzZhiXTpuPQiDcxc3grvzKkdPErXnZOAiQQrQQoXaJ1523XPeuLnZjW3ywzKkt+wYG9+7FzbyG+/LxI/p6Pj2rFyDUDkNG/D9L/mI4RvSXds1UwX3Njc0rnB1zc9Bn09npsklTYpiZKsNx9RSjcW4SCDbtQ+MsuvPvZL+YJSlDf2/v3xoisW5B+fWc0tx8PEuj2r+kYPM9SP8y2kxyCs2GULsHhzlFJgARIwK8EIlW6FOdjRuYUYNIWShe/PkDsnARIgAT8RIDSxU9gw6rbh/BlWTaubeLtpHUoXJ6NK8ZtcdFRKlb8mIuMrt6OByjZn/pKyu0D3ncVhB7IIQjQTUNSugSLPMclARIgAT8SoHTxGi5PuniNkB2QAAmQQH0ClC58KvCYZB+ab5t9yH0qp7HzpSnom+1KuEjPXSUOzI92KabdHlCH4/kLcN0fwlW4kIPbW+7DBpQuPoTJrkiABEggVAh4IF3KijZgxRsrsbtgG6rPyUJaDUSX28bjwdt7I6U2for1SZPfn8baV+cgf9Nm9HymEI/0sCz+zH6sf/NVrPtsC8qKi4Hk3mj7u1uQlpWBVLsbQRVHNyN/zRbs3rEZxw4fMXUQd/FQdLgpQ8btf35c7MLLw+5BgT3ffnMxc1Ya6nIMuTG2q62idHFFiK+TAAmQgAcEKF08gBZxTdoj4+9TMHlsf/Rp39Tt1VUe3I6F02Zg8lvqr/j0mTAT8x4ehuu6Nq1/RcnVDEr2Y+XMKch8qdBVzZB/nRyCs0WULsHhzlFJgARIwK8E3JIuOux+Kxs5r24W0ZKGnsOuQJIyubIfULApH9W9RGw8JWJDES+10uWhuWj70RzsbpeGvl1aoevQLNx4kbx+fAOe/vNEHCprh+Qht6Bb62Zyd/oX7N24EmVlvdH3+eUiZ87Hoyt6Pwuz3wE6pA5A22Rl0HKUFKxBUaEImP5W4+IIPn1tPfad/QG735d72/3Ho2cX6fuCPhid1humpm6O7Yo/pYsrQnydBEiABDwgQOniAbRIbqLEULljKDKH9kH3ru3RKUWkiHVmImXtlTqUlhxB4Z5tWPtKHp7J90Z+dMeICcNw+82puK5bFxkvHolN7ALl1ugk7kuxjLcLH72Zh2VLt4fpdaKGHhxyCOSXFaVLIGlzLBIgARLwjoDh1EEYz8rpEZEqRt0ZoFo+Ggz1O9VXwVh1BjGXXAtNgsiJBkrZlpmYNGslkm7LxZMPWZ8ukUantiHnf1ei7V8WYHTH89LlGESgzF2MR3pZ9a0/glWPD8H6UxkY9ewMjFQkTG05tx+rZo/F+u/SMPatqbi+keUFh1mIdChYMhYvv3MEPZ/dgOy+Vj8LOYvp4snYLraC0sW7Z5WtSYAESMAhAUoXPhgkQALRRoDSJdp2nOslARIIJwLGyjLof1oH/fHdMJT8JJJFuffjumhglP80iPufx6FNNgfrd1xElGQroiQbjywbj75xLvq2SI9jAxZg/vSh5hMmtaUoFxMfnoOUJ7bjyRsdiJ6f5PUJ8vrULXhyiIvMQ2VyYuZOOTFz55tY+mDv82M4ky6+HNsyGqWL6+eMNUiABEjAbQKULm4jYwMSIIEwJ0DpEuYbyOmTAAlEJAFj+RFUf/c29Ps/82h9ddJlwCRom7Vz3scpkRt3OZAbzlpYpEfZ/e9iwd3dbWodWzcRM+ZvQMqQqejS2kEHlqtBFXYipbpsP7Z/vgafb92FkuLDKLPEdjH1YB+zxYl08XTshuBSunj06LERCZAACTRMgNKFTwgJkEC0EaB0ibYd53pJgARCnYDh6E7oPp0N1FTWn2psIrQtO0PTKNl8bSixOaDR1q+n9nqRuymYG6hfKz5c8q2TLjoU5UtcuwV5qG7UGckX90GH33Uxx5PBLyhasRIlbkoX9WO7rAlKF9eMWIMESIAE3CZA6eI2MjYgARIIcwKULmG+gZw+CZBARBHQ/3cjqr5cYLMmTaMWiO11N2LaXQ1NkwvVrVdtIF0PT7pg0hbMHG57RahWuvR93iqTUUOztVw3wm2LJJbMQKtMRUojS7YiN6WL6rFVUKR0UQGJVUiABEjAXQKULu4SY30SIIFwJ0DpEu47yPmTAAlECgH9L9+gatPTNsuJvWo04uQPYuLdW6Za6SJZgVzGdLEOdtvQyRhLXJWkRzbi2dsauNJkWYlZ0gAjX1+AUW3slqcX6XKzpIhWKV3g5thqYFK6qKHEOiRAAiTgJgFKFzeBsToJkEDYE6B0Cfst5AJIgAQigICx7DAq8yfVXSnSxCchftB0aFv38Gx1qqULULFjDiZOz3WYvaj6aD5mT8lDB3l9rBLCpcHrSMVYO30A3tudhmHPz8BoJaWzXSkr0yE52ZyNqGTjFPxtznb0nCUZivpZZSjSl2P7qw9hyTu7nMZ0qRCxM99G7Lg3thqolC5qKLEOCZAACbhJgNLFTWCsTgIkEPYEKF3Cfgu5ABIggQggoPv4CRhOfG9eiZxqSRg5D9oWHT1fmRvSBdBh91vZyHl1M5A8EF2GpCIlURIlndiO3Rs3o7r/XMx8Kg1tY1xJF3n9+GbMmzYeew+3Qlz/NPTtYrmCVCkxWrZsQEmnqZg5S/pSVnZO0lHfl4XdZe3QNi0THUypkIpxaH0+yoZloMuKHOy2P+lSLW3ukDYYiG63p0r2pM4YPmYgOihN3RlbBVlKFxWQWIUESIAE3CVA6eIuMdYnARIIdwKULuG+g5w/CZBAuBMwnNwP3ZrH6pYRP2gaYi5O9W5ZbkkX81BlRRuw4o2V2F2wzZSZOu7ioeg2+jGMHSJBbhXhYnIi+ZiROQWOYrrUTVhfjIKNech/fw0OFe03f7pVf3S4IR1pt4mIsQ4Fc2oXVixZhK1fiNw5J6KmywD0HH0/xt5YjuXDHFwvUua5JxfzZufiWHGx9DsV2Suy0LN2cHfGdkGY0sW7R5CtSYAESMAhAUoXPhgkQALRRoDSJdp2nOslARIINQJVn8+X1NByykSKtvWVSLhJMhd5WzyQLt4OGWntKV0ibUe5HhIggZAgQOkSEtvASZAACQSQAKVLAGFzKBIgARKwJ2A04tyKOwFJ8awUn5xyUTqidPH6WaN08RohOyABEiCB+gQoXfhUkAAJRBsBSpdo23GulwRIIJQI2FwtklgujTLfATQa76dI6eI1Q0oXrxGyAxIgARKgdOEzQAIkQAKULnwGSIAESCB4BGp++hjVX71smoD2oj5IGPp330ymugKG8qO+6Stae4lvAm1T+1zWvoWhMUrxbZfsjQRIgARCmwBPuoT2/nB2JEACvidA6eJ7puyRBEiABNQSqP7mVdR8/76pelyfexF71Wi1TRuuJ9eVDKWHfNNXlPaiadQcmqQUv66e0sWveNk5CZBAKBKgdAmVXcnCpt+mYFBzq/mUbsfkC7IwP1Sm6Kt5XJOBpc9lYGTvdmjTPOF8r5U6lJYcQcHHeXhmbi4+2eerAdkPCdgSoHThE0ECJEACwSNQ9UUO9Ps2mSYQf8NfEXPJ730zGTk/YThZ5Ju+orQXTZMLoUlo5tfVU7r4FS87JwESCEUClC6hsithIl2uGYBpD2Yh86beaLFvAdoOznUPYNpM/PB2Oronumgmwmm6CKdn3OudtUlAFQFKF1WYWIkESIAE/EKg6pNZ0B/eYepbuVqkXDHyVTFdL5JrRiyeEdC26CR3vmpzZXvWh6tWlC6uCPF1EiCBiCNA6RIqWxrq0uUhrP35foxo37QOWOlnc9HCLemSihU/5iKjqwrmu3KhuXquioqsQgLuE6B0cZ8ZW5AACZCArwjo/j0Vhl/3mqXLyHnQplzmq65h1J2G8cwJn/UXVR3FNYa2WVu/L5nSxe+IOQAJkECoEaB0CZUdCXXpIidU9HJCxQqX29JlSA72rxsK+R2Ky3JgdRY637XdZT1WIAFPCFC6eEKNbUiABEjANwR0+Y/D8Jv5DnFC2vPQXqDmtzHqxzbFdbGko1bfijU1zS6CJi7J7yAoXfyOmAOQAAmEGgFKl1DZkSiQLvPzYHzMWtsI+8oSfLI0B8+8mCcxXNpj0B0DMOiWVDT/NBsTlofK3nAekUaA0iXSdpTrIQESCCcC/pYuRkkdbTx9LJyQBH2uShwXJZ5LIAqlSyAocwwSIIGQIkDpEirbEfnSZdDb67HpjvY2wI9/PANtR+aFyiZwHlFCgNIlSjaayyQBEghJAv6WLsqijedOwVjxW0iuP9QmpYlrBE2zdgGbFqVLwFBzIBIggVAhQOkSKjsR+dJl0qbtmHfD+ZgwgA5fzeqD654KlT3gPKKFAKVLtOw010kCJBCKBAIhXUzipeKkyJeToYggdOYUkyDCpS002tiAzYnSJWCoORAJkECoEKB0CZWdiEbpchqfTErF4BdCZQ84j2ghQOkSLTvNdZIACYQigUBJF5N4qSwX+VIsfzGGIoqgzkmT0NRypUgT0HlQugQUNwcjARIIBQKULs52QYUEuSYds57IwLgbOqNN84S6jirPnMaxvT/gk3cW4Zl523FA1UarGM9pPxIL5YF0ZAztjb49uqCTZBhq3uT8fJRmlWd0Ej5lPwq2bsHKF3Ow7BtXk3IwH1dNlNcl1fNkSfU8X/5a/2SLmg7MddQE6e10QxYmTRmKQcqaU5oi0SoNtXm9hfgyfwPmv5gr8WLUj11/3rZySBl32ox0jOzdzsQ58cz5NZtHcbWX3TEuJxuT7k5F9xTLPtXIfEuLsfPjNzF9ZgPzdfbMlfyCnVvXYtlsNXurnkWk1qR0idSd5bpIgATCgUAgpYuJh6HGfN1IV075Ijg0sYnQNGoBxDcOyuNC6RIU7ByUBEggmAQoXZzRb+iNc3s8+toizLq7M5q7OI1ZeXA7Fk6bgclv/eJim129UXfQvH8WVuRkYUTvFJfzsG2tw4HPVmL6Q3Ox0qmMCGHpkvYQ8p65H7f3sL6q1ADemtMo3Lgas7IbWu/59s6lS3dMW5uD6Te1h5XfsRFNLqVL1yysXTcRIzraSjGb2Vf+go+euh8j59k+M32mL8JHTwxAG5vB7dZdU4KvXpiO6/66JZjfVkJ+bEqXkN8iTpAESCCCCQRcutSxNMJYVQHUVIqIqZY/+gimbL00OcmijQHkKhGU+C2xDfwMEgAilC4BgMwhSIAEQosApYu70mUGsCnPLjaJiz2VN8KfzHwIg2cVNlDRA+mCbHxZ9hCubeLhM3V8O6b/IQvPODz1EprSZUROHlaM7+6mZLLwKS3EwkclK5ILAeZYukzBgawcjOvh4AcVq9M9DUqX1C0Y8cUUDEpRs19yuuav6RhsES+dJuei4DnJ6qSmqcTKKVyejSvGUbw4w0XpoupBYiUSIAES8AuB4EkXvyyHnbpJgNLFTWCsTgIkEP4EKF3cky7z32+FR8d2tj3poOoxsH0TXb+JJ9KlPVb8uB4ZXVVNwHGlffkYeflUfFTv1dCTLiOW5SPPI/ZWi5NTJO9Ouh/pi5yfPHIkXQr3AN2dnaxRKV1W7roCGTaBhF3sW2Uh5vdKx+SOM/HDB+no3tAJF/uuan7ByvRhyMz34tmI4KaULhG8uVwaCZBAyBOgdAn5LfLrBCld/IqXnZMACYQiAUoXN6SLxGopTZR4KbVXiuQN/Fer12LFO+uxML8QnW4YioxR6ci8dwC6OzqScGYXnrk6A9MdXunxRLoAj67bjpeGyDWbytM4sOcH7Ny1C598XoQDe/PxkekES3eMuFcyBA0ditvTJIaIg3kVLs+SUxHbXTye8sZfL2/8rWqpibti3amrWCkNTcD5SQ8djn+zHXnvyXo3mtfcJy0NI4amIf2OVPRp4+BkSq3McHK1ymUsmlLZ921bsPbNXdhZx9l69g72UonZIsd5Td5EeW7ekNNSb67Fu58Bg+4YiYw/y3Nzvd21JamqMF6YOBHT+lvW4bBtprRNqScCK3fl4oqr56qMKRSK3538NydKF/+xZc8kQAIk4IoApYsrQpH9OqVLZO8vV0cCJOCAAKWLG9LFWjhsy8WjY5zFCFFifyyW2B/13wg7FxWeSRekpWOcBHJd9pmrmDFmATNt03LMsj9tcXwLMtuNx8oGv0KCKF26yjWqbx1co3J5Xag9MubPwfwJvdHGLvZOQ0LCuXSR2DBv5CBzzErsbJBVA6eESuRK10jHV7ociiUrWVO5bwMmj8zGQgeyyPEpoP1YdnkaHnAjiHC0fJOkdImWneY6SYAEQpEApUso7krg5kTpEjjWHIkESCBECFC6uC9dSrfmYNDAxS7eeLeX7D0OYr/USFyRK9Mxod4bYQ+li9vP0QDkFS3C7R2tG5bgozEDMPKNhjoLnnQZ98EWLE2zC4TSgLywX4XjUzKy5nGy5uX11+xYurgTJ8WJdFFx5cep8GnwlJSyhlS5apZb76rZzheuQN9Jbj8kEd+A0iXit5gLJAESCGEClC4hvDkBmBqlSwAgcwgSIIHQIkDp4qZ0cfnm17q/dKw9MhMj2tiOUbh8mFznsT+ZEijpAnSS2Cj7JTaKdSlcegWueCgUpYsjgeEqPo46kVIp8qyRyDP74lB8HNyAwV2y8YmqL1/H0uX4xzPQdmRewz3cuwjHXpMMRXa1DryVhc6ZDV8Bc7SvpRtnocXwhs8wqVpShFWidImwDeVySIAEwooApUtYbZfPJ0vp4nOk7JAESCDUCVC6uCdd1Lz5te6xk2Tb2T/BOhKKvLonD5pekgXJpvhKurQ3xQi5/ZY+6Nu7PZo3b4VOzeOBRIkn0kB6a9fxWYJ00uWxXJyab5e1R4L/Dpbgv+oEiAVy1+ko+D4DfawZ1AuAa67rSLocWC3S4y5XcW9qN9SRdFFzmkhpPwUFuizbeTo9HWX3CE0QVjm2rFzva6h/h/LP/Chd/MOVvZIACZCAGgKULmooRW4dSpfI3VuujARIwAkBShd3pItkvhk+DOkb3XmcHKR1dvhm3zvp0unubMz7SzoG9U7xKJ2y6zfnwZEunV5bj/33trcBfuCNYeg8Rk0MG+tm7bH0+/UY1836c45FiMOAv39NlfTNavfdwV4qJ6SSJYiyyy4ctFV9yqb+HmGvCL4r7QWfy0lEfAVKl4jfYi6QBEgghAlQuoTw5gRgapQuAYDMIUiABEKLAKWLG9LFycmIhnfUUVpnCXAaIwFObRp6KF26puGlFTPw6DWSwcib4vLNeXCkizcZj+xx1GV6qntBuaZUX6Z4P6aHe2mal4O2LvemdkGULmq/BChd1JJiPRIgARLwPQFKF98zDaceKV3Cabc4VxIgAZ8QoHTxt3QBZn2x83zKX9NwPpIuaVPw5bIsXGsXY9ajB8PlG/vgSJd53/6ASb2tV+SIncoVSywbo30sm+USy2acbXtKF5U8w7gapUsYbx6nTgIkEPYEKF3Cfgu9WgCli1f42JgESCAcCVC6+F+61H8T7wvpIiciTkzBIEfCpeY0ju/7BT/sKcLx40VY+/URoLQIK/MLLYv15EREqEiXX7Dy8mHI9CQNMqVLOH6L8sucKV38gpWdkgAJkIAqApQuqjBFbCVKl4jdWi6MBEjAGQFKF/9Ll6Xf/YBxPazH8V66jHh7PdbeYRvrBJUl+GRpDiZn57lIZx0+0sX7UyfnufN6Eb8P1hKgdOGzQAIkQALBIxB06WI0wKivgQby0WgMGgiNRgNotIBWovwrH6OkULpEyUZzmSRAAucJULq4IV0cXgty9TRlyImU6bYnUiQbzfyEdEy2aepOHJA0SUU9xy4VtTtplMNHujgMpOtWJqFayI4C6ToOjOy96HFnL+2fH8Z0cfUV5YvXKV18QZF9kAAJkIBnBIIiXQx6GHVlQFUFjDWVnk3cn61iE6CJbwxNQjOzhIngQukSwZvLpZEACTgmQOnijnQBdr40DH2z3cicM3YRji0bgDbWwxzfgsx247HSY+niQJpIGuXOkkb5gKoHPXykCxykQYYnKaOHzMH+dWnoZM1HdcpoEVqTJODuC6rgSiVKF7WkglWP0iVY5DkuCZAACQCBli7Git9gPHcqbNBrklpC06hl2MzX3YlSurhLjPVJgATCngCli3vSBSJMHhBhskzVzrfHvG/XSCDYBJvajtMzu/FG3YGIcJ3y+fwUBklsk012AWVdpxYOTkwXhwIDOnw16xZc95R6+VX/9ApQuTUHjQYurreTPOmi6uEO60qULmG9fZw8CZBAmBMImHTRV8Nw5gQQiidbXO1hXBI0TVrLraMYVzXD7nVKl7DbMk6YBEjAWwKULm5KF6muCI6+g3NdnioZIXIjT+RGos0QJfho3ACMXG4/rhvSBQ5OqhzcgMFdsvGJqwciTdq+nY7utpOCJ9IFu3KhuXquqxHrXvdUZjiMX1MqV7TGyBWtfNfDd5qci4LnUtFc1T4Ans7zfPfu7KWK58BlZqnaPjw5weSaXyTWoHSJxF3lmkiABMKFQECkiyJcTh8F5GO4Fk1sIjTNLoq4eC+ULuH6RHLeJEACHhOgdHFfusjBWBz/eBFGjFzsJGBte2TMn4OFj/W2e6MvY8kb6MFXznAgRxy8UT+zC88kZ2B6vSlm48uyh3BtE+sXdChcni3pj7c4fRb6TMhB3nND0cleuCgtXL6x90L0WGbksczoKuv91n690qmIl4WPZmPCW85OvF7RGm4AACAASURBVDjfh0oRRleIMHJ0HcvjedaRp3Tx+BtSgBpSugQINIchARIgAQcEAiFdjGW/hGbsFjefCFOcl6Zt3WwV2tUpXUJ7fzg7EiABPxCgdHFDupw5jdLYpmheKy1K9+OT1WuwbM0Wczrmawbg0VFpyLx7KK7taHulyDyKs1MuymvtseLH9cjoaitSDry/AA/8NRef2KVInrZ5J2Zdbz+GyKBvtmDZ/+Uh76UtIoTaY9AdvXHt/6Qh/bZU9GlvqX9Gh8omCbYncFxKF8eix3p+nW4YihHXtELhvJUOT9x4IzMcn1ZRWClr3o689/Lx0Ub58w2gzCNjVDrS75A1t3GwD4rMulpklpO0097M07x7lC5++Fbl0y4pXXyKk52RAAmQgFsE/C1djBUlEsOl1K05hXJlTeMUaBJtz+uG8nxdzY3SxRUhvk4CJBBxBChd3JAuEnj1mdWtMOkB+ytDah4L1ydRZn2xE9P6O5I1DgK5puVg/wdyakXN0DZ1dNj5wgYkPpaG7tafdyldIPFpfpD4NC4GdBKcVmnlrcxwfF3LTQCVkrFo0v1IX+Q8Hoy386R0cXNPglCd0iUI0DkkCZAACVgI+FW66KtgKD0UWawlnbS2hfzEp6SYjoBC6RIBm8glkAAJuEeA0sU96TL5ghnABysxKy3FLlZLQ9x1OLB6LgbftbLhODDP5eHc5O4O+nWcPccTCWGOR9MZa/US18VN6YLHcnFqvn1sFLt1+1G6KCONeG4llsm1rTaeZFN0eR3JvBZKF/e+h4RjbUqXcNw1zpkESCBSCPhTukTaKZfaPdckyWmXRpFx2oXSJVK+krkOEiAB1QQoXdyVLlmYL9d2MhbmYOED3dHc1Zt/uYK08qkpyHxJrh+5LAOw9PscjOtmf9rFecriEc/l4qUJqY7jtFiPV3MahW/lYOQYRfx4HnDVpejxs3QxLemaDKxYlo2MHk1dEjVVUNa+cTVmZc/FSidXiqw7onRRhzWca1G6hPPuce4kQALhTsCf0sVQ+nNYB891trcaJZuRElQ3AgqlSwRsIpdAAiTgHgFKF0+ki6XNNemY9UQ6Mq7vjk4pVqKk8jSOHyzCl+/nYda0PCfBdp2N2x2PLp6KR2/rjU7NJe6KSeo4ly6mXrqmYdZzGaZ5tK1ro8gGid1SWoydW9di2ewcLJN4J+biuXRRWvcZOx2z/jIM13VNOR/fRhmrsgrHdq3H9IEzsNLB8ryXGbaddrohC5OmDMWgHl2Ef1MkWgUIrlTi1pQU4sv8DZj/Yv2YOA19lXg/T8Z0ce+7UOBrU7oEnjlHJAESIIFaAv6SLkZ9DYylByMTtFwt0rbsEhFro3SJiG3kIkiABNwhQOnijJY3b5zd2QHWJQESCDQBSpdAE+d4JEACJHCegL+kC2oqYZCsRZFatC06AlpXR6xDf/WULqG/R5whCZCAjwlQulC6+PiRYnckEPIEKF1Cfos4QRIggQgm4C/pYqw6C+PpYxFLTpN8MTSxjhIuhNeSKV3Ca784WxIgAR8QoHShdPHBY8QuSCCsCFC6hNV2cbIkQAIRRsBf0gUiXQwRLF20Il1A6RJhXw1cDgmQQFQQoHShdImKB52LJAErApQufBxIgARIIHgEKF08Y0/p4hk3tiIBEiCBoBOgdKF0CfpDyAmQQIAJULoEGDiHIwESIAErApEoXQqWdMfL72TjkfXj0de01l14edg9KID156wg6OXvMe49FpQu7vFibRIgARIIGQKULpQuIfMwciIkECAClC4BAs1hSIAESMABgeiQLkfw6WvrsQ+dMXzMQHSo5XBmG5ZMm45DI97EzOGt3Ho+KF3cwsXKJEACJBA6BChdKF1C52nkTEggMAQoXQLDmaOQAAmQgCMC0SFdnOx9cT5mZE4BJm2hdOGXBwmQAAlECwFKF0qXaHnWuU4SqCVA6cJngQRIgASCR4DShdLFGLzHjyOTAAmQQOAJULoEnjlHJAESCC4BSpfg8ufoJEAC0U0gZKSLvhx7t6zEqlVrcKhov2xKOyT1HYjU0eMxum8rxNlvU239N1bj0OEjQKuB6HnfVIwd0hlFrzqJ6dJvLmbOSkPbuhgvdp3Wve76meD1IteMWIMESIAEQpIApUtIbgsnRQIk4EcClC5+hMuuSYAESMAFgZCQLvojWDt7DN7bcgRx3TPQs297kSzlKClYg6LCI0i68008+2BvJNWuxWH9YhzKz0XZsDcxGvdguaNAunVSxRLj5ewP2P1+PtB/PHp2aQZc0Aej03ojWcVTQ+miAhKrkAAJkEAoEqB0CcVd4ZxIgAT8SYDSxZ902TcJkAAJNExArXQxnNwPw4nvYTi+G8aqs6ZOlb87KxoY5T8N4q59BNoLujY4iaL3x2P2y7+iy9QXMXlIO6tTLToUvT9dXtuOnrM2ILtfgqmfY+smYsb8DSYZM/N+kSS1mYdExrz39zFY+52cfDnnIHuR/UkWxnSBxiiFXyQkQAIkEE0EKF2iabe5VhIgAYUApQufAxIgARIIHoGGpIsiV/SFH6Lmhw/rRIvamaqXLoVYft/t2HrZAsyfPrT+KRO9vJ4lr3dcgGefHooUWOpjKrJzs9DTPtWzRaQcs0kPbUkZTelSb/soXdQ+0axHAiQQMQQoXSJmK7kQEiABlQQoXVSCYjUSIAES8AMBZ9Kl+uulJtniaVEtXWoliVwrSpVrRfVLOY5tWoRDxy0nV2rrZ76LpWO6O6gvp12mDJHTLjzpombvKF3UUGIdEiCBiCJA6RJR28nFkAAJqCBA6aICEquQAAmQgJ8I2EsXTdO2qFo3Dcp1IuuiiU+CtnVPaNvIn5adTC8pf3da5JSM4fQx17OuO5niqqqtdGkozXPBEleBdC1j8XoRrxe5euz4OgmQQOQRoHSJvD3likiABBomQOnCJ4QESIAEgkfAWrrED38G1V/kwHjm17oJaZpciNjutyKm62Bo4hurn6i70kXisyyVYLkui0WUVDyyEfNva+egejk+nZ2KFZ/ypItLllKBJ13UUGIdEiCBiCJA6RJR28nFkAAJqCBA6aICEquQAAmQgJ8IWEsXjQS8Nf62r26kmC6DENfvQfdkS21rtdKlNkZLi1l44vl0dLGP0WK/br3EZ7n9HhR0dFL/3Dbk3J2F3Qykq+qJoXRRhYmVSIAEIokApUsk7SbXQgIkoIYApYsaSqxDAiRAAv4hYC1drEeI7X6LSbh4XFRLl9psRLvQ4ZHlyL6l8/lsRJbBq8vKUZ3crC5l9N637sG8V4+Y6k++rbNNKun1cyZj1bZdbmUvcn5qxvnqmTLa4yeDDUmABEgguAQoXYLLn6OTAAkEngClS+CZc0QSCBgBfRWMNTrAqJc/hoANG94DaSRYSiw0MfFArDlFsj+LI+kSc3Eq4gdN825YN6QLJNXzpy9Mxop1IktapaHnsCssIqUcJd+tR9GeLhi1YgFGtrJMSeqv/fs9eG9bMeIkAG9PCcAbh2IcWp+PshsWYDTuwfJ3VFwvqpZTMXfIqRgMRLfbUyVzUmcMHzMQHVSsnNJFBSRWIQESIIFQJEDpEoq7wjmRAAn4kwCliz/psm8SCAIBgx7GylIYdacBQ00QJhBBQ2pjoEloBk1isknE+KPYSxclYG5C+jLPrhRZT9Ad6WJpV7InH6vy8rC7YBuqz8knG8mpl763YFB6Oob1aCVixaroi7H93UV47/0NKCkuFlkzED3vm4qxQzqj6FWVgXSlu7I9uZg3OxfHTH1IGuoVkoZaBWhKFxWQWIUESIAEQpEApUso7grnRAIk4E8ClC7+pMu+SSCwBEyy5exvMqgxsANH+mgaDTRJF4h8ae7zldpLF6+vFdXO0APp4vPF+bFDShc/wmXXJEACJOBPApQu/qTLvkmABEKRAKVLKO4K50QC7hMwnDkBKKdbWPxGQDnxomlce8fGN8NUrnkMRqv00InpS6FkLPK6ULp4jTAQHTCQbiAocwwSIIGQIkDpElLbwcmQAAkEgAClSwAgcwgS8DMB4+njMFad8fMo7F4hYLpu5AspYsGpe+9hGMqPmP5P2+wiJIxa5BPQRpEuxtPHfNJXKHaiSb4YmgDE3PH32ild/E2Y/ZMACYQcga+/3o1+/e8KuXlxQiRAAiTgLwLDhv4e6z5e4q/u2S8JkICfCRgrfoPx3Ck/j8LurQmYrho1auETKJWrxsr+nTRLl/a/Q8LgGT7pFzWVMJT94pu+QrAXbYuOfouzE8jlUrr4iLZRfwr6M1/CUFkIQ/UxCRx+FhqN1ke9s5uoJGCUe7qaRGjj24ptvxyxjftDE9c6KlH4etE//3wUHTsP8XW37I8ESIAEQpbAH++9Fa+/9mzIzo8TIwESaIBAhL+xDuW91zaXHDtKhiMvS+Xbf5TAx2WmXmIuG474ax/1skdzc6MEUTaeOuiTvkKuE4mxo23ZJeSm5cmEKF08oWbVxlBRgKrfclFTttbLnticBFwTiG02GHEtxyCmyfWuK7NGgwRaXJCK0lLeieZjQgIkEB0Enp39OP469YHoWCxXSQIRRkC5PqJcI2EJPAFfXTM6t0JOWNcoqYIgwuURES83+WwxxtKfJVt4tc/6C5WONHFJ0MhVrEgolC5e7KLu2ExUi3BhIYFAE4hreRcS2s6UkzD+SWsX6PUEY7w77nwMee+uD8bQHJMESIAEAk5g25dvITX1qoCPywFJgAS8I2DUV8FYesi7TtjaKwLalp3kZ+4Yr/rwq3SpKJGrS6VezS8UG2sap/glk1Qw1krp4gl1wzmcOzRerhN97klrtiEBnxDQNuqNRpcsEu/ig8jnPplReHWyOm897hz9WHhNmrMlARIgAQ8I9OnTHQXf5HnQkk1IgASCTcCcHrok2NOI6vE1TVrLVf+mXjHwq3SpETFXFmFiTsJ0mGQXNF5xD5XGlC4e7MS5g/eJcNnqQUs2IQHfEtAmXomkLu+YYr+wuE+gX//R+PrrPe43ZAsSIAESCCMCry2fjfv++IcwmjGnSgIkUEvAKCmijUwRHdQHQtOoOTRJKV7NwZ/SRZmY8WxxXcwYryYaIo19GcQ4FJZE6eLmLuiO/a9cKXrdzVasTgL+IxCbfCsSL37BfwNEcM8bN32FocPGRfAKuTQSIIFoJ3BL2o348IOF0Y6B6yeBsCVgSjNcbY4FwhIcApr4JtA0bePV4P6WLsrkDGWHJW6Mzqt5hkRjieWipNWOpELp4sZuKtmJzh28140WrEoCgSGQePGLiE0eGZjBImyU5+Ysxd+eeD7CVsXlkAAJkABwySUXQYnl0qaNd7+hJUsSIIHgETClA5bsRSzBI6CJbyzSpa1XEwiEdDHF/yk/Kvalxqu5BrVxbAK0TUW4aL2LoRPUNTgYnNLFjR05d/B+uVb0mRstWJUEAkNA26iXXDN6LzCDRdgo+nPf4amnXsEz87dE2Mq4HBIggWgmoAiX/A9fQY8el0YzBq6dBMKeAKVL8LcwXKSLiZShGsbTciUtDEWdKVtR09ZeBy0O/hNTfwaULip3xVB1GBU/DVRZm9VIIPAEGol0iRH5wqKeQFXJUlT/+gKMhgq8trYnHv/fg6iqiryUe+qJsCYJkEAkEBg5YiD+tfjvuOgiBlqPhP3kGqKbAKVL8Pc/rKSLBZex4jfJaHQq+PBUzkDTqKXEzWmpsnb4VaN0Ubln1afehu7IEyprsxoJBJ5AQuspiGv1cOAHDsMRDbofUXXin6gpt00ZfehYEv75xqVY+saPYbgqTpkESCDaCfTq1Q3ZE/+IrLGjoh0F108CEUOA0iX4WxmO0sVETa4ZGSvLYaw+G5qxXmLiocTLQWIzaLSxwd9oP86A0kUlXN2xv0sA3ddU1mY1Egg8gdjkNAmouyDwA4fZiNUn34DuhMRw0Zc6nLkSG+ds3P34aONhfPXVLvz000GU/FYKg8EQZivldEmABEKGgFG+f8hde5si6TAhP3B6WpTvSQkJ8ejUsT169bocgwf1x3XX9fG0O7YjARIIUQKULsHfmLCVLjbojPLvkJzmNuhDAKjy71+cXCOSj1FSKF1UbnTloUfkt+Ifq6zNaiQQeAIxSb9Do85vB37gMBnRoDuI6uIXUF36ocMZa+JaI+HCiYhtcU+YrIjTJAESCCcCVRv/F/ojBTZTjr/hCcRccm04LYNzJQESCDABSpcAA3cwXGRIl+BzjOYZULqo3P3KQw+KdNmksjarkUDgCcQk9Rbp8m7gBw6DEWtOrYbu1+fleOVxh7ONbX474lv9GdqES8JgNZwiCZBAOBLQH/oKVZ/Otpl6TJueiB/+TDguh3MmARIIEAFKlwCBbmAYSpfg70G4z4DSReUOUrqoBMVqQSNA6VIfvf70BlSdfAv605863BdFsiiyRZEuLCRAAiTgbwJVG56E/ugum2HirpPvQZcO9ffQ7J8ESCBMCaiWLsX5mJE5BZi0BTP7HcHyf07D1m37gVYD0eWebDxyc3ckSxbeioP5WLLgFezeI68ly2ujx+PB23sjxTpD77kj2LppNT7fug2HCnbBlGIguTfa/u4WpGVlILWVNcxdeHnYPSi4800svb8dtr81B8tX5aP6XCvE9RiK68dkI7NXMxv6FUc3I3/NFuzesRnHDh8xvRZ38VB0uClD5tLfdi6WliWFeVix5FXLvHujw22yprv7o3rjRMyYvwF9ny/EIz1sN7mkYCWWrFpjWYPMp8sA9Bx9PzIHdDaxUFsoXdSSYj1nBChdVD4blC4qQbFa0AhQupxHX1P2byjBr/VnnKeBVq4RKdeJlGtFLCRAAiQQCAL6g1tRtXmOzVDalp2RMGKu+X47CwmQQFQRMOrKgaqzMOrOSKDTSodrN5wtBrRx0DZr2zCbWunyyAJ0WPMcDvXKRIdkkSyFK7C74AiSMt/Fs8OLMO8huWo92O41ESbPPtgbSbUjHM7DpAmvAn0HoltHs2GpPrEduzduRnWjNIxaPBcj29RWrpUuczHq8AvIxzD07SJtyn5AwSazfOkyPR9PDDgvXorez8Lsd4AOqQPQVuYIlKOkYA2KCkXA9J+LmU+loa2VFDm2boqIlXwRP0PRM62PzFPqb8tDUaupePDa9VjyT3vpokPBa+Px8optIp3S0HPYFaa1VRStxm5FRDkYoyG4hrIjMBz6wqtnU3/467p4KnF9xyC2Z7pX/bFxeBGgdFG5X5QuKkGxWtAIULrIzytlayyy5Uun+6BNvMx8ukUC5rKQAAmQQKAJ6NZNg+H4bpth467JQuyVzDgU6L3geCQQSAJG3WkYfvkahuIfzX9Oypt/FUXCnyLmwu6I6/dgw7Ut0uUYesupj+Vy6iPBUl+H7YvvwZK8BCR3SUSXRxedf01/BGv/fg/e23YFhi1fhNEXWZpIrFXldEuc3WmQij2L8LfHc1BxWy5eeaQ/zKrYIl0atUOH8a/hiZvbWT4vLx2X0zf3TcGxy2bhiZfS0aV2BUos13onTUSULBmLl985gp7PbkB2X8v8i3Ix8eE5qBBR8qTImA617fTl2P7qQ1iSL6LmXLHNSZeKHXMwcXouUsa8iyfuNp/wqS0lO3Lw9PRFSHpkI569rZ2KHRAWXy+B4UShqrpqKsV2GYi46yepqco6EUKA0kXlRlK6qATFakEjEM3Spab0fbNsObu9Qf5xF2Qh/sIJ0MS0CNo+cWASIIHoJqA/sAVVW+bZQNA0udB02kXTiN+bovvp4OojkYBRTknUFH6ImqJPPErba5Yu3US6/KlhPLXSZcACzJ8+FKYDJLWlcBEeyM4B0kSWTKyVJeYXq0VQPCyCouesXcjuVytqnA1VjvWzUrFqi1ztWT8efU3VLNKlzVRk52ahp41M0WHrgt5Yni+nY96W0zGuvsWVbcDTd07EIeWqkpy8AUQYvTwUS97vjVErFmCkzbUmeVmk0arHh2C9+JDz14vK8ensVKwodDQfZb6WNXydjewPxqOnioeO0kUFJFZpkACli8oHhNJFJShWCxqBaJQuNaWrLbLl2wa5x7W8B3FynUjbyO6yb9B2iwOTAAlEM4Gqj5+A/sT3Nghie96JuL5/jGYsXDsJRByBmr1rUb19sfN1Sdp4TUIzaBLl6k1c3eUem/rGGh20zS9GbPe0hvlYpEuFnOCYb3+Cwzrey3A7cyGnVx6Q0yttlVgw1q/JSZKirzdg3ZbNKDpUjIqfLLFdTLMYaiVBLNLFgdBRahYs6S6nV6zrm5dRXbYf2z9fI3FjdqGk+DDKLLFdTC/2kytGs+SKEQqx5A+3Y3uvBXj26aFIcUDAdFXp5W1W0qVWAsm1osHma0X2paRgjlxlqj8nZ4Brvn8X+gPeXS8CFH1mLrGX34y4/g/7/Hn/ptTnXUZth01jgcub+G75lC4qWVK6qATFakEjEE3SpUZOtSgBcg3nvmuQN2VL0B5HDkwCJNAAAX3Rp6ja+k+bGhp5wxU/ci60yReTHQmQQAQQqP56qZxwWSvvtZW7NOaiSUxG7BW3QiOxnLQtOkGT1NLlSj0KpGsvVtyULhUH8zBvynQcKlOCz3ZB2+61sVcgsVfshYVVIF3T6RTbUl+66FCUPxPzFuRJfBgJaHtxH3T4XReLHPkFRStWoqROujTctzLSsXX2gXQtbVySVS9dfBFIt3LlXZJF85xpVnHXPoLYy25yOUM1FRYdAD6RsD8/SlggFt8TuEZOZw0S25fp5T/NlC4q94bSRSUoVgsagUiXLvozn6Pm9GemTESGqoMNcqZsCdpjyIFJgARUEtD9eyoMv+61qR0jv/2M98NvP1VOidVIgAR8RKD6m1dR88OHIlwM5h5jExHX8w6J3XSb3BWKd2uUgEsXfSGWZ92OrXHjMfbZbFxvdzCmvkRxU7r8JDFaJkhA8dsW4cmHBtplKrL0ZS9dnJyiUUA6lS51fbiF22HlUJQuRyXu8mP/oWzxfnfV9aDIl5ndgYsS1dW3r0XpopIbpYtKUKwWNAKRKF30Z75CzZlPRbR8BoNun0u2lC0uEbECCZBAiBDQ79uEqi8kxoJdSbj5OWglaCYLCZBAOBIwQv/fDaj68qW6yWuatkb8DU/IyZaOctRF6/aiAi5dLKdiqv8igWYlKK592b64uwTldXC9qC4Oi20Le0ljliTAyNcXYFRdBiRLG71Il5sl/XSdMCnG2ukD8N7uLIx9ayqub2Q/m9oYMw5iumxz1sbtLUCoSZfTNcDNkjNC+Whdrmnu/trYwjEBha396SHlutGqfp4Ro3RRyY3SRSUopdqJM8gcUgzx+8CEi3D6IVdBudzo265q2QkDElprUU86/qcUTTNPmWv7eQ6ez963LSNFuujP7jCfaBHZYqj8URUkyhZVmFiJBEggxAjoPposWUx+splVTKf/QfyAKSE2U06HBEhADQFjZRkqV4+TAK9VpurKFaL4YTPlWlFT+T+Nmi7q1Qm4dDklwWzvkmC2crpkgQTdtY6JUvbdIsyYItmLHMV0USldSjZOwd/mbJfAvZKhyDpwb202ond2WcV0kczTW2Zi0qyVSJKMSU8+1N/qZIxcU3p/Oublbpe01LbZi6olTs0kiVPj+DSNIK4uRxmaIdmcfsllCTXp8hdJgKdcKVJKE4k98nAn4F4vr7+4hBClFV6R61vKFa7aolwzmnqp+zAoXVQyo3RRCUqp5nfpYsCOlacwZ0U51nVrhX3zm6C1/fQoXdzYsOBX1Vd8a7k6JCdaKm2DSzY0O8qW4O8dZ0ACJOA5gZr/rke11W/Ea3uKH/wkYtpf43nHbEkCJBAUAtUFr6Nm92rT2JrE5ki45XmJ2+Io/Kv66QVcupgyBo2UjEFHJLh3Fvp2N98vqihajd1HbkFqrxxsz/f8pAvObUPOfVnYXdYObdMy0cGUZqkYh9bno2xYBrqsyMFum6tBtamkRca0kuC4w5TguOUSW2YNis7cgVEjfsB7izfYpIxWsh7tfisbOa9uBpIHosuQVKRYfkNbcXA99n4haamf3yKps9XtQyhJF+X0xegd5+e9rA+gXH1psFSflXgyOgkvVAWNXHkzWgX1VUcg8mpptJJmSyvWTa7+KTHVoHEuRRXBpYiu2qKcdnE3yC6li8pniNJFJSilmt+liw5zex7F08pYw5xIl0MVWLCp2jzpTo0x8QbRwBFewumki1F+m6Gv2Gn+c0ZEyzm5lKqyxCRdjZimQxDbbCi0CZ1VtmI1EiABEghNArr8x2H4zfb6pPaiPkgY+vfQnDBnRQIk4JiArhznVj8gaaEl2IaU+N9PREzXIV7TCrx0kSkrp07ezcF772+QzELyjrNVf3S4IQOZ9w5F2Rv22YjcjOmiEDm1CyuWLMLWLzbLKRUlWO8A9Bx9P8beWI7lw6yvF9Xi0+HQtpVY/tpqHCraLyKlNzoMu980n6QtynWlXUjN2YIH7W5mlh3ejPw387BdsjBVlCl9tUNS34FIve1+pP2uHZJt0ls736pQki7WJy9ubWuOM+KwSABn47lTMFaWn48t5PXTGKEdyLU/Jci1ppHYKydXAJX4OZ+WmNf/T8kzPsg+fbkLNJQuKp8dSheVoJRqoSBd3JhupFQNZelirDkF/bmdIld2mUSLQU62GA3mH0rUFEWuKJJFkS2KdGEhARIggUghoP/5C1R99ly95cT/z+OI6XxDpCyT6yCByCYgpwdMcZq+fFHWqTGlgE68M1d+k+79L/1US5fIJuxkdcqpnKFyKmcoMt+dgRt9mOLXesBQki5q3vwbdadhPCuyrDaQc1Q+Gx4sWr5etY3FpsQ3rtfYWnaNl+tcypUudwqli0palC4qQSnVKF3cgOW7qqEkXYw1xRa5UoCaiq9Fsux0e6Ga2BaItZxoUWQLCwmQAAlEKoHqrxai5qd1NsvTplyGhJHzInXJXBcJRByB6i9eRM2+DeJcYhDbbSTi+smpFx8UShcJwSJZt+McnEqpkNgtf5PYi77/LAAAIABJREFULRU3LsL8JwbCdFPJDyWUpMu4AuCbUvMiHV0tMp47CWPFST9QiJ4uNY1TTNcDrcs3Eip0nOXtjBKweFlf93hQuqjkFSzpUnbgHNZ9dQ6bP6vC9v+ew4+mY02x6HdtI/QfIddmbmmE1o6OxlnFNLn1uQ5YMSIGJ3aUY87/leODz6pxQuljWBLuuqs5/tTP8dm6yhNVWP/VWWz+XIdde89hxyFl7Bhc3i8RA69vjD/d0RiXK3HB7Isz6aI7h6dvOo65pjU0xYotKbjV6R1EIzbnHEbaUvkuK2M+uaIDprS2CtDrcN9aYNPu5jAFlVYrfkp0WJV3Gq9vEcb/UUKAK+sTLulNcd/wBMdspVbZvrP412un8fZWy550SMDAvokYfUMTDL8h3mk7lY+bR9WCKV2M1UdRc1aRK99ACYRr0P3XozWYnjCLaFGEiyJeWEiABEgg0gkYz/wK3cdPmH8zaVVie92DuN73RPryuT4SiAgCug8mwFAqPyzLb8vjB/4NMR08THNiRyPqpUu1xIAZORV7u8iVot596mKzVJ/Yjt0b5XpScjpGvTgLI+0zIfnwqQoX6WK6TlTxmw9XHr1daRpfaDqxVlsoXQL0LARFuhw6jbSRJZAQUM5LB5EXS0ReXGRXxUa6XIx/4BQG//WMyBb7EoOB2a2x+oEE2wxAIkimXXMcCxoaO6URnsxpjSlX2QUeakB4fJd7BNc/b47oft8Ll2DhYCep86wFTbeW2PpOMnpZ9+twXu5IFyO+W1mMzNln8bOzNV7VDKvnXoDhdmx//qjYCUtLRylNsfrdFAwPsC/wq3SRe6GGqkMShOuwCBX5Ix9r/658NOpNF2U9LozT4jE6NiQBEogQAno56VIlJ15sigT2S7hptqSQviJCVsllkEDkEjj32h9kcUb5o0HiPW9KmmHrvD+er9tQflSOelR43kG4t9TrcKxoCz7/eDO2f7cNZYePmFYUd/FQdLgpHZk3D0QHP10rqkWnSWgKTZN6aTvcIlu58i752fmcee7XPoLYy25yq31tZacnXeQZMT0rLD4joE2WVEWx5iy8lC4+w9pwR0GRLhJ5+2kJGDtXTlHcKidbUtvVCgo9vvvwDFbtU06BSLlKpMQKkRLWS7CWLlnJ0OWWYe/1zTC6Xyyaw4Cfd5zBv7bWJnePl5MkF9nJkxqseuwwxn0XJ6dakjCsc+1pGLu2KSIm3hUxYS0YGjplIiLpDhFJpkPU16dgzytNcYkD9JVfnUSrP5nfyA+feTFW3yZ3Yk/r8PrqSpSiBluel8xFyovdmmDKiHhZk+lbGP6QlWTur8GTLkbsWHwMg1/SWUaOxfC7mmCAhe+J/Wfxwfs6s4y5SkTOEjk9U/vvpvX85bTQrVnNkGpZe127lBbY+mlz2/0IwHPqrXQx1vxqEiomiSJ/9Ja/m2XLMd+tQJskcVn6ICaxB7SNrkRMox7yg0lH3/XPnkiABEggTAlUfTYb+p+/spm9tm0vJEjKWRYSIIHQJWCsOoPKNzNME1SCcSaOfs1nkzWeLZFgqJb7JD7rlR25Q0CTdIE5yKoXxd/SxVgmP7/X1L638WKibHqegGQ10jYz//ad0iVAD0ZwpIsRZXJ/LLmFoxRWkjZ58fE6cfCnFy/B/BusTo1Yp0wWRv0mXIS1D1mfZrETD1ltUPx4I5vTLpWnDEhs4fgkys9r5LTH/zOfnOk1pR223hd/ficaFB4GfDjjZ2S+r1SPxzPvtMPEbvabaMC6WT/jjreVzzfGso0XYrSNXFaRvaiBOVR+X4qRd5+CKduanGZZIadZ6p0UOnoWk6b8ig8kkvXCmS3rTrucWF+MrpMkV5uUP73SEfOvt9sbnR47CvS4/Np4v90rdfbIa+MuREyzERIzSye/Y6kyfRSLAqNRPhrldJF8NH3O6u/Kaxr5f9PnRcb5o2jiOyG28dUiWK6CNvEK+SiSRWP1vPhjUPZJAiRAAmFIwHCyCLp/P1GX/aR2CXF97kXsVaPDcEWcMglEBwFj+RFUvvewabGa5hcj8Q92p9a8wGCsknS/p334yy8v5hKtTTXJ7eXKuyXntIcQ/CldjJI5S7mmyuJ7AhqRLkpKaUoX37N12GNwpIuLxZ06i0cH/IrXlWoPXYTTE8zHn0zFWrrUXs+x7876CpHbpzOqsODOI5i2Vzod3Ap7Xmhy/sSKi3gqlTtOoce4UpOwaf1AG+zJtpU9OF2BSdedwL+U+d52IX6Z2dhOYHgjXaylT4JIn4scSB8LqKM1+Ll1LC6xCnlz4qNf0fWvZ00VprzWCU+6GUTJr4+r4n+UU61BLBptgkiVvqaTLNok+Zh4pXyj8u44ZhCXw6FJgARIIOAEanavRnWB6V/280Ubg4Th/5BrRs5ygwZ8mhyQBEjAioDh10IRpn81fUb5Ok24uX5GMm+AGU4elJ/xak+oe9MT27pLQBMTLyKtg7vN6tX3p3SJ+itoXu9OAx3I1TKtXC2jdPEnZKu+gytd5MTLgUoJqFuJ7d/q8GNpDfbuUILhWpVhrbBvfhPUvb21ki6ts9th3wOOThbosWrSIYxbr/RjFQ/FjmnlUQmou+OcjH0O350w4OBXlqs3dfXs2roMYivCJlOEjeQ7R0pz5H/cAgOtfFHZphK0f+y0vBiDKcsuxpP97E/6eCFdXAodFw/U3jJcf+dJfKdUk1Myy2a0wOhuTuLSBOjZrBsmgNJFE9sSWrkSpI2/RK4GdTBdD4pJ7ConWa4M9Ko5HgmQAAlEHAHdx/8PhhN7bNYV064v4of8b8StlQsigUggoD9SgKqN5q9P7UV9kTDUt1+rzEgTvKfEUSYbT2ZT+eY9ctjc/IvbuGuyEHvlKE+6Qb2YLs2NMJzaL1IuyL959Wg1od9II4GxNS06UroEaquCJV3KCsrw+Myy8/FbnC24AelSm73IUdMdLx3A4MXKK/WlS+WBM5g55xQW1MV+cTa4u9JFsv/UiRX7gLpWJ1EcCBnzDLyQLnK1qKtcLVKE1a3/uBgrbpFYMW4VAzY/fxxpuVZ3JiXmzn2Dm2L4CIl90y3GNiCxW317WdnH0kUTeyG0CZeYxUqcfJS/K3LF9P8xjtJWeTl/NicBEiABEjAR0B/dhaoNT9ajEXf1GMT2SCclEiCBECPgb+miLNdQeli+OTBmR0C3Xq4UaeVqkS9K5aqxUOSZUmIuG474ax/1qNt60qVZlTlrFovfCGhbdpI03TFMGe03wlYdB0O61MuSo6Ql7paAfj2UYLimb7/47qNSrFKu+HgqXRaLdHlJ6ctWnFTKSZmRmZa4J8rLKRJQ99JE9Lo2ru40zc87Tkow3vptVaVrts5OZB1Q1+qUTK/HJVZMlqMTOl5IFweptN1/fgz4UbhPmluGzab01+dL665NMHXWBfjTlUE4/SJZLjSaxvIrlgT5KEeH5CPkY+3frT8n9sQcV0X5aKonf9c2MZ9eMckV+SMBb1lIgARIgASCQ6D6m1dR870pANr5IsfcE26Sa0YplwVnUhyVBEjAIYFASBcl843RlJ2GJxoC8hjKz9Wm7DXyfdcXpTLvQYm7Yr6noLmgCxLT/ulRt/WkSxNmLfIIpBuNtHK97JvyeEoXN5h5XDXg0sU6XovTayxW14N8Kl2s4rWkJOHJ51Lw5371T3DscCJsVEkX2Ynz6aPPB9Q9HzMlCQvXtsZ9Dq9QeiFdvD7pYvUI6Y34+b8V2PzROby26TR21EnmBDz5VltMudJRAGSPH0GXDb3NXuRyAFYgARIgARIIGAElG0qVcs3o1EGbMbXtrkHCkPqnYAI2MQ5EAiRQj0AgpIsyqPJ9wXj6OHfA3wQU4dK0rdwD8t0vIHXvPwqDZBgySZc4iWeZYcoY4napJ10an4WBgZbd5uhOA0W+fXM6gdLFHWie1g20dCn77De0/3O5aboT/9URz1zr6A28n6SLVdySgU9djPw7HF/B8Va6wCr9svlUixavP3wYjyqnZxpIJ+3V9SJvY7o4fYDk9Mvq3zDy7+aMTrirNX6ZnhTQDEaULp5+dbMdCZAACYQmAf3Bz1G1eW69yXkTDyA0V8pZkUB4EwiUdDFRqpaTDUqmGgMD6/rjqVEC56JxK5MY8WXR5T8Ow2/76rqMv/H/IaZDf7eHoHRxG5nXDShdvEaovoNASxfrLDlPruiEKVc5mKt1wFpfnnRRcwVHJ6dN7j2Kp5WrTfbxYFwG0q1di138liWxeHFUCdbJy/e9cAkWDnZ2RacKc288gqeVqz32667t2ukc3MleVI0fm8fhctWSuwarHjuMcZscMFH/qHlck9LFY3RsSAIkQAIhS6D6iwWo2bfRZn7Km4H44c9Ae0HXkJ03J0YC0UQgoNJFAWs0wFhxEsbK0mjC7Pe1aho1hybpAhnH96fV7aWLtk1PyUr3jNtrCnXpUrCkO15+JxuPrB8PdUled+HlYfeg4M43sfTB3nU8jq2biBnzN6DtpC2YObyVW5zcn0PD3VO6uIXfu8qBli6VX51Eqz+VmSbdS9JBb5R00DbZ4Usq8PRfSzB3h968MF9KFzmBkjayBJst/e6RrEiXWOM7XY3X5xzHo+/XGna7QLrWqaiz2qD4cbuU0FZ92aSPTonBiRJlPcnI/6alTUYj292zOuEjqa43fdoc/ey3twHxUylXjEZKMN0dShu5urVi7gW49SK7Do6exaQpv0raanl9trxuuuZkxOacI/hXYgtMHd0YvVrYtanQ4ekxRzHXURpt7x4/Va0pXVRhYiUSIAESCCsCxsoyCar7FAwnJTuFVYm5OBXxg6aF1Vo4WRKIVAIBly61II16GHWS8VPivUBfBaO+OlIR+2Vdmhg5zR8j8Q+Va0TxSlxEdxNsqJ+WvXRRWnpy2iVapEvZnjys+rYcKVenY1SPZupBS01KF7dwhVblQEsXWAeaFRSXDEvGn3rEmKCUHqnEqrcr8PP1yZiYUIYFyskKX0oX1Jy/5iNdt+7XFPddH2cO3nuqCq+vOYMfRYxMHCZjr1Q+aZ/5yOokCiSzz0NNMWZMU0g3DopV/BjLq60faIM92c5FjUl+PH9QMggpDWIw8I6mGNZBTsWcjcXwCY1xufLpBk/bGLFj8TEJIFwbAV7a3dUEA9qZT9ac2Hsab39kScl9laxtiUgd5bSLdZwdZU+uaoo/DKkNLKzHdx+eqcsyNXzmxVh9m/++cTsiSekSWt8zOBsSIAES8BUB/fHdJvFif50g7ncPIPaKW301DPshARLwkEDQpIuj+TJ1sLpdlLgtgSyOpItGRE/CLTnQNLlQ9VSiRbqoBuKgIqWLN/SC3Dbg0kXWWy+DkDWDlCZYtkKOWsyX6yzr5QWfShfp7+gZjMssxiq77DzmKUig2BVtMfCrgw4zH5lPhBxG2lLLKRw0xrKNF2J0a8eb+PP7J9BjRoXlxfNBdRvacpvTKnUVreSPyytORny3shiZs8/iZ2cDySmY1XIKZnjdKRjnWYusu2h9Syts+ofd6aAAPL+ULgGAzCFIgARIIEgEavauRfX2xbajy29lEwbPgPaiPkGaFYclARJQCISUdOGWhCQBa+miiUuEsbrSNE9FuCgnXrQtO6uaN6WLa0yULq4ZhWyNYEgXBUbl0Qr865VyvL71HH4UAdK6ayMMv6kpJmbIiY6mfgqkW7sLyqmW3FLJzHPWnJlHUlbfem0T/Gl8MwxMgZwWcZxu2tS8Qto+f0pO4VTIvBuWLjYnSBoMoGv7eJQVlOPp3HJ88JlyKiUGl/drjmXLmqGXUs2ldLH0VaLDKjm588HGCnz4H+W6lNJPEu5Kl9M9wxPQ2ny4yLbo5VTLZxVY9dlZbCs4Z8laFIt+w5LwhxHNMGZwXEAD6NZOjtIlZL99cGIkQAIk4BMC1dteQc2P/7bpS9M4BQmDZkDTspNPxmAnJEAC7hOgdHGfWbS1sJYucb3uQfV3b9YhUE68xPbOQGz3W1xi8Ua6WMuIbgc3YPmrL6Bgm1xdbdQZKb/PxKgHM5BqEz7BEm+l31zMnJUGyedkW/YswgOP56Dv84V4pIf5JesxuhTlY/nCV7B7j2WMwQ9j7P1p6NbEupuGY7pY9620KinIxcsLV+DQ4SPyf+2Q1HcgUm/LRNrvOiPZ8r7Neg4dpH7O/FwcKy4GWg1El9vG48Hbe0OiWqgujOmiGpX3FYMlXbyfeRj0YJUtqeEAumGwliBOkdIliPA5NAmQAAkEgICxRoeqjf8Lw4nvbUZTAuomjJjj13gEAVgehyCBsCVA6RK2WxewiVtLl4S056E//DVqrMSLMhHl1Ets18HQSswuZydffCFdxj4PrHp8NeKG3IJurZuh+sR27N64GdWN0jBq8VyMbFOLxXPpUjtGclomOiRL2KGjW1Dw6TYJ3SkxWl6cVX8MJ4F0raXLsY1TMGNOPtAlHX37d0GcTLPi4Hrs/WIXes4V8WP6zft58TNqahHyJRxFt2FXIAnlKNmWh6IikS83LsL8Jwaq/iU5pUvAvkzkxMmhB1FTbkpLw+JTApLxZ5LlilRKc+R/3KKBALo+HTjiOqN0ibgt5YJIgARIoB4BQ8l/TeLFFDzTqmjbXyNXjZ4kMRIggSAQoHQJAvQwG9JeuiiyXH9oG6q/eAHGqtowC+cXpZx+0ciVI+Wj1uok45/KBuPbGnMMmH8124Srtb/IvwdnoG3/O8m81LJBKuYTIJIJKDkVI3PmYpRVIpGKg3mYlz0dh1KmYvK/stDNdBLEU+nieIyy7xZhxpQcVNicnFF70qUYa6cPwHuHpiI7Nws9rU+qVJejTJKfJCsWRkrdOvtn48mn0tGhtq7+CN57agjW7uiPYctyMfpidQ8RpYs6Tj6pReniE4zmTvadxdOf15gC857YU44F681ZkPpNaYdN98X7cKDo6orSJbr2m6slARKIXgL6fZtQ9UVOPQCxlw5F3HV/jl4wXDkJBIkApUuQwIfRsI6kizJ945lfUb1rJQyHtzmUL/ZLfOTCf6AgwXyX5+Vfp+Fq3X8kmqYGcdc+AkXkNFTMMgJO0zAXvT8es1/+Qa4LbbFcF/JUukhUiie248kb62cd2r3iduS8Blz/yrsY20WZrRvS5UmRLt9lYexbU3F9I+crNa+zO65/Sca4zLZe9Y45eHh6LjpM3YInh6hLRU3pEsAvNEoXH8L+TymaZp6y7dA6S5APh4qmrihdomm3uVYSIIFoJ1D9TS5qvn+vHoa4q+5EbJ8/Rjserp8EAkqA0iWguMNyMGfSpXYxxqqzUIS6cvrFeGq/UwHjvXQZilErFmCkI99wPB9/u28KKu5/Fwvu7n5eiLgd06WBMYpyMfHhOUipkx5qpYtcJZIYMn+TGDIVyUPRc0w60lL7o0OrBNM1I+tili4iZ9aKnLF/0RKHpu2kLZg5nNIl5L6YKF18uCXflyPt+XLs3aEEwFXSNTfHjOym6OUwpbQPx43wrihdInyDuTwSIAESsCNQtfHvkjXl23pc4v/nccR0voG8SIAEAkSA0iVAoMN4GFfSxX5phpMSfFZEjCJjDCcP1L38UPn560WLm1quF1WdQYzq60XZeGT9ePR1xFIvAuTme1BQF1/F05MuDYxRnI8ZmVOAOumhXrooU644uhnvvfEmtioxaJRPtEpD3wceRuYAx4F0662T0iW0v4ooXUJ7fzg7ybuU1FuCj79LFCRAAiRAAlFCwFh2GLoNT8F4VtIb2pXE2xZCk6zywnqU8OIyScBfBChd/EU2cvp1V7o4W7n3gXQzkPnuDNxok0HIMppFiBzLfBdLx3hz0qWBMQ7nYdK46UjyULrUcdHrUHJ0G/IXzsTWgiNIqjudY5tBidIlzL6GKF3CbMOicLqULlG46VwyCZBA1BPQ//wFqj57rh4HTXwSEu95K+r5EAAJBIIApUsgKIf3GKEjXSSbz9O7MLl/Qj2gZZ9Ox6TZeegyfTueGKDEYzmCVROGYD1m4YmX0mEKwWJVjv17Imb88xer+Czng9j2nLUB2f3qj+E0boyK7EWOnwDLHA9nI/uD8egplaxTRlO6hNnXDaVLmG1YFE6X0iUKN51LJgESIAEhoARhrPmuvmDRNrsICaMWkREJkICfCVC6+BlwBHQfOtLFcWah6qP5mP3wFBxKlqtBy+T6kSUWStE792D2kiPo8kQ+nrAOjHtOrgU9JFeRYJtNqC5zkKR1HvuPbFzf4vzmNZghyS6N87F1InTmb5CgvpIKWokbXF2ItZ9IAN4h3ZFsnblIvx8rskfiUysxROkSxl8wlC5hvHlRMnVKlyjZaC6TBEiABBwQqN75Bmr+s6reKzEXpyJ+0DQyIwES8CMBShc/wo2QrkNHumRj7PPAqsdXIzktEx2SBXDZThTkb0B1o/5IfWYRHuxhdUJF5MqSxydie5Fkmr4xHd0ukhMwlb9g78aVKCvrLVJkuUiR8/VrhYdpjGlrkDz4DtMY1Se2Y7cSh6VRGkYtnouRbWo3VtI4T5E0zj8NRLfb78HoMQPRQV6yly7VhbmYlD0HFY06I+X3w9CltXISpxwlBWtQVFgl2Yjy6rIRUbqE8RcNpUsYb16UTJ3SJUo2msskARIgAScEqr99DTV78uq9GtvrbsT1ziA3EiABPxGgdPET2AjqNpSkixJIt0tRPpYvfAW790jAXpPIyMSorAykOkrooy/G9ncXIf/jzTh2+IipfnLfWzB8TBaGdbS9QmQtPNSOUbEnF0/PzkVJcUZdkN96J12UZ+HMEWz9bAU+WbsZh4pk3miFuB5Dcf294zG6r/zd8rxQuoTxFw6lSxhvXpRMndIlSjaayyQBEiCBBghUf/OqpJJ+v754ufwmxPV/hOxIgAT8QIDSxQ9QI6zLUJMuDrMXRRhzXy1HK0HpvzmdgHE7zT1e0xxY5iZAjVGKryYUyf3ofnkM1aUfRvISubYwJxDT5Pdo1PH/wnwVnD4JkAAJkIC3BKp3LEFN4Zp63fCqkbdkbdsbTx+H8fQxGHWnYTQafNs5e/OKgEYTA01CU2gkrpGmyYVe9aWmMaWLGkrRXYfSJXz3n9IlgHunOzEP1cUvB3BEDkUC7hGIa3k3Ei76h3uNWJsESIAESCAiCVRvW4SaHz+qtzZtyqVIGDEHkDelLO4TMJ7cj5r/boT+8DaHqbrd75Et/E1AkS5aiW0Ue+lQaFt09MtwlC5+wRpRnVK6hO92UroEcO9qyjeg8tBDARyRQ5GAewQS281GbIu73GvE2iRAAiRAAhFLoOqrhdD/tK7e+jRJKSbxommcErFr9/XCjNWVqPlmGWoc8PT1WOzPfwRiuo1E/O8eALS+lY6ULv7bs0jp2W/SpUkFDOVHVWFqMNaJqh6is5K2eQd8Ux7P60UB2X5jDc780AswngvIcByEBNwl0Pjyr6CJa+1uM9YnARIgARKIYALVO/4lV43y669QozWJF23KZRG8et8szfBbEao+nw9j2S++6ZC9BJWAtmUnxA+YDI3EafBVoXTxFcnI7cdf0uXqpjr53nQ4csGFwMq0LTvjm1ItpUug9qLq+CxUlbwaqOE4DgmoJhDb/DYktpfcbCwkQAIkQAIkYEfAWXBdpVr8jdMQ0yGVzJwQMJ48AN2GJ2GsLCOjCCKgnPJKGPq0iJf2PlkVpYtPMEZ0J/6SLte0MEIRwyx+IqCNk2uJl+CbU6B08RPiet0aa0pw9qdBgOFMoIbkOCSgikDSpeugTbhUVV1WIgESIAESiD4C1QWvo2b3aocLj+v/MGIvvzn6oLhasb4KlWv+wt8iu+IUpq9rL+iKhDTf/MKK0iVMH4IATtt/0kUuYsj1ImN1RQBXEz1DaRKT5SpuK0qXQG959ak86I5MCfSwHI8EnBJIaDsDcRdkkRAJkAAJkAAJNEigetdK1Hz3lsM6sb3uRlzvDBK0IlC97RUJRvxvMolgArFX3Ia4393v9QopXbxGGPEd+FW6KBnUzpyIeIbBWKCmWTsJ39CI0iUY8Kt+XYCqX18IxtAckwRsCMRdcD8S2k4nFRIgARIgARJQRcBw/D/QrXP870bsZcMRd+2jqvqJ9EqGUweh+3BipC+T6xMCibcvhqZpW69YULp4hS8qGvtTuigAjaWHYJTTeSy+I6CJb1z3vYHXi3zH1a2eqksWQ3f8ObfasDIJ+JJA/IWPIv7CSb7skn2RAAmQAAlEAwGJT1K5JhvGipP1VhvT9irE9s6E9sLu0UDC6Rqrv16Kmh8+jGoG0bL42J53Iq7vH71aLqWLV/iiorG/pQuqz0oWo2NRwTJQi1RSRSM2wTQcpUugqDsYR3/mc1SdmAv9uT1BnAWHjjYC2oQuiG89GbHNhkfb0rleEiABEiABHxKo2iI/wxz4vH6PMfGI63MvYq+8zYejhVdXuncfguE038CE1655NluNBMlMvPVFzxpbWlG6eIUvKhr7XboIReO5kw5lelQA9vEilTguSjyX2kLp4mPAnnRXU7YGNaUfQpEwRiOPdXnCkG1cEJDUnjGNf4/Y5FsQ1+IO4iIBEiABEiABnxDQF36Iqh1LHfYV0+FaOQFwr09T6/pk0n7uRDkBVPnOWD+Pwu5DiUBixlsStyHJ4ylRuniMLmoaBkK6KDANFb8B5yTVDovHBDRJKdA0am7TntLFY5z+aWio3AtjzXG5U8cI0v4hHGW9ahOhjWsNTcLl0Ghio2zxXC4JkAAJkEAgCBgOfQXdp7MdDqVJamm6bhR76dBATCUkxjAU/wjdR0yaEBKbEaBJJMhJFyUtrKeF0sVTctHTzlfS5bH/AJ+WmLn9sycwqJUDhrpyGM5KJaMhegD7YqXaGJiES0LTer29cgBYJH+UMr4T8LD8cadojFLcacC6JEACJEACJEACJEACkUXA+Ns+VH2xAEoAWUdFkS6KfFEkTKQX/dFdqNrwZKQvk+uzIpAwYg60rbp5zITSxWN0UdPQV9LF+s1/poQcmXqpE4Sv3Pp1AAAgAElEQVSGGhiUEy+V5VKBb/cbftA0cpWomfnfN02Mw6p/2Q18Umx+6WkJefYHN2NvU7pEzZc6F0oCJEACJEACJEACDRCQALtVkiZZ//OXDitpJKigct1IuXYUyYXSJZJ31/HaKF2ib88DvWJfSZcfzwCjd5yf/bI+wDUtGliNnK8wSpBd1FQC+mo5/KKX0/OaQC8/9MaT0A34/+3dC3hU1b338d+eJCREIILBNARTMGpBRWisgpeiVi5aYxGreOGcluhRKW8rrR6ttqKn4qmK1op9S7G0gn2K1Lu+oBW01eAN1OaBYoXiiVIuRiQHGC4ht5l5156ZwCTkMpmZPdfvPg8PCHutvdZnbfscfq79Xy7zFUF2nqxefUzY0rmJHbbYoUvr9dTp0ldMk55chC490eJeBBBAAAEEEEAgzQWa3/+9ObnnxU5naRfYtQvtyhTcTceL0CUdV7XrORG6ZN6ax3vGsQpd7HGHfmLU1+QG9qcu9q4XrtgLLN4izfn4UL9d7i7q4vGELrFfG3pEAAEEEEAAAQRSWsDz6Uq1fPisvDuDH7G3m419pLT9uZF9xHS6XYQu6bai3c+H0KV7I+6ITiCWocveFukCsyFxn/k59Opyx0t0w8/I1nbx3NCrOE962uxysYOunl6ELj0V434EEEAAAQQQQCADBHzNB/zBS8u6Z01JAE+HM7ZrvWQdN052CJMuF6FLuqxk+PMgdAnfijsjE4hl6GKPwP7MaNZHgZ+5nBf4mjnMaPaJ0iATvERyEbpEokYbBBBAAAEEEEAgQwS8dRv9wYvHnHLU2ZU19Bxl2QFMGux8IXTJkBc7ZJqELpm35vGecaxDl9bx24V1Xzc1RwhfnFlRu3bLt0zR3H+L8vMtQhdn1odeEUAAAQQQQACBtBJo+fg1s/PlGfn2fNZ5+GKK7PrDl8FfS9m5E7qk7NJFPHBCl4jpaBimgFOhS+jj238OE+bQuK0TgVh+rkXowmuGAAIIIIAAAgggEJaAz5xw5P/k6B8vdHm/Hbr4w5cUPOmI0CWsVyGtbiJ0SavlTMrJxCN0ScqJMyi/AKELLwICCCCAAAIIIIBAjwQ8n68LFNrdVt11+GI+N/KHL+bzo1S5CF1SZaViN05Cl9hZ0lPHAoQumf1mELpk9vozewQQQAABBBBAIGKBln++7K/34ttvigp0cdmFdu2Cu3bh3WS/CF2SfYViPz5Cl9ib0mNbAUKXzH4jCF0ye/2ZPQIIIIAAAgggEJ1Aw261/M9f5fnkDXl3beqyL6tvsbJKTpVr0FeVdcxp0T3XodYJCV1qPlb+7A2dz6igj849pUhTThuiyafkq8ChuUfVradZ2+uzVNTXFVU3iWhM6JII9cx6JqFLZq13+9kSumT2+jN7BBBAAAEEEEAgNgI+n1pq7PDldXlr/95tn1afowMBjPmRdczp3d4frxuSMnQJmXzR8DLNv/ZETSyMl0g3z9m7S48+v1Fz//qFRk6foCVjcpNkYOEPg9AlfCvujEyA0CUyt3RpReiSLivJPBBAAAEEEEAAgSQR8G77wOx+eV2eTW+GNSIrv9AEMOXK+vKZJoQpD6uNUzclPHQ5ZYjuHZ7fdnp73Hr8nW3a4A7+dkGJltxVrkkDnFLoQb8hu3QmEbqYXVzlyh3/Xz0A5NZMECB0yYRV7nyOhC6Zvf7MHgEEEEAAAQQQcEzAW/exf/eL13x65GvaH9ZzrN79/Z8eZQ35ulzFI8NqE8ubEh66XHq26r/V//Apeer15KJ3Vflmvf/Pii48Q+uvKFReLCcfSV+ELvKYgtJNrwWCFkKXSF6i9G+T8NDF7ET0eZvNKTpe+cyvM+GyLMscG5QlZeUkfLqELglfAgaAAAIIIIAAAgikt4Bv3xf+z47s2i++vbVhT9bK7WdOPjpbrsITZPUfIlf/Lwf+n2gHr6QNXew5N+zSz37+lu7fbP/DED0/b4QmttsU4yBNx10TuhC6xP2lS70HJiJ08XlbpMa9UnO9fM0HUg8tZiM24UtOnqxeR8jKNRWx7DAmzhehS5zBeRwCCCCAAAIIIJCpAr6WBnlqzGdH2/4mrzl2WhH8RcAqGOwPX1wmhLGOLJXrSPNzv+KYkSZ16GJmuXb5Sp2xJPCd0V2zLtaPy9pN3RS0XbtmsxatqtXy93dpk/3HRf016aTBmja+RBOLu/qvvs3a8LdNeuS1rfrz+n3abkc7ZYU69wTT7owiTSo9VK9l+6oPNHR+FwFaBzt23FtrNX/5Jj3597rAp1JmXOeeMEBTRpXowlEFKnI2T+v0HaGmS8z+9aGjTgTiHbr46nfKd2An69FewHLJyh8gK+/IuNoQusSVm4chgAACCCCAAAII2AK+pn3yfrbWhC9/l8cU3vXt2RY5TFavQyFMcEeMHcoor+fn/CR76BIadrQPXRpqN+umh9dqkZ2WdHKNHvc1LbyqWEMOCzjM50u/fUuV7zR22rbonNFaU3m0//SknoYum1ZV67z52/xBTodXQamev2ekJvaN/DWItCWhS6RytAtXIG6hi9nd4t37uWQCbq4uBHLy5er7JbPrJT6nrRG68DYigAACCCCAAAIIJFzA+7//4/9Mw1trBzFmF0wMLiu7l9Srj/+HK9f8bd7/a7PF3P45t09gu7n5IfP/gNu/7zI/PDs3qfnth2Pw9B50EXpkdGc1XYLdbap6VycurPP/U5vQpW6bKmdX68lgsd2iwSW68qwCmb9WSO0K8RadWa7Xry8xHygduja9uVon/v6LwG8UFWvmuf0DbdWof35Qq0U19Sq6bKw+rQgEWe4as5tmY7O0o063m5OL7GvkmcfrymOCO2mKTR+jgt8+bd+syT9eq+X+u/I16cIhGtMv8KjPP6vVi2+aHTkFw/Tu3OMV/yo+5lX45hy5Bg7rwYK1vZWaLhHTZUzDuIQuJnDx7flMPk9TxrhGNdHsPLn6DYpL8ELoEtVK0RgBBBBAAAEEEEAg5gL2Z0j2J0gmhGnZ+jcpjtvk7SKT/gKM8bzCDl3cmjt7pW6vsQcXWtOlXose+otm+E/qztUVlWfq4XP6+HekHLzqTdvfrtbtawI7WdqeNNSoF3+7Qle9E+x3vqkV065Cb8POXVq1r4/OLW33eVIYNV22v1+tob8O7GS64aaL9MtT2v3X5eZGrd7YoGEnFbQdc5zWIHvkVSZwi7w4jte9TS0bX/GP1ho4XHnfvD9OI+cxqSIQj9DF697KDpeevhD2jhc7eHH4InRxGJjuEUAAAQQQQAABBKIT8O7aJPskJJ/9s/1j56eS+TzJiStpQxdPo5Y//74mL9vln3ZRhTm96LLg6UUm+Bg6e4P/050uTzXa+4V+dMdqPWrvhik9Ue/eXRbcWdKoJ3+9QpXv2z0fr9cXDdPocHHDCV1C6r/8+CcX664Twu08TveZz9MUq90Bpvhz7yv/GKeB85hUEXA6dPHV15kaLrtThSOpxmnlHyX71DwnL0IXJ3XpGwEEEEAAAQQQQMARAd9+85cME8B4TABj/+zbbcKY3VuiflbCQ5dThuje4e12XRyo1xurN2l5a0GUIrPL5Q6zGyVY/2TtshU64xl7B0uh5t1/hqYVdc6w9s+mEK//G6QC3Xv3WM0sDdwbWqB39LhyPTypRCPDqa8SRuiizTU6486PtNZ+UNkQLfzuMF3RfsdM1CsXRQeELlHg0TQcgcaX/tMExxv9t+Ze9KD/RLaYXSYw9O72H2nGFYmA2dno6j/U0c+MCF0iWRjaIIAAAggggAACCCSfgF1EMrgTpnVXjK+p3my5P2AK9wZ+VkvnhWLtCSU8dOlGtWh4meZfe6ImFrbeGLpLZZjZpXJ817tUNm5Q/s8/9je+4YcX6Jejgp8L7TW1WR5+V3P9ny4FriFlpZp07tG68qQijRzQScHJcEIXNeuNJ1frm38O7NLxX+bkomnlpf5TkSaaU5Hafc0U13fL1adI3sbId05ZPlNLI/heWaavvG8viOv4eVjyCzSumOWvV2VfuRNmy1Ucu+pF/gC6gV0u0bwFVn6h2e3i3IlGhC7RrA5tEUAAAQQQQAABBFJPwNSM8dnHVZsfB0OZ4D/b/zW6Zf3S+M4ptKZLR08uMLVUTinSlNOGaPIp+e3qnvQwdOkqJKnfpydfXKfblte1O2UoV8PGnKCF3xmike1Ln4QVutiTMsdRr/pYNy2p0RvBYr8H8xdT9Pe2a0fohqFdHWft3JJQSNc5W3oOCDRV3S/Pprf9v+517o+V9eWzYkbj3f0v83mcKWrNFbmAXVS3YHDk7btpSejiGC0dI4AAAggggAACCKSaQKKPjG5b4DYcvR6GLp3tdAl9VEO91v6jTn9aszlwslDrn5WZE4ZuMycMhWYjYYcuwU48Xm3atl1vvPuFHq/erNUHz5Dur7vuOlM/HhqfI1xDp0voEs57xj3RCDS/+2tTbDlwflfOGf9H2SdMjKa7Q229HrO7z9S44opKwC6ebg0oi6qPrhoTujhGS8cIIIAAAggggAACqSaQeqGLqccSg5ouna6T2f2y6I/vaMY7gc+y2nySZP9GT0OXNg8yu1+q1unChdsCO2u+MVq13zk67icYEbqk2r+lqTfelg+fU/PfFvkHnn3iJOWcdm1sJmF27flPLeKKWsB15JelLGd22xG6RL08dIAAAggggAACCCCQLgKpGLrYwUd0pxd1s3p123TVf1brRfu2S89W/bdCTvr41Dz7Z4GTk3q+S8fusF5P/uovqjQng0th1KRx4EUjdHEAlS7bCHi3f6jGV37i/z3XgGOVe/HDMRHyNe2Xb29tTPrK9E6sgmNkZec6wkDo4ggrnSKAAAIIIIAAAgikokBKhi4K/cQoV1dUnqmHz+nTdsdIvVtzf7tat68J7FhpE5A01+lnP9ukvMnHa/qoAhVktV25BvNJ0nmm+K5dBnTS9edryZkhhV12mkDmpkAgU2QCmU9DAxl/N1698UyVHu01TLedV3z4iUgNu/Szn7+l++3DV04t10c/KNGQOL84hC5xBs/Ex5kThg4svtyu1G1mbynv0kdl9f1S9BImdPESukTvaHpwmdBFhC4xsaQTBBBAAAEEEEAAAQQ6FUjN0MVMx+xGqZxdLf9p0HYAYorTXnlWgfx/rdvj1uPvbNOG1j87s1yvX38o3HD/ba2Kf9V65Gy+Rn+9WJcMCv4X3zZtjzZHUo9ueyS1CWxuv86cemQ/p6BQ0845Wl/pLe0uLNJdp/WRzF8IZ/zgAy0KivtPRPpan8C4TFi05u2tenJrIAiaeO35ev7r7Sv1Ov+yEro4b8wTzNv+/270n65mX9nDK5Rz+vXRsxC6RG8Y7IHQJWaUdIQAAggggAACCCCAQOcCKRu62FOqq9WPfvOBHg059rntTHN17sQRmjelWENCd7N0empRaGuzg2b62Vo4pn0o4jU1Zd7RGc+EHAdtNzv4GVLnpxaF9l7ULgiK5ztK6BJP7cx9Vsu6Z9Rc/YcAgNlR0fuKP0a/syIZQhePmU+73XFJsco9HBehS1KsGoNAAAEEEEAAAQQQSHeBlA5d7MUxR8euNacOLVpVq+XvB08eKuqvSScN1rSJJZpY1EWhSLdbL/59m5av2qk3/hHatlhXfsPsUBncWdtmrX5tg+57wzzT3rViH3F9cbleHldw6HXxNJpxbTcnIm3Tqo11wVOLzK6a047WJWOGatqp7T6HiuOLRugSR+xMfpQpetvwdKU5pn5/IHcxJxjZJxlFdSUwdHFXz9fd936kb8x7RBcNjGoWMW0c6bgIXWK6DHSGAAIIIIAAAggggEDHAgkJXViMhAoQuiSUP6Me3rJ2iZrXLDk455wzv6/s4ydEbpDA0KV2+Y2a9Qtp8uLkCl0iHRehS+SvIS0RQAABBBBAAAEEEAhbgNAlbKq0uZHQJW2WMvknYgrqNr58q7w7Pzk41qxjzzX1Xa6Tldu35+MndDnMjNCl568RLRBAAAEEEEAAAQQQiJsAoUvcqJPmQYQuSbMUGTEQ34Fdalz6Q9k/t1524JJ9yhRlD7vIHKOTHb5DD0KX6gXDNe/pmZqxYrpKqxdq3q8Xa3PxDzX7ngoVB59YV/2EFjy1VJur16hZA5VTNlYjplyjqWOPPXSq2Yfz9R83+Utnt7mKb16p2RPNd0Y7lmnW1Fsk+5/P2quXHpujZX+p0oj/Xq8ZJ5tD4j+r0rKlK7XuvSrVbtnm7yPnmPEqveBqXXfpGBV2VB/Gs0Orn5uvZa+0tilR/lmXqeK7lZowJFj0u7txdaPKTpfwXzvuRAABBBBAAAEEEEAgYgFCl4jpUrYhoUvKLl3KDty37ws1/XW2Oc3oX23mYB1RKFfxSLn6D/H/UN6RsvL6yerdv+O59jh0ma6Lbt6ql363V2XjRquw5KuaUjHKHC/fqOrHp2ve4lXSwAqNmHCi7JLZ9TXPaN0qsytnzAOafZcJZ+xAxIQmC179xPzZUvNnUuklF6v4CKnw1G9r8sn9DoUuNzyg4pfnaF1JhcrLBuq48ZU6b5BU80Kl7n3atBs9VsX+sk97VFe9VDXrTQAT+pzWGX9epQd/Ol0btpSoYNzFGlZknnGwzUCVP7TIhDkmeOluXIQuKfvvCwNHAAEEEEAAAQQQSCMBQpc0Wswwp0LoEiYUt8VcoOmdX8nz8asR92vJZ/7PUs7Ym+XqV9JlP4GdLuYWE2zcaQKU0pAdJfXvzdGNdyxU4Xef0+1XDj+0q8XcXvfeXN19x3zlz3hN911y6BmdfsYT3OlSq1Eqf+BRzRhphyQhV4enCpnQZ8E0M75tGnHfq5pZHty94lmvRddfqrfqKjRh7j2a0rqrxd9dowlwZmve5vG6/cZzVBh8BJ8XRfw60RABBBBAAAEEEEAAAecFCF2cN062JxC6JNuKZNZ4fHs/V8v6pWqxwxdzwlFProOhy9d/JP/nMV1cgdBluM7+v89p2gmhN+7R6/eO1uL1t2rmwkqNOOzznj1acc9oPfX+TM18cbpGdBdutIYuYx/RL+4Yb3bRhHm5X9Xdl9+ozZcv0e+uG+Vv1Fw9V9+7bb4OfroURleELmEgcQsCCCCAAAIIIIAAAokSIHRJlHzinkvokjh7ntxWwPvFR/Lu+Kf5sUG+fTvM8dL7JPMJka9xb4dUPd/pcrWmPjdL5/UJ7W6N5k24StVfMp8VnR/4rKj9VVc9x3z+M77NSUXd7XRxX/OcHjG7Zjq6mt2faPWbS/XmW2tUt2OL3MHaLv57TzefMgXrzKx7fKzmLh7VoxOSCF34twoBBBBAAAEEEEAAgSQWIHRJ4sVxaGiELg7B0q3zAj2u6RIopFveZmTB0KXb0fYsdPEX0rUL67a5zCdBy2brwUeeVXNvU5z3mK+q9LSyYNCzVTWLn1BdSOgSWvy37Zg7HyyhS7cLyQ0IIIAAAggggAACCCROgNAlcfaJejKhS6LkeW7UArEMXULCjnDG1d1Olw5Dl40LdeP350iXzNedN5g6LG0+ZQqGP+x0CYefexBAAAEEEEAAAQQQSE0BQpfUXLdoRk3oEo0ebRMqEJPQJVjTZVWlpv3pVp3dO7wZRRK6tLa56A+PaPKX2j3HY0KXC81nTiGhS7c1XTooystOl/DWj7sQQAABBBBAAAEEEEiIgHf7P9T4yu0JeTYPTYxA3sW/lDWgLOKHe7ZVq+m1//K3dw0qV+74wK+5EHBcICahiylY++F83XzT3E52oJhZNO+RW/1UkHNoRoFwY68m/H6hpoTW8A0W0u1op0vda7fotjmrNeIec0LR6cETiuwuPXu0+rEbtODpNW1qusizTU/dNE4rNnVwepHd5o8zteizaZp9e/vTizoYVzeL4S9EnB0yphgunuUzVwz7oysEEEAAAQQQQAABBFJWwLe3Vg3P3ZCy42fgPRfIm/K4rN79e94w2ILQJWI6GkYrEKPQxT5+ed2fZmruY1VSwTkqGzdahXmBwdVvWqENb5ujnB9aqRknHxpw6y4UnVyp0SNN7ZbjJui6s8yR0l2ELjqwSnO/U6l17hIVV0xVqf9oox3avGKZ3BOuVtniuVrX/jOnz6v04E+na8OWgcoZU6HyMrtOTKBN7Y4SlT+0yIzrUFjS6bgIXaJ922iPAAIIIIAAAggggEBsBBqWXGVODdkfm87oJbkFeg9Q7ymLohojoUtUfDSORiBmoUtgEO4tVVq25Fmtfv9V1bvt3ylRfvk5Gn3JNao4rUQFbWqw7FH14tla8NQyNR8wgcg1j+o39mlFXYUudpe71mjxgvl66+2qQLuysRox5RpNO2+PFtmnKHVUW8azQ6ufm69lr1Sp1j7pyC7CW36ZpswwgU/7Wr3qZFyELtG8abRFAAEEEEAAAQQQQCB2Ak0rH5Dn0zdj1yE9Ja1A1nHj1OusG6MaH6FLVHw0jkagB6FLNI/JhLZ8XpQJq8wcEUAAAQQQQAABBJJCwLPlPTX99Z6kGAuDcFYgd8JsuYpHRvUQQpeo+GgcjQChSzR6bdoSusSMko4QQAABBBBAAAEEEOheoGnFHfLU/r37G7kjZQWyBn9Nvc6/M+rxE7pETUgHEQrYn0Hadai4ohcgdInekB4QQAABBBBAAAEEEAhbwFv3sRpfujns+7kxxQSsLOV+62G5jvxy1AMndImakA4iFWhpkNe9NdLWtAsRcPU3/1vgCjmeKYY6nF4UQ0y6QgABBBBAAAEEEEgfgZaPX1XzO79Knwkxk4MCOV+/SdnHnhsTEUKXmDDSSSQC3hZ5d/3LtORA4kj4Qtu47GPjLSvabjpsT+jiCCudIoAAAggggAACCKSDQMuGl9W8en46TIU5BAXswrl2Ad1YXYQusZKkn0gE/KGLtzmSprRpFcjOk6tgsGMehC6O0dIxAggggAACCCCAQDoIeLZ+oOb3fyffns/SYToZOwfXkaXKOf26qAvntgckdMnYVyopJu7bv0O+Bv/5zlwRClj5R8nq3T/C1t03I3Tp3og7EEAAAQQQQAABBBBQy4fPqWXjcgpXpti7YJn/gp19/ARln3SJIyMndHGElU7DFfA0ybt7i7mbT4zCJWt7nyXXgCHm06KsyJqH0YrQJQwkbkEAAQQQQAABBBBAoFXAW7dR3h0bTfjymfkvzHuASUIBK69AVr9Bcg38ilxHHefoCAldHOWl8zAEfPvrzP8W7Q7jTm5pL2DlDzC7XAY4CkPo4igvnSOAAAIIIIAAAggggEA6CxC6pPPqpsrcfPK5t8lnTjPiCl/Aysn3h7NOX4QuTgvTPwIIIIAAAggggAACCKStAKFL2i5tak3M/sxoTy1FdcNdtaxepnhuiaOfFbUOhdAl3EXhPgQQQAABBBBAAAEEEECgnQChC69E0gjYwcu+7VJLY9IMKSkHktNbrj5Fkis7LsMjdIkLMw9BAAEEEEAAAQQQQACBdBQgdEnHVU3tOfnqTY2XA9R46WgV7fotdh2XeF6ELvHU5lkIIIAAAggggAACCCCQVgKELmm1nOkzGU+zfE175WvcJ5kdMBl9ZeXK6nWErLx+cdvdEupN6JLRbx+TRwABBBBAAAEEEEAAgWgECF2i0aNtXAR8Xn+tF5/XIysuD0z8Q+wDtC37GOjsXvavEjogQpeE8vNwBBBAAAEEEEAAAQQQSGUBQpdUXj3GjoDzAoQuzhvzBAQQQAABBBBAAAEEEEhTAUKXNF1YpoVAjAQIXWIESTcIIIAAAggggAACCCCQeQKELpm35swYgZ4IELr0RIt7EUAAAQQQQAABBBBAAIEQAUIXXgcEEOhKgNCF9wMBBBBAAAEEEEAAAQQQiFCA0CVCOJohkCEChC4ZstBMEwEEEEAAAQQQQAABBGIvQOgSe1N6RCCdBAhd0mk1mQsCCCCAAAIIIIAAAgjEVYDQJa7cPAyBlBMgdEm5JWPACCCAAAIIIIAAAgggkCwChC7JshKMA4HkFCB0Sc51YVQIIIAAAggggAACCCCQAgIJDV08zfK1HJBamiSv+bXPl7RilmVJlkty5cjK6S3ZP7gQyAABQpcMWGSmiAACCCCAAAIIIIAAAs4IJCJ08TXtl69ht9RsApdUvVxZsvIKZPXub2ZgAhkuBNJUgNAlTReWaSGAAAIIIIAAAggggIDzAnENXcxOFu/+L6TGvc5PLF5PcGXLOmKgrF5HxOuJPAeBuAoQusSVm4chgAACCCCAAAIIIIBAOgnEK3Tx2Z8S7a2VPOZTojS8rCMKzc6XI9NwZkwp0wUIXTL9DWD+CCCAAAIIIIAAAgggELFAXEIXn1de99a0DVxa8a0jjjbBS7+I14KGCCSjAKFLMq4KY0IAAQQQQAABBBBAAIGUEIhH6OLb+7l8TftSwiPaQboKjpGyc6PthvYIJI0AoUvSLAUDQQABBBBAAAEEEEAAgVQTcDp08Zn6Lb5921ONJeLxWjn5svoNirg9DRFINgFCl2RbEcaDAAIIIIAAAggggAACKSPg3f6hGl/5iX+8rqKTlXvBz2M6dq97izkSujGmfSZ7Zy47dDHhCxcC6SBA6JIOq8gcEEAAAQQQQAABBBBAICEC3p2fqnHpzEDoMmCoci+eG7txtDQEarlk2GXl9pXVpyjDZs1001WA0CVdV5Z5IYAAAggggAACCCCAgOMCvv071PDMtf7n2Ecf5132+5g901e/U74DO2PWX8p05MqSq//QlBkuA0WgKwFCF94PBBBAAAEEEEAAAQQQQCBSgeYDOvDEFYHWlqXeU5+WsnpF2lubdl5TQFcZUkC3PZir/xCzdSg7Jo50gkAiBQhdEqnPsxFAAAEEEEAAAQQQQCDlBRr+NFV2wVv7yr3gXlPb5aSYzMn/aZH5xCgTL8ucYmRxilEmLn3azZnQJe2WlAkhgAACCCCAAAIIIIBAPAWaqu6XZ9Pb/kfmnDpN2SdfGpPHZ3boMtiELnkxcaQTBBIpQOiSSH2ejQACCCCAAAIIIIAAAh0qkLwAAA0FSURBVCkv0LJ+mZrf+61/HvZnMbnfeiQmcyJ0IXSJyYtEJwkVIHRJKD8PRwABBBBAAAEEEEAAgVQX8B3YrYanKyWfxz+V3PF3yzVoVNTTSovQZccyzZp6i3TzSs2eODBsE6uAnS5hY3FjUgsQuiT18jA4BBBAAAEEEEAAAQQQSAWBpjd/Ic8nVf6h2oFBnr3bJcpCsIQu7HRJhXefMXYtQOjCG4IAAggggAACCCCAAAIIRCng3fmJGpf+8GAv2SdOUs5pgaOkI70IXQhdIn13aJc8AoQuybMWjAQBBBBAAAEEEEAAAQRSWKD53Xlq2fjKwRnklP+7skdcHvGMCF0IXSJ+eWiYNAKELkmzFAwEAQQQQAABBBBAAAEEUl2gcdmP5P3fmoPTcB1Vppwx35Or8IQeTy3s0CW0bspZe/XSY3O07C9VGvHf6zXj5OBj932iFUse0/I3Vsq9Y4dUMErFp12sisqrNbpdqZX6z6q0bOlKrXuvSrVbtvk7yDlmvEovuFrXXTpGhVmHT6V5xyo9tXCR3nq7Ss0HSpR/1lRNnVGp0aKmS48XngZpJUDoklbLyWQQQAABBBBAAAEEEEAgkQK+Brea/jJb3rqNbYaRVXqGsk+aJNdRx0lZvcIaYo9DlxseUPHLc7SupELlZQN13PhKnTfIPOrzV3X3D27UZneJCsZdrGFF/aSGrdrw2hNyu0ep/KFFJpzJPTimmhcqde/TUunosSousH97j+qql6pmvQlgxjyg2XdVqDgkeKn/cL5m/XSu3DpWxedfplLTpvmzlapeVajJd5+jVbdQSDesBeemtBQgdEnLZWVSCCCAAAIIIIAAAgggkDABT5OaVprCupvf7XAIVr9BcvX9kjnmqJ+sPBOA5BzR4X2+xj1y9T5SrpJTu55KcKdLrUyA8sCjmjHS9Nl6ebbpqZvGacWuqzX5vlm6yA5hWq8Dn+ipe6dpxdoKTfvTrTq7d/AP7EOYDtvN0qjqBdM07+ltGnHfq5pZHgxpmtdo3rVXqdpdoYt+84Amh/TvXmvCmDufUP2BHSrm9KKEvY48OLEChC6J9efpCCCAAAIIIIAAAgggkKYCdn2Xln+8KN+ewCc6Pb18dvZx9HDlnH5d101bQ5exj+gXd4yXf3NK61WzUDd+b44Kb1+tO88LCWNa/3yj+fPvmz+/daXuHNfNkc5us2PmcrNj5vIl+t11gSOx3Stn6+Z7nlBph/3bQc1VJqhZT+jS08Xn/rQRIHRJm6VkIggggAACCCCAAAIIIJCMAp6t78vzr3fl3bFBPvfWsIcYCF2GmdDl+rBCF/c1z+mRK4e3ubd2+Y2a9YtXVTjuVpUVddDN/o+07oVlqg8JUuy7mt2faPWbS/XmW2tUt2OL3MHaLv4eTjefGN1jPjEyv6xeMNyEKhWa/OQDuqh/B/2vnav/uGU+oUvYq86N6SZA6JJuK8p8EEAAAQQQQAABBBBAIGkFfE318u2skf3pkK9pv9S4T76Wxg7HG/i8qL/5vKg8rNBFHXzC0xq6dAtyMHRpVM2y2XrwkWfV3PtYFRzzVZWeVqZ8fwdbVbP4CdUdFrrM1IwV09XhKEOL/E7sZidNyCCtgsGysjm9qNt144akFyB0SfolYoAIIIAAAggggAACCCCQiQI9LqTbRehS/lDISUZdYQY/N9Il83XnDee0O6nI1G+ZYOq3HBa6VGraS6YmTE4HHQc/byqgpksmvsLM2QgQuvAaIIAAAggggAACCCCAAAJJKBCL0EXB0CN/xmu675KSbmcZ2BkjXfSHRzTZ1Pptc3lM6HJh29AlcP8ajbjHFNc9/dAJSK3tal6YrnvnVfF5Ubfy3JCuAoQu6bqyzAsBBBBAAAEEEEAAAQRSWiAmoYt26KU7xur5dRWa8NAsTSk7vJiu292ogoJAYFL32i26bc7qw0MUzx6tfuwGLXh6TZuaLnJX6d7vTFdN4XRNe3Cmzg6p61Jvds3cfctC1XF6UUq/hww+OgFCl+j8aI0AAggggAACCCCAAAIIOCIQm9DFDO3zKj340+nasGWgcsZUqLwsWFulwdRoWfmq6obeerAwrg6s0tzvVGqdu0TFFVNV6j8KaYc2r1gm94SrVbZ4rtaFfF5k/2n9h/N1201zVW9qwBSff5m/TfP21Vr3dl9V3H2OVt1yizqqN9MVGjVdHHml6DQBAoQuCUDnkQgggAACCCCAAAIIIIBAdwIxC13sB3l2qPq1Z7XshaXaXPNJ4NEDx6j03G+r4hITxITWuN21RosXzNdbb1ep+YAJasrGasSUazTtvD1a1K6mS+sc3DWvavEfHzNByxo1q0T5Z03V1BmVGq1lmjWV0KW7tebP01eA0CV915aZIYAAAggggAACCCCAQAoLhB26pPAcOxs6O13ScFEzdEqELhm68EwbAQQQQAABBBBAAAEEkluA0IUjo5P7DWV04QgQuoSjxD0IIIAAAggggAACCCCAQJwFCF0IXeL8yvE4BwQIXRxApUsEEEAAAQQQQAABBBBAIFqBzA5djpGVffgR1NGa0h6BeAsQusRbnOchgAACCCCAAAIIIIAAAmEI+PZ+Ll/TvjDuTL9bXP2HSq6s9JsYM8o4AUKXjFtyJowAAggggAACCCCAAAKpIOA7sFO++p2pMNTYjtHKkmuACV24EEgDAUKXNFhEpoAAAggggAACCCCAAAJpKNDSIP8nRhl2Wbl9ZfUpyrBZM910FSB0SdeVZV4IIIAAAggggAACCCCQ8gI+92b5WppSfh49mYCr3yApJ78nTbgXgaQVIHRJ2qVhYAgggAACCCCAAAIIIJDpAr6GPfLt/yJzGLLz5CoYnDnzZaZpL0DokvZLzAQRQAABBBBAAAEEEEAglQV8e2tNQd39qTyFsMfuD1xM8MKFQLoIELqky0oyDwQQQAABBBBAAAEEEEhPAa/H1HbZInlb0nN+wVlZRxTKyjsyrefI5DJPgNAl89acGSOAAAIIIIAAAggggECqCXia5N3zWdoGL1b+UbJ690+1VWG8CHQrQOjSLRE3IIAAAggggAACCCCAAAJJIGDveNm3XWquT4LBxGgIlkuuPgOlXn1j1CHdIJBcAoQuybUejAYBBBBAAAEEEEAAAQQQ6FLA17hXvobdUktj6kpZlpTbT678AZKVlbrzYOQIdCNA6MIrggACCCCAAAIIIIAAAgikoICvpcHsejkgmU+P/PVefL6knYV/ZCZcsbJzpKw8WblH2L+RtONlYAjESoDQJVaS9IMAAggggAACCCCAAAIIIIAAAgiECBC68DoggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAAKGLA6h0iQACCCCAAAIIIIAAAggggAACCBC68A4ggAACCCCAAAIIIIAAAggggAACDggQujiASpcIIIAAAggggAACCCCAAAIIIIAAoQvvAAIIIIAAAggggAACCCCAAAIIIOCAwP8HYtJKFoKJZ/sAAAAASUVORK5CYII=" width="320" /></p>Now we have solved the question of "where to put the comments section, but does it mean that we'll have to duplicate for each of the public facing components the "this is how the admin interacts with it, this is the user's view"? It's not that one view is more "correct" than the other, but rather that one will be easier for us and will match the way we think of our product. <p></p><p style="text-align: left;">Please note, this was the easy example. How would we represent here global requirements such as "always keep the logged in user session until they log out"? or "all pages must conform to WCAG level 2 (AA)" ?</p><p style="text-align: left;">So, it's a challenge to build this, but that's ok - we don't have to get it perfect, just good enough, and we'll roll with the punches. Let's consider the next challenge: Do we version this tree? how do we compare two versions? Do we just keep the latest version? do we freeze it every release and save a copy? can we compare two versions in any meaningful way? </p><p style="text-align: left;">And, since we are talking about managing the requirements tree - who is maintaining it? Is the person adding requirements to the product doing so? Each time I've heard this idea it was a meant to be a testers only effort, and requirements for new features are not written using this sort of structure. In my previous work place we've tried to maintain such a tree, and each time we'd start a new feature one of the testers had to go and translate the requirements one by one to the proper place on the tree, sometimes breaking a single requirement to multiple entries in the tree. This tree, of course, was unofficial.<br /></p><p style="text-align: left;">The next part, tracing tests to requirements, is rather easy - IF you are using a heavy weight test management tool such as MicroFocus's QualityCenter of SmartBear's QAComplete that allows you to link requirements to specific tests (There are probably other products as well, I don't keep track). If you do use such a product, you probably already feel the cost of using it. You'll still have to wonder how to represent "we cover the requirements to support mobile devices and 30 languages", but let's assume it's not that terrible to do. If you do the sensible thing and don't bother with maintaining a repository of test-cases that go stale even before they are written - congratulations, if you want this traceability to happen you now have the extra burden of manually maintaining that link as well over feature changes, refactoring, and what's not.<br /></p><p style="text-align: left;">I hope that by this point I managed to convince you that it's expensive to keep this tree up to date, but even so, it might still be worth the price. <br /></p><p style="text-align: left;">So, let's look at the promised return on investment, shall we?</p><p style="text-align: left;">Let's assume we have all of the product requirements laid out in front of us in a neat tree without too many compromises. We now add a new feature, say, we enable a\b experiments for posts. It touches the post editing, post viewing, analytics, content caching, users and comments, and it might have some performance impact. We've learned all of this in a single glance on our requirements tree and so we also know which tests should be updated and which requirements (new or old) are not covered. We invest some time and craft the tests that are covering our gaps, not wasting time re-writing existing tests simply because we didn't notice they are there.</p><p style="text-align: left;">How much time have we saved? Let's be generous and assume that once we added the new requirements to our tree, we know exactly which requirements are affected, and which are not covered at all. Now we go, think of appropriate tests, add them to our tree, update our automation suites at the appropriate levels, run the attended tests we want and call this feature done. Compare this to working without our marvelous tree: We spend some time analyzing the feature and requirements, then dig around our test repository to see what we have and is relevant, then we do exactly the same steps of planning, updating and executing tests. We saved ourselves the need to dig around and see what we have and is relevant, which could amount to some significant amount of time over multiple features, right? Well, if we replace testers each feature - that's the case. But assuming we are working on the same product for a while, we get to know it over time. We don't need to dig through hundreds of lines, we can ask the person who's been here for a couple of years and be done with it in 10 minutes. Those 10 minutes we "lose" each time are small change compared to the increased analysis time (since we're updating a model, effectively duplicating the requirements as part of the analysis). So, even in theory when all goes well we don't really save time using this method. </p><p style="text-align: left;">Perhaps the gain is in completeness and not in time? There is a lot of value in knowing that we are investing our effort in the right places, and it is far less likely that we'll miss a requirement using this method. In addition, we'll sure be able to provide an exact coverage report at any time. We could even refer to this coverage report in our quarterly go\no-go meeting when we decide to delay the upcoming version. What's that now? We don't do those things anymore? Fast cycles and short sprints? Small changes and fast feedback from customers? The world we live in today is one where most of our safety nets are automatic and running often, so we can see where they break, and since we're doing small chunks of work that are easier to analyze we can be quite confident in how well we did our job. What we miss is caught by monitoring in production and fixed quickly. So, perhaps this value, too, is less important. <br /></p><p style="text-align: left;">And what if we don't work in a snazzy agile shop and we do release a version once in a never? In this case we are probably trying to get to faster release cadence which, in turn, means that adopting a method that drives the wrong behavior is not something we desire and we want to minimize the effort we put into temporary solutions. <br /></p><p style="text-align: left;">There is, however, one case where such traceability might be worth pouring our resources into - in a heavily regulated life critical products. From avionics to medical devices. In such cases we might actually be required to present such traceability matrix to an auditor, and even if we won't, failing and fixing later is a bad option for us (or, as the saying goes: if at first you don't succeed - skydiving is not for you). Such heavy regulatory requirements will affect the way the entire organization is operating, and this will remove some of the obstacles to maintaining such a tree. Whether we'll use it for purposes other than compliance? I don't know. I also don't know whether there are alternatives to fulfilling the regulatory needs with a lighter process. At any rate, when we endeavor to perform such a herculean task, it is important that we know why we are doing it, what we are trying to achieve and whether the cost is justifiable. <br /></p><p style="text-align: left;"><br /></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-26519801094821507872024-01-01T09:16:00.002+02:002024-01-01T09:16:44.855+02:00תרמית הנעקבות<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBQNkH_UCNxjaiaXhEQqt8xXm4_H8astNn9VYP1z0pPT5UP25q0KPAKBRiYlNkfnkwsifQoQVRWF8xPAFKvBEu1PUi1pwJuD5A9Zvt7oVB47_7ZJAfwDSDT3XNVxYJNTRrFmTmPjnt2Uribt1lD_i-sJl2qr4Nx-RaXzqgyhhPsxMoQ4PfCfhjWt3Llqg/s1024/vault.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1024" data-original-width="1024" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBQNkH_UCNxjaiaXhEQqt8xXm4_H8astNn9VYP1z0pPT5UP25q0KPAKBRiYlNkfnkwsifQoQVRWF8xPAFKvBEu1PUi1pwJuD5A9Zvt7oVB47_7ZJAfwDSDT3XNVxYJNTRrFmTmPjnt2Uribt1lD_i-sJl2qr4Nx-RaXzqgyhhPsxMoQ4PfCfhjWt3Llqg/s320/vault.jpg" width="320" /></a></div><br /><p></p><p> <a href="https://always-fearful.blogspot.com/2024/01/the-tracability-fraud.html" target="_blank">English</a></p><p> </p><p dir="rtl" style="text-align: right;">אם אתם מסתובבים מספיק זמן בתחום הבדיקות, חמש-שש דקות לפחות, סביר שתשמעו מישהו שיזכיר את המטרה העליונה, הדבר האחד שכל בודק תוכנה צריך לשאוף אליו - נעקבות מלאה. מה זה "נעקבות"? האמת, זה אכן לא מונח שגור בשום מקום, ובדרך כלל הפעם היחידה בה תשמעו את הגרסה העברית של השקר הזה תהיה בקורס שמנסה לעזור לכם לעבור את המבחן המיותר של ISTQB CTFL. בדרך כלל, קוראים לזה בלע"ז traceability והמשמעות של זה היא שלכל דרישה אפשר לגזור בדיוק מה הן הבדיקות שמבוצעות כדי לאתגר אותה. גם אם לא שמעתם את המונח עצמו, מישהו אולי ביקש מכם "מיפוי" של הבדיקות לפיצ'רים, או רצה לדעת אילו אזורים מכוסים ואז נסחף קצת. תסתובבו מספיק, תשמעו בקשות כאלה. אולי זה עניין מקרי, אבל מהמעט שיצא לי לראות, יש מתאם הפוך בין מידת הניסיון שיש למישהו בבדיקות תוכנה לבין הסיכוי שלו להשמיע את המושג הזה. אלא אם, כמובן, המישהו הזה הוא מדריך בקורס ואז הוא חייב לפחות להזכיר את הרעיון. במבט ראשון, הרעיון הזה נשמע מדהים - אנשי הבדיקות מפעילים את הקסמים שלהם, ואז יש לנו רשימה שאומרת לנו אילו בדיקות קשורות לאיזו דרישה, וכאשר יש תיקון, או פיצ'ר חדש, כל מה שאנחנו צריכים לעשות הוא ללכת לרשימה הזו ולהריץ את הבדיקות הרלוונטיות. מדהים! מה שלא מדברים עליו בהקשר הזה הוא העלות של יצירת ותחזוקת עץ כזה אל מול הערך הצפוי, שבעולם של פיתוח תוכנה מהיר במעגלים קצרים הולך ומצטמק (ולא שהוא היה כזה גבוה מלכתחילה). </p><p dir="rtl" style="text-align: right;">הבעיה הראשונה היא בשאלה "איך לייצג את דרישות המוצר שלנו?" אם יש לנו מוצר בגודל שאינו טריוויאלי, יש לנו כנראה הירארכיה וחלוקה לקטגוריות בין הדרישות, כי אחרת לא נצליח למצוא את הידיים ואת הרגליים שלנו בתוכן. בחירת ההירארכיה הנכונה היא סיפור לא פשוט בפני עצמו. למשל, בואו נדמיין פלטפורמת בלוגים, כמו זו שאני כותב בה עכשיו את הטקסט הזה. זה סביר למדי לחלק את הדרישות לכאלה שקשורות להצגת הפוסט ולכאלה שזמינות למי שכותב את התוכן. אולי יש הרשאה שלישית למנהלי הבלוג, אבל כרגע נניח שהכותב והמנהל הם אותו אדם. אז עץ הדרישות שלנו עשוי להיראות כך:</p><p dir="rtl" style="text-align: center;"><img alt="" height="205" 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" width="320" /> </p><p dir="rtl" style="text-align: right;">איפה, בתוך המודל שלנו, נשים את הדרישות שקשורות להערות? מצד אחד, לקוראים יש יכולת לכתוב ולקרוא תגובות, אולי גם לערוך, אבל למנהלים יש יכולת לאשר או להסיר הערות, ולקרוא אותן בצורה מרוכזת. האם יהיה נכון לפצל את הפיצ'ר הזה ולפזר את הדרישות שלו על פני העץ? אולי יהיה נכון יותר להוסיף קטגוריה שלישית של פיצ'רים שמשותפים לקוראים ומשתמשים? אולי יהיה נכון יותר לחלק את העץ שלנו בצורה אחרת לגמרי? נניח, משהו כזה:</p><p dir="rtl" style="text-align: center;"><img alt="" height="147" src="data:image/png;base64,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width="320" /> </p><p dir="rtl" style="text-align: right;">ואם חילקנו את זה כך, איך אנחנו מייצגים תחת הדרישות להערות את המידע החשוב "הדרישות האלה רלוונטיות לקורא בלבד, אלה רלוונטיות למנהל ואלה משותפות"? זו לא שאלה של מודל "נכון" יותר מהשני, השאלה היא עם איזה מודל אנחנו נוכל לעבוד בקלות רבה יותר ואיזה מודל מייצג בצורה טובה יותר את הדרך בה אנחנו חושבים על המוצר. כך או אחרת יהיו לנו בחירות לא פשוטות עם השלכות על המשך העבודה. כדאי לזכור, אגב, שזו הייתה הדוגמה הפשוטה. רוצים אתגר משמעותי יותר? איך תייצגו את הדרישה "הסשן של המשתמש צריך להישמר עד לפעולת log-out יזומה"? או "כל הדפים צריכים לעמוד ברמת נגישות של WCAG level 2 (AA)"?</p><p dir="rtl" style="text-align: right;"></p><p dir="rtl" style="text-align: right;">בקיצור, זה לא פשוט לבנות את העץ, אבל זה בסדר - אנחנו לא חייבים לעשות כאן עבודה מושלמת. רק כזו שתהיה טובה מספיק כדי להפיק ממנה ערך, ונאלתר משם. מוכנים לאתגר הבא? מה אנחנו עושים בין גרסאות של מוצר? שומרים רק את העץ האחרון? שמים את זה בגיט ומנהלים גרסאות? האם אנחנו יודעים להשוות בין גרסאות שונות של העץ? </p><p dir="rtl" style="text-align: right;">ואם כבר הגענו לזה - זה עץ דרישות, מי מתחזק אותו? האם אנשי המוצר שמוסיפים דרישות למוצר יעשו את זה על גבי העץ? האם זו דרך הגיונית להציג דרישות לצוותי הפיתוח כשהן מפוזרות על פני כל העץ? האם צריך לבקש מהם לעשות עבודה כפולה? עד כה, ראיתי רק מקומות בהם תחזוקת העץ "נפלה" על צוותי הבדיקות, מה שאומר שהדרישות לפיצ'רים חדשים לא ממש התאימו לעץ והיה צריך להשקיע זמן ומאמץ כדי להתאים אותן: בכל פעם בה נוסף פיצ'ר חדש, מישהו מצוות הבדיקות היה צריך לעבור על הדרישות ולמצוא את המקום הנכון להן בתוך העץ, ולפעמים לפרק דרישה אחת לשתיים, שלוש, או חמש דרישות נפרדות מפוזרות על פני כל העץ. כמובן, המאמץ הזה לא הפך את העץ לרשמי בשום צורה, ובמידת הצורך היינו צריכים לחזור ולמצוא את הדרישה המקורית במסמך שקיבלנו. </p><p dir="rtl" style="text-align: right;">הצעד הבא - שימוש בעץ כדי להגיע ממנו לבדיקות הספציפיות, הוא ממש פשוט - בתנאי שאנחנו משתמשים בכלי כבד לניהול בדיקות כמו למשל quality center של מיקרופוקוס (לשעבר HP) או QAComplete של smartbear. אני בטוח שיש עוד כמה כלים, אבל לא התעדכנתי בחמש השנים האחרונות בערך לגבי כלים ספציפיים בתחום. אם משתמשים בכלי כזה, אנחנו יכולים להוציא דו"ח כיסוי לכל דרישה, ולכל חלק בעץ, יחד עם תוצאות הריצה והבאגים. אם אתם משתמשים בכלי כזה, אתם גם יודעים טוב מאוד כמה הוא יקר לשימוש, ולא רק במחיר הרישיון. עדיין תהיה לנו את ההתלבטות של איך לייצג את הדרישה לתמוך ב30 שפות ולתפקד היטב גם על טלפונים חכמים, אבל נניח כרגע שזה לא החלק הדרמטי. העלות המציקה באמת היא להמשיך ולתחזק את הקשרים האלה. הוספתם עוד מבדק אוטומטי? ברכותי - יש לכם עוד חמש דקות מיותרות של ליצור עבורו רשומה בכלי הזה ולקשר אותה לכל המקומות הנכונים. אם אנחנו רוצים לקבל את הקסם שבנעקבות נצטרך להשקיע זמן ומאמץ בתחזוקת העץ לאורך חיי המוצר - שינויי דרישות, שכתוב, החלפת טכנולוגיות ומה לא. </p><p dir="rtl" style="text-align: right;">בקיצור, יקר. <br />מצד שני, "יקר" לא תמיד אומר "לא משתלם". יכול בהחלט להיות שהערך שאנחנו מקבלים מכל כאב הראש הזה לחלוטין מצדיק את ההשקעה. אז בואו ננסה להבין מה אנחנו מקבלים אם כבר הצלחנו ליצור כזה עץ לתפארת. </p><p dir="rtl" style="text-align: right;">ניקח את המערכת עליה כבר דיברנו ונוסיף לה פיצ'ר אחד של ניסויי A\B לפוסטים כדי לבדוק מה מבין הפרסומים שלנו אפקטיבי יותר. כיוון שכבר יש לנו עץ שבנינו בלי יותר מדי פשרות מוזרות אנחנו מוסיפים את הדרישות הרלוונטיות לעץ ומגלים שהתחומים המושפעים הם עריכת וקריאת פוסטים, אנליטיקה, ניהול משתמשים וcaching (כי "הטמנה" זה מה שעושים עם פסולת). גילינו את ההשפעות האלה ממש מהר בזכות העץ שלנו. אנחנו גם יודעים בזכותו אילו מבדקים צריך לעדכן או להריץ מחדש, ואיפה יש לנו פערים שאנחנו צריכים לכסות עם מבדקים קיימים. אנחנו גם לא מבזבזים זמן בכתיבה מחדש של מבדקים רק כי לא ידענו שהם כבר כתובים. </p><p dir="rtl" style="text-align: right;">אז כמה זמן חסכנו בפועל? נניח שפעולת הוספת הדרישות לעץ כבר חושפת בפנינו בחינם את הדרישות שלא כוסו וצריכות להשתנות בלי שום מאמץ אנליטי נוסף. כתיבת המבדקים האוטומטיים והרצת הבדיקות הקשובות לוקחת את אותו הזמן ואנחנו משלמים עוד מחיר קטן יחסית כדי לקשר את המבדקים החדשים לעץ המוצר ואז סיימנו. לעומת זאת, אילו היינו עובדים בלי עץ, היינו עושים מה שסביר להניח שרוב הקוראים עושים היום - מתחילים בניתוח הדרישות ותכנון הבדיקות הנדרשות, חיפוש במאגר המבדקים שכבר יש לנו כדי לראות מה מכוסה ומה צריך לעדכן, ואז עוברים דרך אותם צעדים של תכנון, עדכון וביצוע המבדקים עצמם. מבחינת זמן שהשקענו, ניתוח הדרישות ללא הצורך לשכפל אותן לתוך העץ שלנו היא פעולה מהירה יותר. כמה מהירה יותר? זה משתנה, אבל נראה לי שחיסכון בזמן של 10% זו הערכה שמרנית. חוץ מזה, יש את עניין איתור המבדקים הקיימים ועדכונם. כאן מתחבא, לפחות בפוטנציה, בזבוז זמן רציני - אם יש לנו מאות ואלפי מבדקים, ואנחנו מחליפים בודקי תוכנה כמו גרביים, זה עשוי לקחת ימים שלמים, בהשוואה לתוצאה המיידית שקיבלנו בעזרת העץ. למעשה, סביר שלא ננסה לחפש ופשוט נכתוב מחדש את הכל ונתמודד עם הכפילויות. חוץ מדבר אחד - אנחנו חיים בעולם האמיתי. בעולם הזה סביר להניח שיש לנו מישהו שכבר עובד עם המערכת שנה, שנתיים או עשר, וידע לכוון אותנו בתוך עשר דקות למקומות הרלוונטיים. זה עדיין יותר חיפוש, אבל הוא קצר באופן משמעותי מהמחיר של תחזוקת העץ. בנוסף, אם חלק הארי של המבדקים שלנו אוטומטי - הרצה שלהם תיתן לנו מענה לגבי "מה נשבר" כך שנדע לעדכן. בקיצור - גם על הנייר אנחנו מאבדים זמן ברוב המקרים. </p><p dir="rtl" style="text-align: right;">אבל אולי הרווח אינו בזמן? אולי הוא ברמת הוודאות שיש לנו שעשינו את העבודה היטב? יש לא מעט ערך בידיעה שאנחנו משקיעים את המאמץ במקומות החשובים, והסיכוי שנחמיץ משהו חשוב נמוך יותר. חוץ מזה, בעזרת מודל כזה נוכל ליצור דו"ח כיסוי בכל רגע בו נזדקק לאחד כזה. אולי אפילו נשתמש בו כאחד הקריטריונים בישיבת Go\No-Go הרבעונית בה אנחנו מחליטים לדחות את שחרור הגרסה כי לא הספקנו. <br />רגע, מה? אנחנו כבר לא עושים כאלה דברים? מחזורי פיתוח מהירים וספרינטים קצרים? CI\CD? שינויים קטנים ומשוב מלקוחות בשטח? אנחנו חיים בעולם בו רוב המבדקים שנערכים למוצר שלנו הם אוטומטיים ורצים באופן תדיר, כך שאנחנו יודעים אם הם נשברים. כיוון שאנחנו מוסיפים שינויים קטנים בכל פעם, קל לנו לנתח אותם בצורה טובה, ואם בכל זאת פספסנו - יש כלי ניטור שיעזרו לנו למצוא את התקלה בשטח ונוכל להוציא תיקון מהיר. כך שגם האספקט הזה נופל בחשיבותו. </p><p dir="rtl" style="text-align: right;">ונכון, לא כולנו עובדים במערכות היפר-אג'יליות עם כל הנצנצים האלה, ואולי אנחנו עדיין תקועים במחזורי פיתוח ארוכים, מוציאים גרסה פעם באף פעם ועיוורים לגמרי למה שקורה אצל הלקוחות שלנו. אבל אם אנחנו במצב כזה, אנחנו רוצים לשנות אותו ולעבור למודל פיתוח אג'ילי יותר, פשוט כי זו הדרך העיקרית לשרוד ולשגשג בשוק היום. אם אנחנו רוצים לפתח התנהגויות כאלה - למה לאמץ כלים ותהליכים שמובילים אותנו בכיוון ההפוך?</p><p dir="rtl" style="text-align: right;"> </p><p dir="rtl" style="text-align: right;">כמובן, לכל כלל יש גם יוצא מן הכלל, וישנם מקרים בהם יכול להיות שממש נרצה לבנות עץ דרישות פורמלי ולהשקיע את המאמץ הנדרש. סיבה אפשרית אחת היא אם מישהו מכריח אותנו - אולי אנחנו נמצאים בתעשייה עם רגולציה כבדה במיוחד כמו רפואה או טיסה, או בכל סיטואציה בה המשמעות של כישלון אצל לקוח היא שמישהו עשוי למות. יכול להיות שבמהלך ביקורת נצטרך להראות עץ נעקבות מלא, ואם זה כך - כל הארגון יתיישר לעבוד עם המודל הזה, מה שגם יוריד את המחיר של תחזוקה (לא לאפס, אבל בתקווה מספיק). האם זה אומר שבהכרח נרצה להשתמש בעץ דרישות במצבים כאלה? אין לי מושג. אני לא יודע אם ישתמשו בזה למשהו חוץ מאשר כדי לעבור את הביקורת, או אם יש דרכים קלות יותר לנהל את המידע הדרוש. כך או אחרת כשאנחנו מגיעים לסיטואציה בה אנחנו שוקלים לקחת על עצמנו כזו משימה ענקית, כדאי שנשאל את עצמנו מה אנחנו באמת שואפים להרוויח, והאם הרווח הזה שווה את העלות ואת המאמץ.<br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-62474307287896062382023-08-09T19:41:00.001+03:002023-08-09T19:41:17.634+03:00Cargo-cult testing<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://www.dover489.org/wp-content/uploads/2018/06/cargo.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="464" data-original-width="640" height="290" src="https://www.dover489.org/wp-content/uploads/2018/06/cargo.jpg" width="400" /></a></div><br /><p></p><p dir="rtl" style="text-align: right;"> <a href="https://always-fearful.blogspot.com/2023/08/cargo-cult-testing-heb.html" target="_blank">עברית</a><br /></p><p><a href="https://en.wikipedia.org/wiki/Cargo_cult" target="_blank">Cargo cults</a> were a set of practices and beliefs that sprang in Melanesia post WWII, where the native population saw the westerners arrive, set up some rudimentary airfield, landing stripes, control tower, etc. and started getting supplies by planes. When the war was over, the natives tried to achieve the same results by maintaining their own airfields, trying to lure the gods to send them food and weapons as well. They were missing only one part: The people sending supplies from the other side of the airplane flight. Since it was coined, this term was used to indicate situations where someone is mimicking the form but missing out the essence.</p><p>And how does this relate to software testing? Well, every now and then there's a public talk on "how to get hired". Naturally, talks like this focus on people with less experience and so, eventually, someone will spurt "go and test a public website or product". Sounds legit, right? it's just like coding - you can demonstrate your skill by creating a useless pet project and present it to whomever it is you might want to hire you. </p><p>Just like the cargo cults, however, there's only one part that's missing - The actual, professional testing. Ask any experienced tester and they'll be able to tell you that the important things are not test cases ran, bug reports written or the excessive documents produced. The real value is in the conversations you have, in bringing a different perspective to reviews, <a href="https://www.brendanconnolly.net/testers-translators/" target="_blank">in translating between different functions</a>, highlighting the risks and improving design by presenting just the right question at the right time. When you work (effectively) as a tester on a product you gain a profound understanding of its inner workings, of its tech stack, architecture and technical debt. You gain some insight on the business side: what's important, what's less urgent, what sort of problems we don't care enough to bother fixing and what properties of the product are the ones that help sell it. You then use this understanding in the conversations and test design.<br /><br />Those parts are completely missing from the so0called "experience" you'll get if you follow such advice. If you are investing your time on that, or even on the paid version in crowdsourced testing, you might be gaining or demonstrating some technical capabilities, but you are not dealing with nor growing the core skills required in a testing position. You are not negotiating schedule difficulties, you are not involved in prioritization debates and you are smashing your keyboard without the slightest idea of the priorities of the company whose product you are testing or the entire iceberg beneath the nice looking website you chose as your target.</p><p> </p><p>Or, if we sum it all in a click-baity fashion: Good software testers shouldn't waste their time testing. <br /></p>Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-7221956419134179830.post-72900555917279735362023-08-09T19:41:00.000+03:002023-08-09T19:41:09.755+03:00על בדיקות ועל כתות מטען<p dir="rtl" style="text-align: right;"></p><div style="text-align: center;"><a href="https://www.dover489.org/wp-content/uploads/2018/06/cargo.jpg" imageanchor="1"><img border="0" data-original-height="464" data-original-width="640" height="290" src="https://www.dover489.org/wp-content/uploads/2018/06/cargo.jpg" width="400" /></a></div><br /><p></p><p style="text-align: left;"> <a href="https://always-fearful.blogspot.com/2023/08/cargo-cult-testing.html" target="_blank">English</a></p><p dir="rtl" style="text-align: right;"><a href="http://he.wikipedia.org/wiki/פולחן_מטען" target="_blank">כתות מטען</a> הן תופעה שצצה במלנזיה בעקבות מלחמת העולם השנייה. התושבים הילידים ראו את האדם המערבי מגיע, בונה שדות תעופה, משרדים, מגדלי פיקוח ומסלולי נחיתה ובעקבות זאת מצליח לגרום לאלים (או לרוחות האבות) לשלוח לו אוכל, נשק ומשאבים נוספים. כשהחיילים עזבו, הילידים ניסו לחקות את מה שראו כדי לגרום לחלק מכל השפע הזה להגיע גם אליהם. היה חסר להם רק חלק קטן אחד - האנשים בצד השני של הטיסה שבנו את המטוס ומילאו אותו בכל הציוד הנדרש. מאז שהביטוי הזה נטבע הוא משמש כדי לתאר מקרים בהם מישהו מחקה את הצורה אבל מחמיץ לחלוטין את המהות של מעשה מסויים. </p><p dir="rtl" style="text-align: right;">ואיך כל זה קשור לבדיקות תוכנה? ובכן, מדי פעם אפשר למצוא באינטרנט (ובמקומות אחרים) הרצאות עם הנושא החשוב "איך למצוא עבודה". באופן די טבעי, חלק נכבד מההרצאות האלה יעסוק במי שאין לו ניסיון. יש כל מיני דברים שאנשים חסרי ניסיון יכולים לעשות, אבל מתישהו מישהו יפלוט אמירה בסגנון של "בחר לך איזה אתר אינטרנט ולך לבדוק אותו, כתוב תוכנית בדיקות ומצא באגים". ככה, לפחות על פי הרציונל, יהיה לנו מעין "תיק עבודות" שנוכל להראות. <br />עצה טובה, לא? הרי זה בדיוק מה שמתכנתים עושים. הם לוקחים בעיית צעצוע שלא חשובה לאף אחד וכותבים קוד בסיסי שמטפל בה. ככה הם מראים כמה הם יודעים למי שאולי ירצה לראיין אותם. </p><p dir="rtl" style="text-align: right;">כמו במקרה של כתות מטען, חסר פה רק חלק קטן אחד - בדיקות תוכנה מקצועיות. כל בודק תוכנה מנוסה ידע לספר שאת רוב הערך משיגים עוד לפני שכתבנו או הרצנו בדיקה אחת. שזה לא חשוב כמה יפה כתובה תוכנית הבדיקות או כמה מפורט הבאג שפתחנו. מה שחשוב זה השיחות שיש לנו מסביב כשאנחנו מתכוננים. זו השאלה שנשאלה בדיוק ברגע הנכון כדי לשנות לחלוטין את איך שמפתחים פיצ'ר וחסכה לכולם מלא שעות של תסכול, זו הפרספקטיבה השונה שמביאים לסקירת הדרישות או העיצוב, אלה שיחות המסדרון בהן חושבים יחד עם המפתחים על דרך טובה לפתור איזו בעיה. זה כשאנחנו <a href="https://www.brendanconnolly.net/testers-translators/" target="_blank">מתפקדים כמתרגמים</a> או כשאנחנו מסתכלים על "מה יכול להשתבש" במקום על "איך לגרום לזה לעבוד". הם ידעו לספר שכשעובדים כבודקי תוכנה ועושים את העבודה היטב אנחנו לומדים המון על המערכת הנבדקת ועל איך היא בנוייה. על הארכיטקטורה שלה ועל המוזרויות של הספריות הספציפיות בהן היא משתמשת, על האילוצים הטכניים ועל הדברים שנשברים באופן תדיר יותר. אנחנו גם לומדים קצת על הצד העסקי - על אילו תלונות מגיעות מהלקוחות שלנו, על מה חשוב לנו מספיק כדי לעצור גרסה או אילו תכונות עוזרות לנו למכור את המוצר על פני המתחרים. מה דחוף וממה לא אכפת לנו בכלל ולא נתקן גם אם הוא שבור. אנחנו מנצלים את כל הידע הזה בתכנון הבדיקות ובשיחות שלנו עם אנשים. </p><p dir="rtl" style="text-align: right;">החלקים האלה נעדרים לחלוטין מ"תיק העבודות" שנוכל לבנות אם נעקוב אחרי העצה המטופשת הזו. אם אנחנו משקיעים זמן בדברים האלה, או אפילו מצטרפים לפלטפורמת בדיקות המון ומקבלים דמי כיס על בניית תיק העבודות. בהחלט יכול להיות שנוכל לשפר ולהדגים כמה כישורים טכניים, ואפילו כאלה שנמצאים במודעות הדרושים, אבל צריך לזכור שזה לא נוגע בכישורים הספציפיים של בדיקות תוכנה. במצבים האלה אנחנו לא נגיע לדיונים על סדרי עדיפויות או על מה אפשר לא לבדוק, לא נצטרך להתווכח על תכנון תאריכים ולא נדע אם המשוב שלנו הועיל למישהו. פשוט חבטנו קצת על המקלדת בלי שיש לנו מושג ירוק מה חשוב לחברה. הסתכלנו על קצה הקרחון שחשוף באתר האינטרנט בלי שיש לנו גישה לכל הקרחון העצום שמתחת לפני השטח של האתר החמוד שבחרתם לעצמכם כקורבן. </p><p dir="rtl" style="text-align: right;"> או, אם לסכם את כל הפוסט במשפט קצר ורק קצת מוקצן - בודקי תוכנה טובים לא מבזבזים את הזמן שלהם על בדיקות. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-83661307557467356232023-07-28T08:23:00.002+03:002023-07-28T08:23:27.991+03:00פשוט אמרו לא<p dir="rtl" style="text-align: right;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh5_5_T4c-Uh1QMYlUze59jYdmL_PSmxu7oESIFPrNTNVV5yZAGsza0SytW0xJTZK8AyteXGdJxLG7mr0Py-Sbqp0SYQVhwi2VUdEv1QuPRBeAt3eq961VRrvjXW0ibDUTqN1eRrRTXmADqXqmvhNrKVMdqyDK5vdEOQ5cI1TK_5GiwOVutlRp8430s3gY/s900/certified%20poop.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="640" data-original-width="900" height="228" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh5_5_T4c-Uh1QMYlUze59jYdmL_PSmxu7oESIFPrNTNVV5yZAGsza0SytW0xJTZK8AyteXGdJxLG7mr0Py-Sbqp0SYQVhwi2VUdEv1QuPRBeAt3eq961VRrvjXW0ibDUTqN1eRrRTXmADqXqmvhNrKVMdqyDK5vdEOQ5cI1TK_5GiwOVutlRp8430s3gY/s320/certified%20poop.jpg" width="320" /></a></div><br /><p></p><p style="text-align: left;"> <a href="https://always-fearful.blogspot.com/2023/07/still-not-polished.html" target="_blank">ENGLISH</a></p><p dir="rtl" style="text-align: right;">לפני חמש שנים כששמעתי על הסילבוס החדש של ISTQB CTFL הקדשתי לו את הזמן, קראתי ו<a href="https://always-fearful.blogspot.com/2018/07/things-you-cant-polish-until-shine.html">כתבתי עליו ביקורת</a>. במאי השנה, אותו ארגון ממש הוציא את גרסה 4.0 של הסילבוס הנ"ל, ואחרי ששמעתי עליו מאחד האנשים שהשתתפו בשלב ההערות והעריכה החלטתי להקדיש את אותו המאמץ כדי לראות אם המסמך החדש שעבר כל כך הרבה שיפורים אכן טוב יותר. אחרי הכל, מסיבות שאינן לחלוטין ברורות לי, יש כמה אנשי מקצוע טובים מאוד שמשייכים את עצמם לארגון הזה, ואולי הם הצליחו להוציא משהו סביר תחת ידיהם. </p><p dir="rtl" style="text-align: right;">כבר בהתחלה, אפשר לשים לב לשיפור אחד ניכר - המסמך החדש קצר יותר. במקום 76 עמודי תוכן ממשי (לא כולל מראי מקום, זכויות יוצרים ושאר מילוי תוכן שאין טעם לקרוא) יש הפעם רק 50 כאלה, מה שיכול להסביר איך הצלחתי לסיים את קריאת החומר בתוך שעה ו55 דקות בהשוואה לשלוש ומשהו שעות שזה לקח לי בפעם הקודמת. הפעם, כיוון שחלק מהקריאה התבצע ברכבת, לא מדדתי את הזמן באמצעות פומודורי, אלא בעזרת כלי בשם super productivity שאפשר לי למדוד זמן שהקדשתי למשימה הזו. באופן משעשע, סיימתי את הקריאה עם מספר דומה של הערות שוליים שכתבתי לעצמי (59 הפעם, כלומר שתיים יותר) וכמו בפעם הקודמת - רוב ההערות היו שליליות. המסקנה הבסיסית שלי אחרי קריאת הטקסט - זו עדיין פיסת תוכן מאוד לא טובה ואני בטוח שכל אחד מהכותבים (וקל וחומר כולם יחד) יכולים להוציא תחת ידיהם משהו טוב יותר. </p><p dir="rtl" style="text-align: right;">כמו בפעם הקודמת, הבעיה המהותית היא במודל העסקי ובהנחות שהוא כופה על המסמך. כדי ללמד נושא שלם בפחות מ19 שעות (שעתיים ועשרה יותר מאשר בסילבוס הקודם!) צריך לסמוך על המון ידע מוקדם, או לעשות עבודה גרועה במיוחד. חלק מהנושאים שמוזכרים בסילבוס בקצרה הם מורכבים ורב מימדיים, מה שאומר שצריך זמן כדי להפנים אותם ולהפוך אותם לכלי יעיל. לצמצם את הכל לכדי משהו שאפשר לשאול עליו 40 שאלות אמריקאיות בלי להניח ידע מוקדם של הנבחנים? שום סיכוי בעולם. </p><p dir="rtl" style="text-align: right;">אילו היו מכוונים לאנשים עם ניסיון (כמו, למשל, שעושים ISC2 עם הסמכת CISSP) הייתה יכולה לאפשר למסמך הזה להיות ממוקד יותר ולפתוח נושאים לדיון גם במסגרת הזמן המוגבל שמקצים בו. כפי שהמצב כיום, הסילבוס מנסה להיות גם מבוא לעבודה בחברת תוכנה וגם לתאר את הליך הבדיקות הכבד והמתיש ביותר שניתן לדמיין (הם נעזרים לשם כך ב IEEE 29119, שעל המחלוקת סביבו אפשר לקרוא בויקיפדיה). מה לגבי להזכיר שזה בסדר להתאים את התהליך ושלא חייבים להשתמש בכל הארסנל הכבד הזה כדי להרוג זבוב? להסביר מתי נכון להימנע מחלקים מסויימים ומה הערך שהם מוסיפים במקרים אחרים? בשביל מה? למה לא להעמיד פנים שהדבר המיותר הזה הוא הסטנדרט שראוי להתיישר סביבו? הרבה יותר מצחיק ככה. </p><p dir="rtl" style="text-align: right;">המסמך הזה הוא שינוי דרמטי למדי ביחס לגרסה הקודמת (כמו שאפשר להבין מקיצור היריעה) ויש בגרסה הזו כל מיני דברים חדשים. קודם כל, יש שיפור ניכר בבאזזוורד בינגו - מונחים כמו דבאופס, CI\CD או הזחה שמאלה נזרקים לחלל המסמך כאילו אין מחר. באופן טבעי, אלו מילים חלולות. </p><p dir="rtl" style="text-align: right;">אבל, גרוע אפילו מזה הוא היחס שמקבלים כמה מונחים שנעדרו מהסילבוס ונפוצים בקרב בודקי תוכנה. ספציפית, ההתייחסויות לפירמידת הבדיקות ולמרובע הבדיקות האג'יליות. שני המונחים נמצאים, מסיבה עלומה כלשהי, תחת הקטגוריה "תכנון בדיקות". למה? ישנם, הורציו, בשמיים ובארץ, דברים שנפלאו מדעת חכמיך. <br />כנראה שתיאור הפירמידה הוא התיאור הכי גרוע שנתקלתי בו עד היום. מעבר לכך שלא טורחים להסביר איך המודל הזה שימושי אלא פשוט מתארים אותו באופן גרוע. ואז, מגיע הדבר הזה:</p><blockquote><p style="text-align: left;">Tests in the bottom layer are small, isolated, fast, and check a small
piece of functionality, so usually a lot of them are needed to achieve a
reasonable coverage. The top layer represents complex, high-level,
end-to-end tests. These high-level tests are generally slower than the
tests from the lower layers, and they typically check a large piece of functionality, so usually just a few of them are needed to achieve a reasonable coverage.<br /></p></blockquote><p dir="rtl" style="text-align: right;">בעוד שהתיאורים פה הם טכנית נכונים, האם הבחנתם בכך שהטקסט בעצם שולח את הקורא להשוות בין הרמות השונות ולטעון שאפשר להשיג את אותה רמת כיסוי (של מה?) בעזרת הרבה בדיקות קטנות או מעט גדולות? האם הבחנתם בכך שזה נעשה במקום לציין איך שהשכבות השונות אמורות לפעול בסינרגיה?</p><p dir="rtl" style="text-align: right;">הטיפול שמקבלים הרביעונים האג'יליים אפילו גרוע יותר, אם דבר כזה אפשרי. במקום להשתמש במודל כפי שהוא מיועד להיות - מודל תיאורי שנועד לעזור לנו לאתר נקודות עיוורון ביחס שלנו לבדיקות, הופכים את המודל הזה לסט הנחיות לביצוע. למשל:</p><p style="text-align: left;"></p><blockquote><p style="text-align: left;">Quadrant Q1 (technology facing, support the team). This quadrant contains component and<br />component integration tests. These tests should be automated and included in the CI process</p></blockquote><p dir="rtl" style="text-align: right;">באופן לא מפתיע בכלל, הטיפול של הסילבוס בבדיקות חוקרות ממשיך לסבול מחוסר הבנה קיצוני של המושג. הראיה הברורה ביותר? בדיקות חקר מתוייגות כ"טכניקת בדיקות", אחת מכמה טכניקות "מבוססות ניסיון". בדיקות חוקרות אינן לא זה ולא זה. בראש ובראשונה, זו אינה טכניקה, אלא גישה מקיפה לבדיקות תוכנה - כזו שאפשר להפעיל בעזרתה כל טכניקת בדיקות שתרצו (ספציפית, באך ובולטון גורסים שכל הבדיקות הן בדיקות חוקרות), ובנוסף, בדיקות חוקרות אינן "מבוססות ניסיון" יותר מאשר הגישה לפיה מנסים לכתוב את כל הבדיקות מראש. ההבדל המרכזי הוא בכך שאנחנו מכירים בעובדה שבודקי תוכנה מנוסים ישיגו תוצאות טובות יותר ולא מנסים להעמיד פנים שמעקב אחרי האלגוריתמים שהזכרנו יוביל לתוצאות זהות גם אצל עובדים לא מיומנים. בסופו של יום, כל הטכניקות הפורמליות שמוזכרות בסילבוס נמצאות בתוך סט הכלים של הבודק גם במהלך חקירה, ועליהן מוסיפים כמה יוריסטיקות שזמינות רק במהלך ביצוע הבדיקות. </p><p dir="rtl" style="text-align: right;"><br /></p><p dir="rtl" style="text-align: right;">אז לסיכום, <br />להסמכת CTFL עדיין יש קורלציה שלילית ליכולתו של אדם לתפקד היטב כבודק תוכנה מקצועי. הסילבוס עדיין מקדם מגוון תפיסות שגויות לגבי בדיקות תוכנה - בין אם הטענה על כך שנדרשת "עצמאות" של גוף הבדיקות ("accelerate" כבר השליכו את הטענה הזו מהחלון לגבי רוב המקרים), שמציאת באגים היא אחת המטרות של בדיקות תוכנה או שהדרך הנכונה להתקדם היא בעזרת תהליך ארוך וכבד. הסילבוס עדיין מתמקד בתיאור של איך דברים נראים באופן שטחי וכמעט ולא מתייחס לשאלה "כיצד נכון לעשות" ואין בו אפילו זכר לשאלה הכי חשובה "למה עושים" (למעשה, הנקודה היחידה בה שואלים למה היא "למה חייבים לבדוק תוכנה", וזה דווקא המקום בו צריך לשאול - מתי צריך לבדוק ומתי לא). הסילבוס עדיין לא עוזר לבודקי תוכנה לתקשר עם אנשים מחוץ למקצוע, ולמעשה מקשה על התקשורת בכך שהוא משתמש בחלק מהמונחים בצורה שונה מזו שמקובלת בתעשייה. הוא עדיין מתמקד יותר מדי בתוצרים ומסמכים מיותרים ולא מספיק בתועלת שאמורים להשיג בזמן הכתיבה שלהם, והוא עדיין מכוון כדי לאפשר לאנשים לא מיומנים עם אפס הבנה של התחום לנפנף בחתיכת נייר חסרת משמעות ולהציג אותה בפני מעסיקים עתידיים.</p><p dir="rtl" style="text-align: right;">או, ממש בקצרה - זו עדיין תרמית.<br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-65883819135493741272023-07-28T08:23:00.001+03:002023-07-28T08:23:08.483+03:00still not polished.<p> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOSOrjXk4BSd-PvoxXsjs3tRyAm1DpEaGMKr_bGPqZCxnXWpyxKHKM8pcTU05SUlJUKFyCxNY2kGMBepSFBs4Z1PDYMC5KB4sHdsGoM4vCE9Jfc_Mi5NE2NWXCxrV-z3S7vxHgMxLcJFrGz90HK38TbGyjbR1XXb-9bRmVXUbAvPJdmzXfu4IgQ6E5060/s900/certified%20poop.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="640" data-original-width="900" height="228" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOSOrjXk4BSd-PvoxXsjs3tRyAm1DpEaGMKr_bGPqZCxnXWpyxKHKM8pcTU05SUlJUKFyCxNY2kGMBepSFBs4Z1PDYMC5KB4sHdsGoM4vCE9Jfc_Mi5NE2NWXCxrV-z3S7vxHgMxLcJFrGz90HK38TbGyjbR1XXb-9bRmVXUbAvPJdmzXfu4IgQ6E5060/s320/certified%20poop.jpg" width="320" /></a></div><div dir="rtl" style="text-align: right;"><a href="https://always-fearful.blogspot.com/2023/07/still-not-polished-heb.html" target="_blank">עברית</a><br /></div><p></p><p>5 years ago, I welcomed the new release of ISTQB CTFL new syllabus with an <a href="https://always-fearful.blogspot.com/2018/07/things-you-cant-polish-until-shine.html" target="_blank">honest (if a bit hostile) reading</a>. This may, ISTQB have released version 4.0 of the CTFL syllabus, and after hearing about it from a colleague, I thought to myself that it would only be fair to give it the same respect. After all, for reasons unbeknownst to me, there are some very good professionals who identify with this organization, so maybe they have managed to get something decent from under their hands. </p><p>There is one significant improvement in this version only 50 pages of non-skippable content (such as refrences, appendices, copyright and the like) compared to 76 in the previous one, which can explain how I managed to go over it in 1 hour and 55 minutes (this time, as I was reading in the train, I did not use pomodoro, but rather a time tracking software name "super productivity"). Amusingly, I ended up with a similar number of comments (59, compared with the 57 from last time), and my conclusions are still the same - it is a terrible piece of content, and I'm sure that each and every one of the people who were involved with it (let alone all of them together) could produce something better from under their hands. </p><p>as before, the underlying problems are the business model and the assumptions it forces on the document: Teaching something in less than 19 hours (which is still 2 hours and 10 minutes more than 2018), must either rely on previous knowledge or experience or be extremely shallow and inefficient. Some of the topics mentioned in the syllabus are multi-faceted and complex, grokking them takes time and practice. Narrowing it to a 40 multiple-choice questions for people without any prior knowledge? that's simply impossible to do. </p><p>Aiming to people with some experience (and adding this as requirement to the certification, in a similar manner to what ISC2 does with its CISSP certification) would enable much more focus to this document. As it is, though, the syllabus still tries to be an introduction to working in tech and a description of the heaviest test procedure fathomable (Heck, they even mention the notorious IEEE 29119). Mentioning that it's ok to tailor it to your needs? nah, why not pretend it's the golden standard? much more fun this way. </p><p>As this document is a major revamp of the previous, there are some new things in this version. The buzzword bingo is even more noticeable. With terms such as shift-left, devops, CI/CD sprinkled just about everywhere. Naturally, it's devoid of meaning. </p><p>Even worse is the homage to some other terms that are by now well established in the testing profession. Those would be the test-pyramid and the agile testing quadrants. both terms are oddly placed under the section of test planning. Why? There are more things in heaven and earth, Horatio. </p><p><br />Their description of the pyramid is probably the worst botch I've seen in trying to describe or use it. Its existence is mentioned, but we skip completely questions like "how to use it". Then comes this: </p><blockquote><p>Tests in the bottom layer are small, isolated, fast, and check a small piece of functionality, so usually a lot of them are needed to achieve a reasonable coverage. The top layer represents complex, high-level, end-to-end tests. These high-level tests are generally slower than the tests from the lower layers, and they typically check a large piece of functionality, so usually just a few of them are needed to achieve a reasonable coverage.<br /></p></blockquote><p> while most of it is technically correct, have you noticed how they shift the whole issue to a comparison of "how many are needed to get full coverage" (one might also ask, coverage of what?) instead of mentioning that those are completing each other, or that this is an observation on what a healthy set of tests usually looks like, it's now hinting that you can get the same done with a few large tests as you can with a lot of smaller ones. </p><p>Treating the agile testing quadrants is even worse: instead of treating it as an observational, descriptive model that can expose some gaps in the way we think about tests or in what we actually implement, in the hands of the ISTQB authors, it has become a prescriptive model. <br />for instance:<br /></p><blockquote><p>Quadrant Q1 (technology facing, support the team). This quadrant contains component and<br />component integration tests. These tests should be automated and included in the CI process<br /></p></blockquote><p>Naturally, the ISTQB treatment of exploratory testing still represents an utter lack of understanding of it. It's still treated like an after-thought, not really planned testing, and it being placed under "experience based techniques" is probably the best sign of this misunderstanding - it's not a technique, it's a complete approach to testing, and it is not any more "experience based" than the approach where we try to write our test plans ahead of time. The main difference is that the exploratory testing approach admits that experienced people perform better. During exploration we employ all of the formal looking techniques that can be performed ahead of time, as well as other heuristics that are more readily available during test execution.<br /></p><p>so, summing it up: </p><p>CTFL certification still has negative correlation to one's ability to function in a professional testing situation. The syllabus still promotes a lot of misconceptions about testing - that it should have some "level of independence" ("accelerate" has thrown this claim out of the window for most cases) , that finding bugs is a goal and that a slow and heavy process is the best way to go forward. It still focused on telling the "what" and very rarely the "how", the most important "why" is not present at all (The only place is when the question "why is testing necessary", and there the question should be "When is it needed, and when not "). It still does not help testers communicate with non-testers, and I would say that it even hinders such efforts by choosing to use terminology in a way different than what is common in the industry. It still focuses too much on the useless artefacts produced and not on the value we should aim to get when creating them and it still aimed to enable untrained, unskilled people hold a piece of worthless paper and present it to future employers. </p><p>Or, briefly - it's still a scam. <br /></p><p> </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-24007809231921986632023-07-27T12:59:00.004+03:002023-07-27T12:59:57.642+03:00This too is a way to learn a lesson.<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.pixabay.com/photo/2017/11/09/23/37/school-2934975_960_720.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="536" data-original-width="800" height="268" src="https://cdn.pixabay.com/photo/2017/11/09/23/37/school-2934975_960_720.jpg" width="400" /></a></div><br /><p></p><p dir="rtl" style="text-align: right;"> <a href="https://always-fearful.blogspot.com/2023/07/this-too-is-way-to-learn-lesson-heb.htm" target="_blank">עברית</a></p><p>Recently we've engaged with a consulting company to help us build a product in our core business. </p><p>A terrible idea, right?</p><p>In our defense, there were some compelling(?) arguments to act this way. We knew the risks of employing a company under contract to build something we care about, and were willing to pay the price.</p><p>So, things have started moving. As the main reason we didn't do the work was that we were too busy with other stuff happening, the consulting company has started working pretty much unsupervised for a few months. When we finally joined the effort we could see a lot of design work that has been done, with some proof of concept code for various parts - it was not a lot, but it was as much as could be expected given the requirements we gave them and our availability until then. At least, this is what I thought until I examined their "tests". Starting with a review of the code, I found a couple of robot-framework scripts that are executing a browser against the UI defined in Figma, and only running in theory. But, we are starting to work on a new project and some work has been done by the contractor, so we should at least give it the benefit of the doubt, right?</p><p>So, we had a talk and I started asking the basic questions - Where in the SDLC are those tests expected to run? against which of the multiple repositories? how should the product be deployed? how often? who are the intended authors of future tests? who are the intended consumers of the results? <br />Based on the work I've seen, I didn't really expect them to have answers, but I could use this to start an internal discussion about how do we want to approach testing in the new product. While it was rather easy to name the gates that a piece of code should pass on its way to production, we found that some things needed to be defined better, and that we needed to choose a lot of new technologies, or at least review the existing ones to see if they fit. </p><p>We talked about what is a "component test", should we run full system tests on pull requests to some services, how to incorporate contract tests to our process, and so on. </p><p>As we are trying to answer those questions we find that the decisions we make are influencing the rest of the product - from our branching strategy to the way we architecture our product and deployment. There are some difficult questions that we need to answer in a short time, but it's quite interesting so far. </p><p>Key takeaways?</p><p><strike>First, never fight a land war in Asia. </strike></p><p>First, when you plan your testing strategy, take time to figure out your context - who should be doing what, what results are important, and what limitations will hinder your progress.</p><p>Second, Especially if you are a service provider, be very explicit about how you expect your work to integrate with other parts of the development process. Have a discussion about what you will need, what you will provide, and how will this impact the work of others. If you can't have a discussion, share your thoughts with the other people involved. </p><p>Third, your choices will change the way the entire team works, it will have an impact on both the processes you employ and the architecture of your product. In this sense, it's no different than choosing a tool, a programming language or the roles of people in the team. And just as is the case in those other examples, it is a two way street and choices make there will impact how you do testing. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-39188174787032734142023-07-27T12:59:00.003+03:002023-07-27T12:59:52.861+03:00גם זו דרך ללמוד לקח<p dir="rtl" style="text-align: right;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.pixabay.com/photo/2017/11/09/23/37/school-2934975_960_720.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="536" data-original-width="800" height="268" src="https://cdn.pixabay.com/photo/2017/11/09/23/37/school-2934975_960_720.jpg" width="400" /></a></div><br /><p></p><p style="text-align: left;"> <a href="https://always-fearful.blogspot.com/2023/07/this-too-is-way-to-learn-lesson.html" target="_blank">ENGLISH</a></p><p dir="rtl" style="text-align: right;">לפני זמן מה סגרנו חוזה בעבודה מול חברה חיצונית כדי שתוכל לפתח את אחד המוצרים שנמצאים בליבה העסקית שלנו. </p><p dir="rtl" style="text-align: right;">רעיון ממש רע, נכון?</p><p dir="rtl" style="text-align: right;">להגנתנו, היו כמה טיעונים טובים(?) למדי לעשות את זה. ידענו מה הם הסיכונים שבהעסקה של חברה חיצונית והיינו מוכנים לשלם את המחיר. </p><p dir="rtl" style="text-align: right;">בקיצור, הדברים התחילו להתגלגל, וכיוון שהסיבה המרכזית בגללה פנינו לחברה חיצונית הייתה שהיינו עסוקים עד מעל הראש בפרוייקטים אחרים, החברה התחילה לעבוד עם מעט מאוד הכוונה או מעורבות שלנו למשך כמה חודשים. כשסוף כל סוף התחלנו להצטרף למאמץ יכולנו לראות לא מעט עבודת תכנון שנעשתה, יחד עם הוכחת היתכנות לגבי כמה מהחלקים. זה לא המון, אבל בהתחשב באיכות הדרישות ורמת הקשב שהקדשנו להם, זה לגמרי מסוג הדברים שאפשר היה לצפות להם. לפחות, ככה הנחתי עד שהגעתי לבחון את החלק לו הם קראו "בדיקות". הסתכלתי קצת על הקוד שלהם וראיתי כמה סקריפטים שכתובים בעזרת robot-framework ומיועדים להריץ כמה תרחישים בעזרת הדפדפן, והחלק הכי טוב? זה נכתב אל מול ההגדרות שהיו בfigma כי כמובן שאין שום דבר פועל שאפשר לרוץ מולו. האם זה יכול לרוץ? תיאורטית. מול מה? לך תדע. מצד שני, אנחנו רק נכנסים לפרוייקט והחברה החיצונית כבר עובדת כמה חודשים אז בטח יש להם כל מיני מחשבות ותובנות לגבי איך הכל מתחבר, לא? </p><p dir="rtl" style="text-align: right;">קבענו שיחה, והתחלתי לשאול שאלות - איך זה אמור להתחבר לתהליך הפיתוח? איפה ומתי הסקריפטים האלה צריכים לרוץ? מי אמור לתחזק אותם? מי אמור לקרוא את הדו"חות שיצאו כתוצאה מזה? למה בחרנו את רובוט כתשתית להרצת הבדיקות?<br />אחרי שראיתי את הקוד, זה לא שבאמת ציפיתי לקבל תשובות אינטיליגנטיות, אבל זו הייתה הדרך שלי לשקף להם שהם בזבזו את הזמן לעצמם ולנו, ובנוסף יכולתי להשתמש ב"הכנות לשיחה" כדי להתחיל את הדיונים האלה אצלנו. בעוד שזה היה די פשוט להגדיר את השלבים השונים שקוד צריך לעבור כדי להגיע לתוך המוצר, אבל מה בדיוק כולל כל שלב, אילו טכנולוגיות מתאימות לו ודברים כאלה - זה כבר יותר קשה. דיברנו על איך כל אחד רואה את התחום המעורפל של "בדיקות רכיב" (component tests, שבניגוד למה שמי שקרא את המילון של ISTQB עשוי לחשוב, זה דבר שונה לגמרי מבדיקות יחידה), ואיך אנחנו רוצים להריץ בדיקות חוזה ובדיקות אינטגרציה ומתי ואיפה נרצה להריץ בדיקות מערכת מלאות.</p><p dir="rtl" style="text-align: right;">אחד הדברים שבלטו מאוד בדיונים האלה היה עד כמה צריך לקחת בחשבון את הארכיטקטורה שאנחנו רוצים לעבוד בה, ועד כמה הבחירות שאנחנו עושים משפיעות על תהליך הפיתוח ועל העבודה של כל הצוותים המעורבים. נתקלנו גם בכמה מגבלות שנובעות מהארכיטקטורה שאנחנו רוצים שתהיה למוצר, וגילינו שיש דברים שנצטרך לשפר כדי להגיע לתהליך הבדיקות שאנחנו רוצים. </p><p dir="rtl" style="text-align: right;">בקיצור, בזכות העבודה המחופפת של החברה החיצונית, קיבלנו הזדמנות לתכנן את תהליך הפיתוח שלנו באופן מודע ולא פשוט להתגלגל מדבר לדבר. שיעור מרתק. </p><p dir="rtl" style="text-align: right;"> </p><p dir="rtl" style="text-align: right;">אז, מה אפשר ללמוד מכאן?</p><p dir="rtl" style="text-align: right;"><strike>קודם כל, לא להשתתף במלחמה יבשתית באסיה</strike></p><p dir="rtl" style="text-align: right;">קודם כל, כשמתכננים את הבדיקות למוצר, קחו בחשבון את ההקשר - מי אחראי על מה, אילו תוצאות חשובות ולמי ומה המגבלות שיפריעו לנו בהמשך?</p><p dir="rtl" style="text-align: right;">שנית, במיוחד אם אתם מספקים שירותים לחברה אחרת, היו ברורים מאוד לגבי איך אתם רואים את התהליך. נהלו דיונים על הדברים שתזדקקו להם ועל אלו שתספקו כמו גם על ההשפעה שתהיה לכם על עבודתם של אחרים. אם אין לכם מול מי לנהל דיון כזה, הניחו הנחות ושתפו אותן. העבודה שלכם תימדד, בין היתר, על בסיס ההנחות האלה. </p><p dir="rtl" style="text-align: right;">לבסוף, הבחירות שלכם ישפיעו על כל הצוות. הן ישפיעו על התהליכים ועל ארכיטקטורת המוצר. במובן הזה בחירת תהליך בדיקות אינה שונה מבחירת טכנולוגיה לעבוד איתה, או הגדרת התפקידים השונים בצוות. ובדיוק כמו במקרה של הדוגמאות האלה - זו דרך דו סטרית. כל הדברים עליהם תהליך הבדיקות משפיע, ישפיעו עליו בחזרה. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-30107349857832028872023-03-12T09:06:00.001+02:002023-03-12T09:06:14.177+02:00Quality is a four letter word<div class="separator" style="clear: both; text-align: center;"><a href="https://images2.minutemediacdn.com/image/upload/c_fill,w_1080,ar_16:9,f_auto,q_auto,g_auto/shape%2Fcover%2Fsport%2Fistock-172435175-d4d251c233f964e5584b8f07a7bb1867.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="450" data-original-width="800" height="225" src="https://images2.minutemediacdn.com/image/upload/c_fill,w_1080,ar_16:9,f_auto,q_auto,g_auto/shape%2Fcover%2Fsport%2Fistock-172435175-d4d251c233f964e5584b8f07a7bb1867.jpg" width="400" /></a></div><br /><p><a href="https://always-fearful.blogspot.com/2023/03/quality-is-four-letter-word-heb.html" target="_blank">עברית</a></p><p>A question that started in the <a href="https://www.moderntesting.org/" target="_blank">ABTesting slack group</a> and has found its way to <a href="https://www.linkedin.com/posts/ckenst_software-quality-activity-7026368026388152320-py7b/?utm_source=share&utm_medium=member_android" target="_blank">LinkedIn</a> was wondering about whether an unethical product, even if exquisitely built, can be referred to as "high-quality". </p><p>This question resonated with a biff I have with the term "quality" for quite a while now. Specifically, with how it is commonly used. Since many of us have the word "quality" in our role description, many of us and of our colleagues in other software specialization are using it to indicate the sum of all desired things we might want in a piece of software. As we do that, we are shooting ourselves in our metaphoric leg. How bad is it? well, as I was writing this post, I stumbled upon this <a href="https://testingpeers.com/?p=1041" target="_blank">episode of the testing peers</a> where we can hear four professionals who care about their craft, spend half an hour talking nonsense. By the end of the episode titled "what is testing" they do recognize the fact that they have not actually defined it.It is of no surprise, this term is difficult to define and is pretty vague, and as I've written in the past, <a href="https://always-fearful.blogspot.com/2019/03/quality-in-closet.html" target="_blank">it does not conform with the idea of quality in other fields</a>. When my manger says "we have a quality problem in our product" he means one thing that is probably not the same as the product manager saying "We need to have this shipped with perfect quality", and is surely different from the now-so-common definition of "value to some person who matters" or the approaches <a href="https://isoupdate.com/resources/what-is-quality/">adopted by ISO</a> - "fitness for use" or "customer satisfaction". What we are getting when we use such an ambiguous term is a feeling of consensus that will blow up in our faces once the actual differences will start to show.</p><p>Moreover, "improving quality" is not actionable. If you actually have such a problem, you have a lot of work to do just to clarify what you actually need, and you will need to override people instinctive understanding of this term. Some people would go as far as claiming that "quality" is just about technical mastery and skillful coding. Others will stress the visibility of the product and others yet will look at the extent in which it gets the job done. All of those are important, but we need to align in order to move forward <br /></p><p>Another notion I've stumbled upon sometime during my career is that "quality" is just another word fro "property", which leads me to suggest talking about the specific qualities we are actually seeking in our software. It could end up being a long list, but it will enable us to align more easily, and to break the dichotomous view of "high\low overall quality" - Software (and life in general) is all about choosing the right tradeoffs, and I might go for something that looks nicer even if it is not as fast, or choose the product that preserves my privacy better over the one with more features and better integrations - we need to use a language that helps us notice these possible tradeoffs instead of concealing them under the very broad term "quality". </p><p>If "Quality" is a profane word that should not be used, what should we talk about instead? My preferred way is to break it down to different qualities, and preferably, to avoid confusion: Desired properties. What sort of desired properties are there of a software? I'll list a few here, and there are probably more that would be relevant in the various contexts one might find themselves in: </p><ul style="text-align: left;"><li>Correctness: This is the extent to which the software is doing what it is supposed to do. A tax calculator makes no tax mistakes given the proper input, a game is responding to the user commands in the expected way, etc. <br /></li><li>Robustness: The product can withstand messy (and even malicious) input, recover from mistakes and does so for a prolonged time. </li><li>Scalability: The product can support the expected growth in a viable way.</li><li>Performance: Personally I lump here both the quickness of response and the resources consumed by the system, those can be separated if we are in a place to make internal trade-off (for instance, throw more hardware to get faster results without a redesign). </li><li>Privacy: How well is the product protecting human data that it handles (or generates). </li><li>Ease-of-use: How well can people achieve their goals with the product.</li><li>Attractiveness: How appealing is a product to the target audience, that would conform to what James Bach has named "charisma"<br /></li><li>Explainability: The extent to which the behavior of the software is predictable or easy to explain.</li><li>visibility: can we figure out what happens in production? can we use that data to improve the product? </li><li>Safety: will the product kill someone? Harm them in any way? will releasing a new version put us in risk we can't tolerate?<br /></li></ul><p>This is a partial list that can be extended with a lot of other properties (an example can be found in <a href="http://thetesteye.com/posters/TheTestEye_SoftwareQualityCharacteristics.pdf" target="_blank">the paper</a> by Rikard Edgren, Henrik Emilsson and Martin Jansson extending James Bach's quality criteria), but one obvious thing is not on my list: Testability. Why is that? because in itself, it provides very little value. I think of testability as a secondary desired property that helps achieving the others or verify their state.Other properties in this category could be maintainability or extendability I also don't consider process properties in this list, even though they are often more desirable than almost every bullet in my list. Things like efficiency or pace of deployment: I had to draw the line somewhere and so I chose to stick with properties that are attached to the product and not to the teams building and maintaining it.</p><p>So, next time someone asks you how to improve the quality of your product, take the time to understand what do they really mean. Are they bothered by functional problems? do we have a security problem? There's no need to go out right and tell them "don't use this meaningless, confusing word", but try to drill down using guiding questions such as "what is the most significant current concrete pain that we feel now?" or "do we want to invest now our efforts in increasing our ability to spot problems in production before our customers, or do we have more pressing matters?" get to somewhere concrete and work from there. Ask such questions again after there has been some improvements in the top priority desired properties.<br /></p><p><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-38281123246423641702023-03-12T09:05:00.003+02:002023-03-12T09:05:44.285+02:00איכות זו מילה גסה<div class="separator" style="clear: both; text-align: center;"><a href="https://images2.minutemediacdn.com/image/upload/c_fill,w_1080,ar_16:9,f_auto,q_auto,g_auto/shape%2Fcover%2Fsport%2Fistock-172435175-d4d251c233f964e5584b8f07a7bb1867.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="450" data-original-width="800" height="225" src="https://images2.minutemediacdn.com/image/upload/c_fill,w_1080,ar_16:9,f_auto,q_auto,g_auto/shape%2Fcover%2Fsport%2Fistock-172435175-d4d251c233f964e5584b8f07a7bb1867.jpg" width="400" /></a></div><br /><p dir="rtl" style="text-align: right;"><a href="https://always-fearful.blogspot.com/2023/03/quality-is-four-letter-word.html" target="_blank">English</a></p><p dir="rtl" style="text-align: right;">שאלה שהתחילה בערוץ הסלאק של הפודקאסט <a href="https://www.moderntesting.org/" target="_blank">ABTesting</a> התגלגלה ל<a href="https://www.linkedin.com/posts/ckenst_software-quality-activity-7026368026388152320-py7b/?utm_source=share&utm_medium=member_android" target="_blank">פוסט בלינקדאין</a>. בקיצור נמרץ, השאלה היא "האם תוכנה לא מוסרית יכולה להיחשב כבעלת 'איכות גבוהה'?". </p><p dir="rtl" style="text-align: right;">השאלה הזו מתכתבת עם נושא שמציק לי כבר כמה שנים טובות: בעולם התוכנה משתמשים במילה "איכות" בצורה חסרת אחריות. אולי זה כי לחלקנו יש תפקיד שהמילה "איכות" מופיעה בהגדרה שלו, או כי כולם סביבנו מדברים ככה, אבל המשמעות של המילה "איכות" באופן בו משתמשים בה היא פחות או יותר "כל מה שטוב ושום דבר ממה שרע". השימוש הזה עושה בעיקר נזקים, ובמקרים מסויימים אפילו אפשר לומר שאנחנו יורים לעצמנו ברגל. כמה המצב חמור? בעודי כותב את השורות האלה נתקלתי <a href="https://testingpeers.com/?p=1041" target="_blank">בפרק של Testing peers</a> שמנסה להגדיר את המונח הזה. זו דוגמה מושלמת לאיך שארבעה אנשי מקצוע שמשקיעים לא מעט זמן בלשפר את ההבנה המקצועית שלהם פשוט מדברים שטויות. אחרי חצי שעה של קשקשת, הם עוצרים כדי להכיר בעובדה ש"בעצם, לא ממש הגדרנו מה זה איכות". תכל'ס? זה לא מפתיע - די קשה להגדיר "איכות תוכנה". זה מושג עם הגדרה מעורפלת <a href="https://always-fearful.blogspot.com/2019/03/quaity-in-closet-heb.html" target="_blank"> שפשוט לא מתיישרת עם הגדרות מתחומים אחרים</a>. התוצאה? כשמנהל אחד אומר "יש לנו בעיית איכות" הוא לא מדבר על אותו דבר כמו מנהל המוצר שרוצה להוציא "מוצר באיכות מעולה" ושתי ההגדרות האלה בטח לא מתיישבות עם ההגדרה הנפוצה "ערך עבור מישהו שאכפ לנו ממנו" (Value to someone who matters) כמו שאמר ג'יימס באך בעקבות ג'רי ויינברג, או ההגדרות שאפשר למצוא בתקן ISO "התאמה לשימוש" או "השבעת רצון הלקוחות". אם ננסה לעבוד כשיש לנו תחושה כוזבת של הסכמה, זה רק יגרום לחיכוכים אחר כך,</p><p dir="rtl" style="text-align: right;">כל כאב הראש הזה אומר ש"שיפור האיכות במוצר" זו הכרזה ריקה מתוכן, כזו שאי אפשר לפעול לפיה. אם אכן יש רצון לשפר את "איכות" המוצר, יש בפנינו עבודה לא בהכרח קלה של הגדרת התחומים בהם נרצה להשתפר. תוך כדי התהליך הזה נגלה שחלק מהאנשים מבינים את המושג "איכות" כהפגנת מיומנות - שימוש בטכנולוגיות העדכניות באופן אלגנטי ויעיל, אחרים יסתכלו על תוצאות עסקיות, או על חווית המשתמש או על מרווח בין תקלות. כל הדברים האלה חשובים, אבל כדי לזוז קדימה, אנחנו צריכים לדבר על אותו דבר. </p><p dir="rtl" style="text-align: right;">אבחנה מילונית נחמדה בה נתקלתי מתישהו לאורך חיי היא שמשמעות אחת למילה "איכות" היא פשוט שם אחר ל"תכונה", זה אמנם טבעי יותר באנגלית, אבל גם בעברית האמירה "יש לו מגוון איכויות" מובנת בלי להיות העתקה בוטה מדי מאנגלית. דווקא הריבוי הזה נראה לי כמו נקודת מוצא נוחה מהתסבוכת הזו. אם במקום לדבר על "איכות" נוכל לדבר על "תכונות", או אפיל על "תכונות רצויות", נוכל לשבור את הדיכוטומיה סביב "איכות גבוהה\ירודה" ולהתחיל להתמקד במה שחשוב לנו - בלבחור את האיזון שמתאים לנו.</p><p dir="rtl" style="text-align: right;">ואם "איכות" היא כזו מילה מגונה, במה יהיה לנו נכון יותר להשתמש? כמו שרמזתי בפסקה הקודמת, אני מעדיף לדבר על מכלול התכונות הרצויות ואלו שאינן. ככה אפשר למנוע בלבול ובאשכרה להתקדם לקראת המטרה אליה אנחנו חותרים. כמובן, זה לא כזה פשוט - רשימת התכונות הזו אינה סגורה, אבל הנה כמה תכונות שנראות לי רלוונטיות במגוון מוצרים, וכל אחד יוכל להוסיף מתוך ההקשר שלו. </p><ul dir="rtl" style="text-align: right;"><li>נכונות: עד כמה התוכנה עושה את מה שהיא אמורה לעשות. מחשבון מס לא מגיע לסכומים לא נכונים, משחק מגיב לפקודות מהשלט בצורה הצפויה ודברים כאלה. </li><li>עמידות: עד כמה התוכנה יכולה לתפקד לאורך זמן בעולם האמיתי, גם לנוכח משתמשים שמנסים להתקיף אותה</li><li>סילומיות: תכל'ס, חיפשתי באינטרנט את המילה הזו, אנשים נורמלים יקראו לזה סקיילבליות או משהו כזה. עד כמה התוכנה שלנו מסוגלת לגדול כדי להכיל את סדרי הגודל הצפויים בעתיד הקרוב והבינוני. </li><li>ביצועים:אישית אני דוחס לכאן גם את התשובה לשאלה "כמה מהר" וגם את המשאבים שהמערכת צורכת. אם זה רלוונטי, אפשר להפריד את שני אלה כדי שנוכל לשלם באחד כדי לקבל את השני. </li><li>פרטיות: עד כמה התוכנה מגנה על מידע אנושי שהיא נחשפת אליו. </li><li>נוחות וקלות שימוש: כמה טוב אנשים יכולים להשיג את המטרות שלהם בעזרת המוצר שלנו. </li><li>אטרקטיביות: כמה המוצר מוצא חן בעיני המשתמשים. ג'יימס באך משתמש במושג "כריזמה" כדי לתאר את זה. <br /></li><li>נהירות:עד כמה קל לנו להסביר מה התוכנה שלנו עושה ועד כמה קל לנו לצפות את ההתנהגות שלה במצבים שונים. </li><li>נראות: עד כמה אנחנו יכולים לדעת מה קורה עם המוצר שלנו בעודו רץ? האם יש לנו מידע מהשטח שאנחנו יכולים להשתמש בו כדי לשפר את המוצר? כדי לאתר בעיות?</li></ul><p dir="rtl" style="text-align: right;">אפשר להרחיב או לשנות את הרשימה הזו, ויש כבר מי שעשו דברים כאלה (למשל, אדגרן, אמילסון ויאנסון הרחיבו <a href="http://thetesteye.com/posters/TheTestEye_SoftwareQualityCharacteristics.pdf" target="_blank">כאן</a> את העבודה של ג'יימס באך), אבל אני רוצה להפנות לרגע את תשומת הלב לתכונה אחת שאני לא כולל בתוך התכונות שאנשים רוצים: בדיקתיות. הסיבה לכך היא שיש מעט מאוד ערך בבדיקתיות בפני עצמה. הדרך בה אני חושב על בדיקתיות היא כעל יעד עזר: זה כנראה יהיה משהו שנרצה כדי להשיג מטרה אחרת, או שזו תהיה תוצאת לוואי של תכונה קיימת (למשל, אם שיפרנו את נהירות התוכנה, ממילא שיפרנו את היכולת שלנו לבדוק אותה, ויהיה לנו קשה מאוד לשמור על רמת נכונות טובה אם המוצר שלנו יהיה קשה מאוד לבדיקה). יש תכונות נוספות שאפשר להכניס לתוך הקטגוריה הזו - כמו תחזוקתיות או יכולת הרחבה, אבל זה לגמרי עניין של מי שמרכיב את הרשימה. <br />חוץ מזה, יש סוג אחר של פריטים שלא כללתי ברשימה - תכונות תהליך. דברים כמו עקביות, או צפיות (כמה אנחנו יודעים לומר מה ייקח לנו כמה זמן). זה לא שהם לא חשובים, אלא שהייתי צריך למתוח את הקו איפשהו, ולכן בחרתי להתמקד בתכונות של הקוד עצמו ולא של תהליך הפיתוח. זה גם מתיישר עם התפיסה של "מוצר איכותי" - זה לא משנה מה התהליך שהוביל ליצירת מוצר כלשהו. אם הוא טוב, אז הוא טוב. </p><p dir="rtl" style="text-align: right;">לכן, בפעם הבאה בה מישהו שואל "איך אפשר לשפר את איכות המוצר", עצרו לרגע כדי לחשוב למה בדיוק הוא מתכוון. האם יש לנו תקלות פונקציונליות? האם יש לנו בעיות אבטחה? האם לקוחות מתלוננים בהמוצר שלנו זוחל? כמובן, אין צורך (או טעם) לריב על טרמינולוגיה ולהתחיל להסביר "אל תשתמש במילה 'איכות', היא חסרת משמעות ומבלבלת", אבל מאוד כדאי לצלול טיפה יותר לעומק ולשאול שאלות מנחות כמו "מה התסמין הכי בולט של בעיות שיש לנו כרגע?" או "האם כדאי לנו להשקיע מאמץ בניסיון למצוא טעויות לפני הלקוחות או שיש דברים בוערים יותר לעשות?" אחרי שיש לנו כמה תשובות כאלה, אפשר להתחיל לעבוד. על הדרך, בזמן רגוע יותר, אפשר להתחיל לדבר על התכונות שמרכיבות "איכות" מבחינתנו. </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-16451131425821829852022-11-21T02:10:00.008+02:002022-11-21T19:51:45.490+02:00לאן מתקדם בודק תוכנה?<p dir="rtl" style="text-align: center;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhDo1x5ZTU0ELN8dTyQ5FUSFiUkG1mNhKHmpvQdMtalFlugqOo6XNsSHZ5btHD_lCVWC1sExsvHVFbDWzfa4Jsj1Xvfd6cy4pRoRwT_pfMv3n0RlN3uiwbQud3Nxul8J1nH9XTe_CR2DdJpEDrAjk7C-2HJ8y_NxChqErIhQImZLnZ6Dm6Uvl_i_ff8/s699/paths.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="498" data-original-width="699" height="285" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhDo1x5ZTU0ELN8dTyQ5FUSFiUkG1mNhKHmpvQdMtalFlugqOo6XNsSHZ5btHD_lCVWC1sExsvHVFbDWzfa4Jsj1Xvfd6cy4pRoRwT_pfMv3n0RlN3uiwbQud3Nxul8J1nH9XTe_CR2DdJpEDrAjk7C-2HJ8y_NxChqErIhQImZLnZ6Dm6Uvl_i_ff8/w400-h285/paths.jpg" width="400" /></a></div><br /><p dir="rtl" style="text-align: center;"><br /> </p><p dir="rtl" style="text-align: left;"><a href="https://always-fearful.blogspot.com/2022/11/whats-career-path-of-tester.html">English version<br /></a></p><p dir="rtl" style="text-align: right;"><a href="https://docs.google.com/document/d/1TE_7Xg71sDJplFRxHYiFivclhDvx1gd7IBH_C1Z7mno/edit?usp=sharing" target="_blank">קישור למודל</a><br /></p><p dir="rtl" style="text-align: right;">כשמתכנת מתחיל את דרכו המקצועית יש עשרים אלף אופציות בהן הקריירה יכולה להתפתח - מערכות משובצות, שרתי-אינטרנט, מערכות הפעלה,מובייל, ועוד, בנוסף, כל תחום כזה יכול לבוא יחד עם התמחות בשפות תכנות ספציפיות כמו ג'אווה, סי, פיית'ון ואחרות. מתכנת מתחיל ימצא את עצמו בדרך כלל בצוות עם עוד מתכנתים שיש להם קצת יותר ניסיון ויתחיל לשמוע על כל הדברים שחסרים לו - כתיבת קוד מאובטח, תבניות עיצוב או היכרות עם ספריה כזו או אחרת. בכל פעם בה מתכנת מסיים קטע עבודה הוא מקבל משוב על טיבה - מישהו אחר בצוות יקרא את הקוד שלו, יעיר הערות ושני הצדדים ילמדו. אחרי זמן לא ארוך במיוחד יהיו למתכנת הזדמנויות לחזור לקוד שהוא כתב בעבר ולראות כמה הוא כבר השתפר ואיך טעויות שהוא עשה אז מפריעות לו עכשיו. בסך הכל - סביבת למידה מדהימה, גם אם אנחנו לא לוקחים בחשבון את מאות הקורסים הזמינים שמלמדים תוכן רלוונטי ברמה המתאימה. </p><p dir="rtl" style="text-align: right;">ומה המצב אצל בודקי תוכנה? אפשר לומר שהוא כמעט הפוך. לא מאוד נדיר שבודק תוכנה מתחיל ימצא את המשרה הראשונה שלו באיזה סטארט-אפ בו הוא בודק התוכנה היחיד, או היחיד בצוות אליו הוא גוייס, רוב מי שהוא ייתקל בו, גם בודקי תוכנה מנוסים יותר, לא יודעים הרבה על הצד התיאורטי של בדיקות תוכנה, ובלא מעט מקומות, בודקי תוכנה נמצאים אי שם בחלק התחתון של שרשרת המזון. כמעט כל מי שמדבר על "לאן מתקדמים" מצביע על דרך התקדמות אל מחוץ למקצוע - לפיתוח, דבאופס, הנהלת מוצר או "סתם" לתפקיד ניהולי. אילו כישורים נדרשים מאדם כדי להיות בודק תוכנה מוצלח? יש כאן הרבה פרשנות ומעט מאוד קונצנזוס. אילו קורסים זמינים למי שרוצה להשתפר? יש עשרות קורסים מסביב לתוכן של ISTQB, אבל התוכן הזה פשוט לא טוב, ובטח לא משפר את המקצועיות של מי שטורח ולומד אותו (למה אני אומר את זה? אפשר לקרוא <a href="https://always-fearful.blogspot.com/2018/07/things-you-cant-polish-until-shine.html" target="_blank">כאן</a>). קורסים טובים? צריך לחפור מתחת לסלע. זה לא שאין בכלל, קיבלתי המלצות על כמה קורסים, אבל זה לא טריוויאלי למצוא אותם. ומבחינת משוב - גם כאן אין כזה באמת. לפעמים משהו מתפוצץ בשטח ואנחנו אומרים "כן, יכולנו לעשות עבודה טובה יותר", אבל מה עם כל הפעמים בהן לא היינו בסדר ושום דבר דרמטי לא קרה? או כשהיינו זהירים מדי ותקענו מקלות בגלגלים של כל תהליך הפיתוח? בדרך כלל המשוב שנקבל יהיה ספורדי, באיחור ודי מעורפל. גם כאן, יש את יחידי הסגולה שמצאו דרך לקבל משוב מהיר מהשטח על איכות הפיתוח - הם בדרך כלל לא באמת צריכים בודקי תוכנה. <br />בקיצור, מנקודת המבט של בודקי תוכנה מתחילים, כל מסלולי הקידום מובילים אל מחוץ למקצוע הבדיקות.חלק קטן מהבודקים האלה ימצאו במקרה מסלולים בתוך עולם הבדיקות (וחלק מתוך אלה יתקדמו בהם עד לנקודה בה תיאורי תפקיד רשמיים לא משנים מאוד), אבל רוב העובדים שטובים מספיק כדי להתקדם יכוונו את הקריירה שלהם למקום אחר. מקומות עבודה שמחפשים בודקי תוכנה מנוסים יצטרכו להתאמץ מאוד כדי למצוא מישהו שגם פנוי לעבודה, גם מנוסה וגם מסוגל לעשות עבודה טובה. </p><p dir="rtl" style="text-align: right;">במקום בו אני עובד התמודדנו עם הקושי הזה על ידי גיוס עובדים מתחילים, תחת ההנחה שקל יותר ללמד אנשים איך להיות בודקי תוכנה טובים אם לא צריך לשבור להם הרגלים רעים. כמובן, ידענו שיש כמה חסרונות לגישה הזו - נצטרך להשקיע יותר בחניכה ואנחנו מהמרים על כל עובד בלי המון יכולת לנחש את סיכויי ההצלחה שלו מראש. הנחנו שנוכל להתמודד עם האתגרים האלה ולבנות צוות שיתאים למה שהחברה צריכה. אחד האתגרים שלא לקחנו בחשבון היה שימור עובדים. זה לא שהתעלמנו לחלוטין מהבעיה הזו, רק הנחנו שהקושי לשמר עובד יהיה דומה לזה שכל החברה חווה. נכון, לבדיקות תוכנה יש מוניטין לא טוב במיוחד, אבל אנחנו בונים צוות חזק שמאפשר לאנשים ללמוד המון ולהשפיע על הסביבה שלהם - הם יראו את זה ויישארו. לא? ובכן, מסתבר שלא מספיק. בודקי תוכנה מתחילים הגיעו למקצוע ממגוון סיבות, חלק גדול יחסית פשוט מנצל את רף הכניסה הנמוך יותר כדי לצבור ניסיון לקראת המשרה אליה הוא באמת מכוון. לא מעט מאלו שעזבו אותנו עשו את זה כי מנקודת המבט שלהם - אין לאן להתקדם. הם ידעו לומר שהם מתכנתים טובים יותר, אבל כל שאר הכישורים שהם צברו היו שקופים להם, וחשוב יותר - הם לא שיפרו את היכולת שלהם למצוא משרדה טובה יותר בהמשך הקריירה. משרות לפיתוח תוכנה יהיו ספציפיות - מומחה מסדי נתונים, מומחה מערכות משובצות, קרנל, או עוד עשרות תחומים. משרות לבודקי תוכנה יהיו דומות להחריד, לא משנה אם הן לבעלי ניסיון של שנתיים או של עשר והיכולת לומר "זה אני, זה הערך שאני מביא איתי ואלה המשרות שמעניינות אותי" דורשת חשיבה מעמיקה שלא זמינה בתחילת הקריירה, בטח לא במקום שעדיין בונה את תהליכי העבודה והקידום שלו. </p><p dir="rtl" style="text-align: right;">אני רוצה לבנות מפה, משהו שיתאר מסלולי התקדמות אפשריים בתוך בדיקות תוכנה. משהו שיאפשר לאנשים להסתכל ולומר "אלה תחומי ההתמחות שלי". כלי כזה יוכל לתת לעובדים מתחילים תחושת התקדמות והתפתחות - במקום לומר
"קודם בדקתי, היום אני בודק, מחר אבדוק", הם יוכלו לומר "קודם ידעתי את
הבסיס, היום אני יודע קצת על בדיקות נגישות, קצת יותר על בדיקות ביצועים
ואני טוב יותר בניתוח סיכונים מאשר הייתי לפני שנה". יותר מזה, הם יוכלו
לומר "כשאהיה גדול אני רוצה להיות מומחה ניטור, ולכן אני צריך ללמוד
ויזואליציה, עבודה עם נתוני עתק ואסטרטגיות אחסון מידע". עם קצת מזל, זה יכול להיות כלי שישמש חברות כשהן בונות מסלולי התקדמות לעובדים או אפילו בתהליך הגיוס שלהן. <br /></p><p dir="rtl" style="text-align: right;">כדי לתמוך במטרות האלה, בניתי מודל שמכיל שלוש רמות של הגדרות:</p><p dir="rtl" style="text-align: right;">1. בכירות.<br />חלוקה כללית לדרגות - מי מקבל יותר כסף ויותר אחריות. אם אלה הדברים שמעניינים אותך (ולא, זה לא בהכרח עניין של שטחיות או סתם חמדנות - יש אנשים שנמצאים בתפקיד אחר: משאבי אנוש, כספים, ואפילו המנכ"ל - לא צריך להיות להם אכפת מה בדיוק אומר התפקיד שלך, רק כמה הוא בכיר), אפשר לעצור כאן. אבל, גם לכולנו נוח מדי פעם להכיר את החלוקה לרמות, גם במובן של שכר וגם כדי לדעת מה סוג האחריות שאנחנו צפויים לה. <br />במודל השתמשתי בשלוש רמות - ג'וניור, סיניור וprinciple (ולא, לא היה לי הגיוני לכתוב את זה בעברית) כשההפרדה היא לפי מידת השפעה מרמת הוא שעושה את העבודה שלו ועד לזה שמשפיע על כל הארגון. ככל הנראה, ארגונים קטנים מאוד יצטרכו רק שתי קטגוריות, ובארגוני ענק יהיה צורך ברמות נוספות.<br /></p><p dir="rtl" style="text-align: right;">2. תפקידים: אלה כותרות כלליות לסוגי עבודה שונים שאפשר לכלול תחת המטרייה הרחבה של "בדיקות תוכנה". בארגוני ענק יכול להיות שיהיו צוותים שונים שממלאים את הפונקציות האלה, אבל במקומות קטנים יותר (ובארגונים גדולים שבחרו לארגן את העבודה כך), סביר שאדם אחד ימלא מספר תפקידים. למשל - ארגון לא חייב להחזיק מומחה נגישות במשרה מלאה, אבל כדאי שיהיה מישהו שמבין בתחום ויכול לעזור במשימות קשורות כאשר הן צצות. </p><p dir="rtl" style="text-align: right;">3. כישורים ויכולות: כדי לבצע תפקיד בהצלחה, נדרשים כישורים מסויימים. יהיה קשה מאוד לתפקד כמומחה ביצועים בלי להכיר כלי או שניים שמייצרים עומסים או בלי לדעת לנתח צריכת משאבים. עבור כל כישור ניתן לבנות מסלולי הכשרה - ביצוע משימות קשורות, קורסים, הרצאות וכדומה, ולהגדיר דרכים למדידת המיומנות של עובד בכישור מסויים. </p><p dir="rtl" style="text-align: right;">העבודה שלי, באופן טבעי, רחוקה מלהיות מושלמת. אני משתמש בשפה שמובנת בעיקר לי, ואין ספק שאני מחמיץ תחומים שלמים. לכן, אני מבקש מכם להוסיף הערות - הייתי שמח אם המודל הזה יהיה משותף לקהילה כולה ויוכל לשמש גם את מקומות העבודה השונים בהגדרת התפקידים שהם מחפשים ומסלול ההתקדמות וגם את בודקי התוכנה עצמם כשהם מנסים להגדיר לעצמם את מסלול ההתפתחות שהם רוצים לנסות. המודל שלי נמצא <a href="https://docs.google.com/document/d/1TE_7Xg71sDJplFRxHYiFivclhDvx1gd7IBH_C1Z7mno/edit?usp=sharing" target="_blank">כאן</a> ויש הרשאות הערה לכל מי שניגש אליו. ספרו לי מה עובד בשבילכם היטב, אילו הנחות אני מניח ולא מתאימות לכם, מה חסר. שתפו את המודל הזה עם קולגות וחברים כדי שגם הם ייתנו משוב. זו גם ההזדמנות להודות ל<a href="https://twitter.com/perze" target="_blank">Perze Ababa</a> שהשקיע לא מעט מזמנו ועזר לי לשים לב לכמה פערים ולראות גם חלק מנקודת המבט של ארגונים גדולים באמת. </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-40648006476362876352022-11-21T02:10:00.007+02:002022-11-21T19:49:56.272+02:00What's the career path of a tester?<p dir="rtl" style="text-align: right;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGLR6Q2MQAcJA5GuT2teSt3nVj4pbiALTZ-7IVyFKW71lMRfr90sMMBGiTrP7T8H0VEyILILiYyozAVKPQbwtzq9woJvTKA7coNuh_yZyPyK-eD9peyvBiySJhLIpbiiH-a4E-TPS2TDx_J9NNR4NikxjNZXoYAFPeEWyuRemqTbXGXLW-YOF9r8vC/s699/paths.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="498" data-original-width="699" height="285" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGLR6Q2MQAcJA5GuT2teSt3nVj4pbiALTZ-7IVyFKW71lMRfr90sMMBGiTrP7T8H0VEyILILiYyozAVKPQbwtzq9woJvTKA7coNuh_yZyPyK-eD9peyvBiySJhLIpbiiH-a4E-TPS2TDx_J9NNR4NikxjNZXoYAFPeEWyuRemqTbXGXLW-YOF9r8vC/w400-h285/paths.jpg" width="400" /></a></div><p><a href="https://always-fearful.blogspot.com/2022/11/whats-career-path-of-tester-heb.html" target="_blank">עברית</a></p><p><a href="https://docs.google.com/document/d/1TE_7Xg71sDJplFRxHYiFivclhDvx1gd7IBH_C1Z7mno/edit?usp=sharing" target="_blank">Link to the model</a> <br /></p><p></p><p> When programmers set their foot on their first job, there are quite a lot of options they can see and choose to specialize in: Embedded systems or highly distributed cloud architectures, specific programming language such as Java, Python or c, optimization and debugging, and that's even before we consider business domains. They will often find themselves with more experienced developers who will give them feedback on their work during design & code reviews, who will talk about concepts they still need to master such as secure code design, efficiency or design philosophies. They will have plenty of opportunities to go back to a piece of code they wrote and experience their mistakes biting in their respective behinds, just so that they can see how much progress they have made. All in all, that's an amazing learning environment even before we consider the plethora of easy to find courses and online resources that teach relevant skills in the appropriate level. </p><p>How is it for software testers? Well, one might say it's the complete opposite. It's not uncommon for testers to find themselves the sole tester in a team, or even in the whole start-up that hired them. Most of the people they meet, including some more experienced testers) won't know a lot about testing and in many places testers are so down the pecking order that it can seem that any career progress is pointing outside of testing - to programming, devOps, product management, or just to being a manager. What skills are required to function well as a function tester? One can find as many opinions as there are people, and most of them won't be actionable. Courses? By a simple search one will stumble upon a lot of ISTQB content, but this content is the opposite of helpful as it provides misconceptions and bad thought patterns (more on that <a href="https://always-fearful.blogspot.com/2018/07/things-you-cant-polish-until-shine.html">here</a>). There is some good content out there (I've heard good recommendations on BBST and RST, but can't attest yet to their content personally), but if you can find it - you have already burrowed deep enough into the rabbit hole that is the very specific testing community where I've been around for a while now. As for feedback on our work - what feedback? With the exception of an occasional blaming finger of "how did this bug escaped QA" (which we try to avoid), there's very little feedback on testing done right or wrong. What about the times where we were slacking but by chance there wasn't any critical blowup that happened? Or when we were too careful and slowed down the entire development process? Yes, there are outliers that have managed to build a way to get good, reliable feedback on their product from the real world, where it's deployed. But guess what? They usually don't really need testers.<br />In short, taking the view of an inexperienced software tester, it looks as if all growth paths are leading outside of testing. A small part of those testers will manage to find their way inside the testing world (and some of those will make progress to the level where formal titles do not limit them and matter a lot less) but most capable employees will find a way out of testing. As a result, places looking for experienced, high-skilled testers will have a hard time finding them, and those testers will have a difficult time finding a workplace that will appreciate their skills.</p><p>Where I work, we decided to tackle this difficulty initially by looking for inexperienced employees, assuming that it will be easier to teach people good testing if there aren't any bad habits that needs to be broken. We knew that this plan has some drawbacks: We'll have to invest more in training, and we are taking a gamble on their ability to develop the skills we need - which is more difficult than assessing the existing skills of a person. Those were challenges we assumed we could overcome. Naturally, we did not foresee all of the challenges we would face. One of which was employee attrition. To be fair, it's not that we completely ignored this risk, we just assumed that it would be at the same rate as in other teams in the company. Sure, software testing has bad reputation, but we're building a strong team and we can show employees that they can grow significantly and stay. <br />Well... no. Inexperienced software testers apply for their first (or second) positions for many reasons, and the most prominent I've encountered is that Software testing has a lower entry bar to "high-tech" jobs, and they assumed (with some degree of truth in it) that it would provide them with a good stepping stone towards the position they actually aim for. Talking with people who left us raised a common pattern: They decided that they can't build their career in testing, and since they did gain some relevant skills, they were ready to make a transition. They also didn't feel they were improving as testers - they could say they are better coders, but the rest of the skills they improved were invisible to them. More importantly, they couldn't see how those skills would help them find their next, hopefully better, job. This brings me back to the job market: Developer jobs are specific (see the list at the top for examples). Testing jobs are almost always "tester\QA\QE\SDET" - nothing that will help differentiate between testers with different experience. It means that the ability to articulate one's added value or interests requires deep thought that is not easy to do at the beginning of one's career, especially if they work in a place that is still building its career ladders and work procedures. </p><p>I want to build a map. Something that will describe possible progress paths inside the role of an individual contributor software tester. It might have exit paths to other roles or to management , but the focus is on software testers and the various kinds of tasks they might perform. Such a map will enable people to look and say "Those are my areas of expertise, this is what I want to do next". For beginners, it can help visualizing their progress, they could look and say "A year ago I had some basic fundamental skills, since I've gained knowledge of accessibility testing, delved a bit deeper into load generation and log filtering and I'm better at risk assessment than I was then". They could then go and say something like "I enjoyed trying to make sense out of heaps of data, so I might want to look into production monitoring and deeper into performance tests, for both I will need skills in data visualization, working with big data and data storage and retention strategies.<br />With some luck, companies could use it when they define their career ladders, or even in their recruiting processes and ads which will lead to more diversity in hiring "tester".<br /><br />To support those goals, I built a three layered model:</p><ol style="text-align: left;"><li> Seniority. <br />A generic, non specialized, catch-it-all division. People who only care about prestige, compensation and responsibility can stop at this level (no, they are not shallow or indifferent, they might be from other professions, such as finance, HR or even your CEO), for the rest of us, it would still be helpful to know which level is any role - both in terms of compensation and in terms of the type of skills that would be relevant to it.<br />In the model I use 3 such categories - Junior, Senior and Principle that represent ever growing sphere of influence - from just doing one's own job, to having a company-wide impact. Smaller companies might only need two such categories, while larger corporations might need more.</li><li> Roles.<br />Those are titles to the types of work people with the title of "tester" are often doing and are not considered a distinct role (so, doing part time customer support does not count). Again, the size of the organization matters. Larger ones can have teams that focus on one such role, while smaller places, or corporations that chose to organize their work differently, will have people wearing multiple hats. For instance, not every place needs or can afford an accessibility expert, but most places can benefit from someone who has some accessibility expertise and help the rest of the team(s). </li><li>Skills and capabilities.<br />In order to be successful in each role, certain skills are needed. Since those are the things one can learn intentionally breaking down the roles to specific key competencies will be helpful to people who try to make their way through a specific career path. At a later phase, each such skill can be augmented with resources that can help acquiring it, and companies can decide on ways to measure them to decide whether someone has achieved the necessary level of a skill for the step they want to make. </li></ol><p>My work, quite naturally, is far from being perfect or complete, I'm using language and terminology that makes sense to me, and I'm most certainly missing entire fields. This is the point where I'm asking for your help: I would be happy if this could become a model that is developed by the community and will be usable by both companies defining their processes and individuals crafting their career. <br />My model can be found <a href="https://docs.google.com/document/d/1TE_7Xg71sDJplFRxHYiFivclhDvx1gd7IBH_C1Z7mno/edit?usp=sharing" target="_blank">here</a> and anyone with access can comment. Tell me what works for you, what does not, which assumptions that I make are not suitable for you and what you can see missing there. <br />This is also the place to thank <a href="https://twitter.com/perze" target="_blank">Perze Ababa</a> that contributed from his time and helped me notice some key insights I was ignoring, as well as see the perspective of the larger corporate for a while. <br /></p><p> </p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-10227456067081708692022-10-14T13:52:00.006+03:002022-10-14T13:52:51.986+03:00Testing Magic 101<p> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.pixabay.com/photo/2017/06/26/14/17/child-2443969_960_720.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="532" data-original-width="800" height="213" src="https://cdn.pixabay.com/photo/2017/06/26/14/17/child-2443969_960_720.jpg" width="320" /></a></div><p></p><p></p><p dir="rtl" style="text-align: right;"><a href="https://always-fearful.blogspot.com/2022/10/testing-magic-101-heb.html">עברית </a><br /></p><p> Every now and then, I get to hear about "X testing", where X might be API, mobile, embedded software or just anything that isn't a website. While I normally scoff at the relevance of such distinctions that usually can be narrowed to "understand your system, understand how to interact with it, the rest of the differences are nothing you haven't seen before", it is true that the good content will help you notice parameters you possibly didn't consider such as battery consumption for mobile, or heat and power fluctuation for embedded systems.</p><p>I got to listen to a recent episode of "<a href="https://qualitestgroup.com/insights/podcasts/" target="_blank">The testing show</a>" on AI testing which was, sadly, another one of the infomercial-like episode. It wasn't as terrible as the ones they bring in people from Qualitest, but definitely not one of their better ones. The topic was "AI\ML testing", and was mainly an iteration of the common pattern of "it's something completely new, and look at the challenges around here, also ML is really tricky".<br /></p><p>This has prompted me to write this post and try to lay the basics for testing stuff related to ML, at least from my perspective. </p><p>The first thing you need to know is what you are testing - are you testing an ML engine? A product using (or even based on) an ML solution? the two can be quite different. </p><p>For the past decade, I've been testing a product that was based on some sort of machine learning - first a Bayesian risk-engine detecting credit-card fraud, and then an endpoint protection solution based on a deep neural net to detect (and then block) malware. The systems are quite different from each other, but they do have one aspect that was shared to them - The ML component was a complete black-box, not different than any 3rd party library that was included in it. True, that particular 3rd part was developed in-house and was the key differentiator between us and the competition, but even when you have a perfect answer to the relevant question (is this transaction fraudulent? is this file malicious?), there's still a lot of work to do in order to make that into a product - For the endpoint protection product it would be hooking to the filesystem to identify file-writes, identifying the file type, quarantining malicious ones and reporting the attack, all of which should be done smoothly while not taking up too much resources from the endpoint itself, not to mention the challenge of supporting various OS and versions deployed in the field. All of which have zero connection to the ML engine that powers everything. If you find yourself in a position similar to this (and for most products, this is exactly the case) - you are not testing ML, you are at most integrating with one, and can treat the actual ML component as a black box and even replace it with a simulator in most test scenarios.</p><p>There are cases, however, that one might find themselves actually testing the ML engine itself, in which case start by admittin that you don't have the necessary qualifications to do so (unless, of course, you do). Following that, we need to distinguish again between two kinds of algorithms - straightforward and opaque. </p><p>Straightforward algorithms are not necessarily simple, but a human can understand and predict their outcome given a specific input. For instance, in the first place I've been the ML was a Bayesian model with a few dozen parameters. The team testing the risk engine was using a lot of synthetic data - given specific weights for each "bucket" and a given input, verify the output is exactly X. In such cases, each step can be verified by regular functional tests they might require some math, but if a test fails we can see exactly where did the failure happen. In a Bayesian case, calculating a weighted score, normalizing it, recalculating "buckets" and assigning new weights are all separate, understandable steps that can be verified. If your algorithm is a straightforward one, "regular" testing is just what you need. You might need a lot of test data, but in order to verify the engine correctness, you just need to understand the rules by which it functions. </p><p>Opaque ML systems are a different creature. While it is possible to define the expected output of the algorithm given the state it's in (unless it also has a random effect as well), there's use to actually finding them since it would not help us understand why something was a specific answer was given. Notoriously, there are the deep neural networks that are nothing short of magic. We can explain the algorithm of transitioning between layers, the exact nature of the back-propagation function we use and the connections between "neurons", but even if we spot a mistake, there isn't much we can do besides feeding it through the back-propagation function and move on to the next data point.In fact, this is exactly what is being done on a massive scale while "training" the neural net. With opaque systems testing is basically the way they are created, so accepting them as fault-free is the best we can do. </p><p>That being said, ML algorithms are rarely fault free, and this brings us to the point we mentioned before - most of our product is about <b>integrating</b> ML component(s) to our system and we should focus on that The first thing is to see whether we can untangle it from the rest of our system. We could either mock the response we expect or use input that is known to provide certain result and see that our system works as expected given a specific result from the ML component.</p><p>Clever, just assume that correctness is someone else's problem, and we're all peachy. Right? <br />Well, not exactly. Even though in most cases there will be a team of data scientists (or is it data engineers now?) who are building and tuning the model, there are cases where we actually need to cover some gaps in figuring out whether it's good enough - Is our model actually as good as we think it is? Maybe we've purchased the ML component and we take the vendor's claims with a grain of salt. <br /></p><p>A lot of the potential faults can be spotted when widening the scope from pure functionality to the wider world. Depending on what our ML solution does, there's a plethora of risks - from your <a href="https://www.huffpost.com/entry/microsoft-tay-racist-tweets_n_56f3e678e4b04c4c37615502" target="_blank">chatbot turning Nazi</a> to <a href="https://www.cbsnews.com/sacramento/news/google-photos-identifies-two-black-users-as-gorillas/">describing black people as apes</a> to <a href="https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G">being gender-biased in hiring</a>, that's without considering <a href="https://arstechnica.com/cars/2020/01/how-a-300-projector-can-fool-teslas-autopilot/">deliberate attacks that will run your self-driving car to the ditch</a>. To avoid stupid mistakes that make us all look bad in retrospect, I like to go through a list of sanity questions - ones that have probably been addressed by the experts who've built this system, but just in case they got tunnel vision and forgot something - The list is quite short, and I'm probably missing a few key questions, but here's what I have in mind:</p><ul style="text-align: left;"><li>Does it even makes sense? Some claims (such as "<a href="https://techxplore.com/news/2016-11-reliable-inference-criminality.html" target="_blank">Identifying criminals by facial features</a>") are absurd even before we dive deeper into the data to find the problems that make the results superficially convincing). <br /></li><li>The "Tay" question: Does the software continues to learn once in production? If it does - how trustworthy is the data? what kind of effort would be needed to subvert the learning ?</li><li>The Gorillas question: Where did we get the training data from? is it representative of what we're expecting to see in production?</li><li>Our world sucks question: Is there a real world bias that we might be encoding into our software? Teaching software to learn from human biased decisions will only serve to give this bias a stamp of algorithmic approval. <br /></li><li>Pick on the poor question: Will this software create a misleading feedback loop? This idea came from Cathy O'Neil's "<a href="https://www.goodreads.com/book/show/28186015-weapons-of-math-destruction" target="_blank">Weapons of Math Destruction</a>" - Predictive policing algorithms meant that cops were sent to crime ridden neighborhoods, which is great. But now that there are more cops there, they will find more crimes - from drunken driving to petty crimes or jaywalking. So even if those areas are back to normal rate of crime, it will still get more attention from the police and will make the life of the residents there more difficult. </li><li>"Shh.. don't tell" question: Is the model using data that is unlawful to use? Is it using proxy measures to infer it? Imagine an alternative ML based credit score calculator. It helps those who don't have a traditional credit score to get better conditions for their credit. Can it factor in their sexual preference? And if they agree to disclose their social profiles for analysis, can we stop the algorithm from inferring their sexual preferences?<br /></li></ul><p>After asking those questions (that really should be asked before starting to build the solution, and every now and then afterwards), and understanding our model a bit better, we can come back to try and imagine risks to our system. In security testing (and more specifically, threat modeling) there's a method of identifying risks called "movie plotting" where we assemble a diverse team and ask them to plot attacks in a movie like "mission impossible". This idea could work well to identify risks in incorporating ML components to our business, with the only change is that the movie plot will be inspired by to "Terminator" or "The Matrix" <br /></p><p>And yet, the problem still remains: Validating ML solutions is difficult and requires a different training than what most software testers have (or need). There are two tricks that I think can be useful.</p><ol style="text-align: left;"><li>Find an imperfect oracle: it could be that you have competitors that provide similar service, or that there's a human feedback on the expected outcome (you could even employ the Mechanical Turk). Select new (or recent) data points and compare your system results with the oracle ones - The oracle should be chosen such that every mismatch is not necessarily a problem, but that it's something that is worth investigating. Keep track on the percentage of differences. If it changes drastically, something is likely wrong on your side. Investigate a few differences to see if your oracle is good enough. In our case, we try a bunch of files that enough of our competitors claim to be malicious, and if we differ from that consensus, we assume it's a bug until proven otherwise. <br /></li><li>Visualize. Sometimes, finding an oracle is not feasible. On the other hand, it could be that people can easily spot problems. Imagine that Google were placing a screen in several offices and projecting crawled images with what the ML thinks it actually is. It is possible that an employee would have seen it classifies people as apes way before real people were offended by it. </li></ol><p>With that being said, I want to circle back to where I've started: While I hope you've gained an idea or two, testing is testing is testing. With any sort of system that you test, you should start by understanding just enough about it and how it interacts with your business needs, then figure out the risks that you care about and find the proper ways to address those risks.<br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-16942656456631181322022-10-14T13:52:00.005+03:002022-10-14T13:52:47.103+03:00מבוא לבדיקות קסם<p style="text-align: left;"> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.pixabay.com/photo/2017/06/26/14/17/child-2443969_960_720.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="532" data-original-width="800" height="213" src="https://cdn.pixabay.com/photo/2017/06/26/14/17/child-2443969_960_720.jpg" width="320" /></a></div><p></p><p style="text-align: left;"></p><p style="text-align: left;"><a href="https://always-fearful.blogspot.com/2022/10/testing-magic-101.html">English</a><br /></p><p dir="rtl" style="text-align: right;">מדי פעם יוצא לי לשמוע על "איך לעשות בדיקות X", כשאיקס יכול להיות כל דבר - מובייל, API, מערכות משובצות חומרה (אמבדד, בלע"ז) או תכל'ס כל דבר שהוא לא אתר אינטרנט. בדרך כלל אני מגחך לשמע כל ההייפ הזה מסביב למשהו נפוח ומלא בעצמו שבדרך כלל אפשר לצמצם ל"אני צריך להבין את המערכת שאני בודק, להבין איך אני מתממשק מולה, כל השאר זה שום דבר חדש". אם התוכן ממש טוב, אז יהיה בזה קצת ערך מוסף - יכולים להפנות את תשומת הלב שלי לפרמטרים שאני בדרך כלל לא חושב עליהם. כמו צריכת סוללה בבדיקות מובייל, או השפעות של תנודות בטמפרטורה ובזרם במערכות חומרה. </p><p dir="rtl" style="text-align: right;">יצא לי להאזין לפרק של <a href="https://qualitestgroup.com/insights/podcasts/" target="_blank">The testing show</a> על בדיקות AI. הפרק היה, למרבה הצער, עוד פרק על גבול הפרסומי בו הביאו מישהי אחת לדבר על המוצר שהיא מוכרת. סליחה, על אילו אתגרים בבדיקות יש במוצר הזה. זה לא היה גרוע כמו בפעמים בהן מגיע מומחה מטעם Qualitest, אבל זה לא היה רחוק מזה. בכל מקרה, הנושא היה למידת מכונה, ומעט התוכן שהיה שם ולא היה סתם היכרות או קשקשת ידידותית, היה בעיקר חזרה על "יש פה משהו חדש לגמרי ושונה מכל מה שהכרנו עד היום, ותראו אילו אתגרים יש פה! כבר אמרתי שזה קשה?". </p><p dir="rtl" style="text-align: right;">הפרק הזה גרם לי לרצות לכתוב טקסט עם קצת יותר תוכן שיעזור לאנשים שמרגישים קצת אבודים ויפרוש את מה שבעיני הוא הבסיס. </p><p dir="rtl" style="text-align: right;">כנראה שהדבר הראשון שצריך לענות עליו הוא "מה אנחנו בודקים?" האם אנחנו בודקים מנוע לומד? האם אנחנו פשוט מחברים חתיכה של קסם למוצר שלנו ואומרים "זה יסדר לנו את החיים"? שני הדברים האלה שונים למדי ודורשים אסטרטגיה שונה. </p><p dir="rtl" style="text-align: right;">בעשור האחרון עבדתי עם מוצרים שהיו מבוססים על למידת מכונה. קודם על מוצר שעוטף מודל בייסיאני כדי לאתר הונאות בכרטיסי אשראי, אחר כך מוצר שעוטף רשת נוירונים כדי לחסום רושעות. לא בטוחים מה אחד המונחים האלה אומר? אפשר לקרוא על זה בזמנכם הפנוי, כרגע מספיק אם נתייחס לזה כאל כישוף שחור 1 וכישוף שחור 2. למרות ששני המוצרים שונים מאוד זה מזה בכמעט כל פרמטר, מבחינת שניהם המודל היה סוג של קסם - זורקים עליו את הנתונים בצורה הנכונה ומקבלים בחזרה תשובה שתהיה חלק מעץ ההחלטות. נכון. הקסם הזה פותח באותה חברה, והוא גם היתרון היחסי על המתחרים, אבל גם כשיש תשובה מושלמת לשאלה (האם מישהו משתמש בכרטיס אשראי גנוב? האם הקובץ הזה הוא זדוני?) עדיין יש לא מעט לעשות כדי להפוך את זה למוצר - זה לא מספיק לדעת לזהות כל רושעה, צריך גם להתחבר למערכת ההפעלה כדי לתפוס קבצים שנכתבים, צריך לדעת לבודד אותם ולהתריע על המתקפה. כל זה צריך לעבוד בצורה חלקה בלי לגזול יותר מדי משאבים, לתמוך במגוון מערכות הפעלה ועוד. לכל הדברים ההיקפיים האלה יש בדיוק אפס קשר לבדיקות של למידת מכונה והם יהיו עיקר העבודה של מי שמוצא את עצמו בודק מוצר כזה. אלה לא בדיקות של למידת מכונה, אלה בדיקות אינטגרציה עם למידת מכונה, וזה די סבבה להתייחס לרכיב הזה כאל קופסה שחורה, או אפילו להחליף אותו עם איזה סימולטור ברוב המקרים. </p><p dir="rtl" style="text-align: right;">מצד שני, לפעמים צריך לבדוק את המנוע עצמו. כשזה קורה חשוב לזכור שאין לנו את ההכשרה הנדרשת לזה (כלומר, לך אולי יש, אבל מתמטיקה ברמה הזו לא נדרשת לרוב תפקידי פיתוח התוכנה), אחרי שעשינו את זה, נחלק את העולם לשני סוגי אלגוריתמים - הגיוניים וסתומים. </p><p dir="rtl" style="text-align: right;">אלגוריתמים הגיוניים הם כאלה שאנשים יכולים להבין בצורה אינטואיטיבית ולדעת בערך איך תיקון מסויים ישפיע עליהם. למשל, אם נסתכל על המודל הבייסיאני שעבדתי מולו בעבודה הראשונה שלי - הוא קיבל כמה עשרות פרמטרים, מיקם כל אחד מהם בתוך "דלי", לכל דלי כזה היה משקל, סכמנו את כל הדליים הרלוונטיים, והופ! יש לנו תשובה. הצוות שבדק את המנוע הזה לא היה צריך להתמודד עם כל המורכבויות של למידת מכונה. פשוט לקחת מודל סינתטי, לזרוק עליו הררים של נתונים מפוברקים, ולעקוב אחרי החישוב הצפוי. לארגן נתונים נוספים, להפעיל את חישוב המשקלות ולראות שהכל עובד היטב. ברמה בה הם עבדו, הכל היה דטרמיניסטי לחלוטין והאתגרים שם היו שונים מאשר אלו שמקובל לדבר עליהם לגבי למידת מכונה. כדי לוודא את נכונות המנוע צריך היה רק לעקוב אחרי החוקים המתמטיים. זה אולי מפרך, אבל לא קשה. </p><p dir="rtl" style="text-align: right;">הסוג השני הוא האלגורתמים הסתומים. לא, לא במובן של מטומטמים, אלא במובן של נסתרים (כמו בביטוי "נסתם ממנו", או ההגדרה השנייה <a href="https://www.milononline.net/do_search.php?vid=1019717&Q=%F1%FA%E5%ED">כאן</a>). בעוד שטכנית אפשר לחשב באופן ידני את התוצאה אם ידוע לנו המצב הפנימי והקלט בדיוק כמו במודלים ההגיוניים (ואין חלקים אקראיים), כאן אין טעם לעשות את זה כי זה לא יוסיף שום דבר להבנה שלנו. ידועות לשמצה בחוסר המובנות שלהן הן רשתות הנוירונים שגם המומחים הגדולים מסתכלים עליהן ואומרים "עזוב, קסם" (זה לא לגמרי מדוייק, יש מחקר אקדמי שנועד להפוך אותן לנהירות יותר, הוא לא טוב מספיק עדיין). אנחנו אולי יכולים להסביר את ארכיטקטורת הרשת ולמה היא הגיונית באופן אינטואיטיבי, ואנחנו יכולים להסביר ואפילו לחשב את פונקציית ההפצה לאחור (back propagation), אבל גם אם יש לנו טעות - אין לנו מושג איך לתקן את התוצאה חוץ מאשר לאמן את המודל בעוד המון נתונים ולקוות שלא שברנו שום דבר שפעל קודם. עם אלגוריתמים סתומים, בדיקות הן בעצם חלק בלתי נפרד מתהליך הבנייה שלהן (מאמנים את המודל על סט אימון, בוחנים את הביצועים שלו על סט נתונים נפרד. אם הוא לא מספיק טוב, ממשיכים לנסות) ולכן האסטרטגיה הכי הגיונית מבחינתנו היא לקבל אותם כקופסה שחורה נטולת טעויות. </p><p dir="rtl" style="text-align: right;">הממ.... להכריז על בדיקת הנכונות כעל בעיה של מישהו אחר וכל הבעיות שלנו נפתרו... נכון שזה מתוחכם?<br />אז זהו, שלא בדיוק. למרות שבדרך כלל יהיה צוות של מדעני נתונים שבודק את המודל ואת ההתאמה שלו לעסק, יהיו מקרים בהם נצטרך להשלים פערים מסויימים - האם המודל ממשיך לעבוד גם מחוץ למעבדה? אולי בכלל קנינו את הרכיב הזה ממישהו אחר ואנחנו לא סומכים במאה אחוז על הצהרות היצרן?</p><p dir="rtl" style="text-align: right;">הרבה מהבעיות האפשריות עם למידת מכונה הן לא בעיות של תפקוד "לא נכון", כמו שהן בעיות של תפקוד לא צפוי, ולכן תקלות פוטנציאליות יהיו ברורות אם נרחיב קצת את המבט שלנו ונסתכל על ההקשר בו התוכנה שלנו פועלת. זה לא שחסרות תקלות "מפתיעות" שקשורות ללמידת מכונה - מ<a href="https://www.huffpost.com/entry/microsoft-tay-racist-tweets_n_56f3e678e4b04c4c37615502" target="_blank">צ'אטבוט נאצי</a>, <a href="https://www.cbsnews.com/sacramento/news/google-photos-identifies-two-black-users-as-gorillas/" target="_blank">זיהוי אנשים שחורים כגורילות</a>, <a href="https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G" target="_blank">הטיה מגדרית בסינון קו"ח</a> וזה לפני שאנחנו סופרים <a href="https://arstechnica.com/cars/2020/01/how-a-300-projector-can-fool-teslas-autopilot/" target="_blank">מתקפות מכוונות שגורמות למכוניות אוטונומיות לעשות שטויות</a>.כדי להימנע מתקריות שיביכו את החברה שלנו מאוד, אני אוהב לשאול כמה שאלות בסיסיות שצריכות להישאל עוד לפני שמתחילים לחשוב על שימוש בבינה מלאכותית לפתרון בעיה, והמומחים שבנו את המערכת כנראה כבר שאלו, אבל רק למקרה שהם החמיצו משהו (או, שוב, אם קונים מוצר מדף):</p><ul dir="rtl" style="text-align: right;"><li>כמה זה מטומטם? יש טענות (כמו <a href="https://techxplore.com/news/2016-11-reliable-inference-criminality.html" target="_blank">היכולת לזהות פושעים לפי תווי פנים</a>) שהם מספיק לא הגיוניים עד שאין צורך לחפש את הבעיה בנתונים כדי לדעת שיש אחת כזו. <br /></li><li>יש מלא נאצים שם בחוץ: האם המודל ממשיך ללמוד תוך כדי שהמוצר נמצא בשטח? אם כן, עד כמה המידע שם אמין? איזה מאמץ יידרש כדי להטות את המודל בצורה שלא תמצא חן בעיני?</li><li>מוצא האדם מן הקוף: מניין השגנו את הנתונים עליהם אימנו ובחנו את המודל? האם הם מייצגים את המציאות שהמוצר שלנו יפגוש?</li><li>העולם דפוק: האם יש איזו בעיה בעולם האמיתי שהאלגוריתם שלנו מנציח? אם כשבני אדם מקבלים החלטה הם עושים את זה בצורה מוטה ומפלה, ללמד תוכנה להחליט כמונו רק ייתן להחלטה המוטה סמכות כי "המחשב החליט". <br /></li><li>בוא נציק לערבים: האם התוכנה שלנו תייצר מעגל משוב מזיק? הרעיון הגיע מהספר של קת'י אוניל "<a href="https://www.goodreads.com/book/show/28186015-weapons-of-math-destruction" target="_blank">Weapons of Math Destruction</a>" שם היא מדברת על מוצרים שנועדו לעזור לכוחות משטרה (בארה"ב) לחלק את כוח האדם המוגבל שלהם בצורה אפקטיבית יותר ושלח יותר שוטרים לשכונות מוכות פשע. אלא מה? נתוני הפשיעה מגיעים ממה שהמשטרה כבר מצאה. אז גם התחלנו עם הטייה נגד שחורים, וגם עכשיו נמשיך למצוא יותר פשיעה בשכונות האלה, כי מי שנוהג שיכור בשכונה אחרת פשוט לא ייתפס באותה מידה, אז נמשיך לשלוח שוטרים לאותן שכונות ולמצוא בהן יותר פשעים משכונות אחרות. </li><li>אל תשאל, לא תשמע שקרים: האם התוכנה שלנו משתמשת במידע אסור כדי לקבל החלטות? אם נחזור לסינון קורות החיים, אנחנו אולי חושבים שהתוכנה לא תפסול מועמדים רק כי הם השתחררו מצה"ל בדרגת רס"ן וטוחנים מילואים, אבל עם מספיק קורות חיים היא תמצא סימנים מקשרים (למשל, לימודים באמצע השירות הצבאי או כניסה מאוחרת לשוק העבודה) שיאפשרו לזהות מי קצין בכיר גם בלי שזה יהיה כתוב באופן מפורש. אם המודל שלנו סתום, הצלחה לנו עם למצוא את זה. </li></ul><p dir="rtl" style="text-align: right;">אחרי שקיבלנו תשובות טובות מספיק לשאלות האלה, כדאי לחזור רגע להגדרת המשימה - מה אנחנו מנסים לעשות? איך זה הולך להתפוצץ לנו בפנים? בעולם אבטחת התוכנה יש שיטה לגילוי סיכונים שקוראים לה "תסריטאות" - מושיבים אנשים עם רקע מגוון בחדר ואומרים להם "תתפרעו, אנחנו כותבים סרט בסגנון משימה-בלתי-אפשרית על לתקוף את המוצר שלנו". אפש לעשות משהו דומה גם כאן, רק שסגנון הסרט יהיה יותר בכיוון של "שליחות קטלנית" או המטריקס. </p><p dir="rtl" style="text-align: right;">ועדיין, הבעיה נותרת בעינה: לוודא מערכות לומדות זו משימה מורכבת שדורשת הכשרה שונה מזו שיש לרובנו. יש עוד שני טריקים שאני מאמין שיכולים לעזור.</p><ol dir="rtl" style="text-align: right;"><li>אוראקל לא מושלם: יכול להיות שיש לנו מתחרים שפותרים את אותה בעיה, או משוב שחוזר לגבי כמה התשובה שהאלגוריתם נתן מתאימה. אולי זו סטטיסטיקה כלשהי שאנחנו מודדים, אולי זה משוב אנושי (אפשר גם להפעיל את הטורקי המכני של אמאזון אם אנחנו ממש מגזימים). מה שחשוב הוא שתהיה לנו דרך לומר על מקרה מסויים אם התוצאה הייתה נכונה או שגוייה. אם מצאנו אוראקל לא פגום מדי, אז כל "טעות" היא משהו שמעניין לחקור. למשל - כדי לבדוק את היכולת שלנו לזהות זודנות, אנחנו סורקים קבצים שמספיק מהמתחרים שלנו טוענים שהם זדוניים. לפעמים קורה שאנחנו צודקים יותר, אבל ברוב המקרים, אם אנחנו לא מסכימים עם הקונצנזוס, יש לנו מה לתקן. שיחקנו קצת עם "כמה מתחרים צריכים לחשוב שקובץ הוא זדוני" כדי שברירת המחדל תהיה "אם יש חוסר הסכמה, זה באג עד שיוכח אחרת".</li><li>ויזואליזציה. לא תמיד אפשר למצוא אוראקל בקלות (כמה מתחרים יש לגוגל בסיווג תמונות לקטגוריות?), אבל בחלק מהמקרים בן אדם יכול לזהות בעיות במבט חטוף. כמה מהר גוגל היו מוצאים את בעיית הגורילות אילו הם היו מקרינים על מסכים במשרד תמונות ביחד עם התיוג שלהן? או אם הם היו שולחים לכל עובד דוא"ל יומי עם שלוש תמונות ובקשה לאישור?</li></ol><p></p><p dir="rtl" style="text-align: right;">לסיכום, אני רוצה לחזור על מה שכבר כתבתי כאן בהתחלה: בעוד שאני מקווה שקיבלתם רעיון או שניים, הבסיס נשאר אותו דבר: הבינו את המערכת שלכם, הבינו איך לתקשר איתה ואילו סיכונים רלוונטיים יש במערכת. אחרי שהבנתם את זה, מצאו את הדרך הטובה ביותר לחפש את הסיכונים האלה. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-25143651508835918882022-09-20T08:30:00.001+03:002022-09-20T08:30:13.851+03:00defensive coding<div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.pixabay.com/photo/2016/05/19/14/18/defense-1403072_960_720.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="407" data-original-width="800" height="163" src="https://cdn.pixabay.com/photo/2016/05/19/14/18/defense-1403072_960_720.jpg" width="320" /></a></div><div><a href="https://always-fearful.blogspot.com/2022/09/httpsalways-fearful.blogspot.com202209httpsalways-fearful.blogspot.com202209defensive-coding-heb.html" target="_blank">עברית</a><br /></div><p> </p><p>One of the things happening to people in testing positions is that every
now and then we get to say "I told you so", usually around a bug report that was filed and closed as a "won't fix\not a bug\not important" and came back to bite us in the rear. While there's always the basic joy of being right (and more importantly, of other people being wrong), over the years I've learned to see those cases as a professional failures instead of sources of joy. After all, I saw the problem in advance, knew that it was a problem and maybe I could have done something differently to actually get it fixed. Maybe I could have presented the problem differently, talked to other people who could advocate it better for me, collected more evidence or perhaps it was only a matter of being more persistent in asking it to be fixed. In other cases, there was nothing I could do at the time since the reason for not addressing it is rooted in the organizational culture, that I now can start pushing towards. Saying "I told you so" is not the professional thing to do. </p><div>Last week I had just such a case - Something didn't work for a customer, and upon further inspection - had never worked since deployment. Before we got to difficult debugging, we went over the short checklist of problems. Something quite equivalent of your ISP tech support asking you to reboot your router when you call. In our case, this list consisted on one thing - checking the configuration file on the server. With two relevant entries, it was a rather short glance - the authentication token looked fine and the destination URL pointed to the correct base path. So far, so good. </div><div>Then, by sheer luck, something stirred my memory and I've noticed that the URL has been typed with schema+FQDN, you know - the way URLs are usually formatted. I recalled that when I've worked on that feature there was something odd regarding to having the schema provided in the file. A short trip to our bug tracking system, and indeed my memory was correct - if we provide the schema (for those less fluent in this specific terminology, that's the https:// part) , it won't work as the client consuming this configuration will add the schema themselves, and https://https://any.domain will fail. To make it that much more fun, there won't be any way to understand that from the logs. The ticket was logged in last December (about 9 months ago!) and in the discussion around it there were some acceptable reasons to not fixing it, a case could have been made for a fix anyway, but back then it would have been a harder battle to win. It's not that any fix would have been difficult - the team configuring the server could add a regular expression validation to their tool, the server could reject the config or remove the schema, the client could do the same and log a meaningful error and all of us could be monitoring for this feature once it was deployed We could even change the name of the parameter so that instead of "...URL" it would be "...DOMAIN" and reduce the chance of errors. For almost all steps that we could have taken but did not there's a common reason: Optimistic coding. </div><div>Optimistic coding is a state of mind where we assume that everything is going to be fine - the API is to be used only internally, so everyone will know what they should be doing. And if someone makes a mistake? Well, it's their problem to fix. </div><div>What we should be doing (and I intend to use this incident as leverage to push towards such behavior) is to create our software with a slightly more paranoid approach. Software is created to be used and operated by human beings, and human beings make mistakes. We need to assume that people operating, configuring and debugging the system will act very stupidly, not because they are stupid but because they are doing something else, under time-pressure and with a lot of distractions around. Most likely, at least some of those people will be our future selves. If we keep that in mind we can adopt a "Nothing gets passed me" approach - any mistake that can be detected in a given phase should be dealt with at this place - fix it if possible, return (or report) an error if fix is not possible. and almost never let a problem pass from one component to the next. <br /></div><div><br /></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-22425042658243345792022-09-18T08:08:00.005+03:002022-10-14T13:53:36.126+03:00קידוד מתגונן<p> </p><div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.pixabay.com/photo/2016/05/19/14/18/defense-1403072_960_720.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="407" data-original-width="800" height="163" src="https://cdn.pixabay.com/photo/2016/05/19/14/18/defense-1403072_960_720.jpg" width="320" /></a></div><br /><p></p><br /><div><a href="https://always-fearful.blogspot.com/2022/09/defensive-coding_0863470953.html" target="_blank">English</a></div><div><br /></div><div dir="rtl" style="text-align: right;">אחד הדברים שקורים למי שבודק תוכנה למחייתו הוא שמדי פעם עולה הזדמנות לומר "אמרתי לכם", בדרך כלל מעורב בזה באג שנסגר תחת "זה לא יקרה\לא בעיה\לא נתקן". יש בזה משהו מאוד מספק ברמה ילדותית שכזו, אבל לאורך השנים הרגלתי את עצמי להסתכל על אירועים כאלה כעל כישלון מקצועי. במקום ריקוד ניצחון קטן והידיעה שבפעם הזו הייתי צודק יותר ממי שזה לא יהיה שהתווכחתי איתו, אני מנסה להבין מה יכולתי לעשות כדי לא להגיע למצב הזה מלכתחילה. לפעמים זה עניין של להתנסח טוב יותר, לאסוף ראיות מתאימות או לדבר עם האדם הנכון. לפמים נדרש שינוי תרבותי משמעותי יותר כדי שבעיות מהסוג הזה יילקחו ברצינות. כך או אחרת, אם משהו הצליח להרגיז לקוח, זה לא מספיק לכסות את הישבן שלי ולומר "אבל אמרתי לכם", צריך להבין איך יכולתי לעזור לארגון להימנע מזה מלכתחילה. </div><div dir="rtl" style="text-align: right;">בשבוע שעבר קרה לי בדיוק מקרה כזה - משהו לא עבד בשביל לקוח, ומסתבר שלא עבד בכלל מהרגע בו הוא קיבל את המוצר. לפני שניגשנו לדבג את הבעיה ולחקור הכל כמו שצריך, התחלנו עם מעבר בסיסי על שטויות מטומטמות. קצת כמו שמתקשרים לתמיכה הטכנית ושואלים אם ניתקנו את הראוטר מהחשמל וחיברנו חזרה. הפעם, סט הבדיקות היה קצר למדי - בודקים שקבצי הקונפיגורציה בשרת תקינים, כל דבר יותר מזה כבר דורש לקבל אישור מהלקוח. לפיצ'ר הספציפי הזה היו שני פרמטרים רלוונטיים, מזהה כלשהו ופרמטר נוסף שמייצג URL אליו צריך להתחבר. שניהם נראים די סבבה בסך הכל. ואז, בתופעה של יותר מזל משכל, משהו בזיכרון שלי קפץ וצעק "רגע! רגע!" זכרתי שהיה משהו בו אם מספקים פרמטר של URL אבל כוללים בפנים גם את הסכמה (יעני, https://) אז יש בעיות. או להיפך, אם לא כוללים אותה. מפה לשם, בדקנו את מערכת תיעוד הבאגים שלנו ואכן, זכרתי נכון. בשדה שנקרא משהו משהו URL, הערך שצריך להכניס הוא בעצם שם הדומיין (ובלעז - FQDN). יותר מזה, בתיאור הבאג אי אז בדצמבר האחרון (לפני תשעה חודשים!) אפילו כתבתי שבגלל שם המשתנה מישהו עלול להתבלבל ולכלול את הסכמה בלי לדעת שזה יעשה צרות. סטטוס הטיקט - לא באג. האמת? היו כמה נימוקים סבירים לגמרי - זה משתנה שנערך רק על ידי צוות פנימי, יש לנו שדות אחרים בשם URL עם אותה מגבלה ממש, וככה היה החוזה שסגרו מול הצוותים השונים. זה שפשוט למדי לתקן את זה? אז פשוט. גם במבט לאחור, זה לא דיון שהייתי יכול להגיע בו לתוצאה אחרת באותו זמן, כי הגישה של "אם מישהו אחר בשרשרת טועה זו בעיה שלו" הייתה רווחת מאוד, וגם אילו הייתה לי דרך להסלים את זה (לא היה אז למי, היום אולי יש) - זה לא בהכרח משהו שכדאי לפתוח סביבו מאבק שיפגע ביכולת שלי לעבוד בהמשך עם האנשים. זה לא שחסרו דברים שיכולנו לעשות - אם הצוות שאחראי על קינפוג השרת היה מסדר ולידציה פשוטה בתהליך האוטומטי שמפעיל את זה, אם הצוות של השרת הזה מוריד את הסכמה אם היא קיימת, או אם הקליינט היה עושה אותו דבר, אם אחד הצוותים היה דואג לשורת לוג ברורה שהיינו טורחים לנטר או אפילו אם היינו פשוט משנים את שם המשתנה כך שבמקום URL תופיע המילה DOMAIN - סביר להניח שלא היינו נמצאים היום במצב בו לקוח כועס עלינו. <br /></div><div dir="rtl" style="text-align: right;">הבעיה הייתה, ועודנה, אופטימיות יתר. מישהו טעה? המערכת קורסת? זה בגלל מי שטעה וזו בעיה שלו. אין לנו מערכת מסודרת להעברת כל הפרטים הקטנים האלה שהיו לי ברורים מאליהם ולא טרחתי לתעד? או להעביר את המידע הלאה גם אחרי ששמתי לב שיכולה להיות כאן אי-הבנה? אז אין מערכת, שישאל אם הוא לא בטוח. זו התרבות הנוכחית - חזקה יותר בצוותים מסויימים, פחות חמור בצוותים אחרים. </div><div dir="rtl" style="text-align: right;">בסופו של יום, צריך לזכור שקוד נכתב כדי שבני אדם ישתמשו בו. ובני אדם טועים. אנחנו רוצים לבנות מערכות שעוזרות לנו להתמודד עם המציאות הזו - לתקן טעויות אנוש אם אפשר, לעצור את הטעות אם אי אפשר ולא להעביר אותה לנקודה הבאה בשרשרת, ולהציף את השגיאה כמה שיותר מהר. הטענה "זה עומד בדרישות המוצר" פשוט לא מספיקה. </div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-43213862084384509762022-09-15T16:18:00.002+03:002022-09-15T16:18:56.519+03:00Book review - Radical Candor: Be a kick-ass boss without losing your humanity, by Kim Scott<div class="separator" style="clear: both; text-align: center;"><a href="https://cdn.shopify.com/s/files/1/2091/2603/products/Radical_2.png?v=1605874983" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="744" data-original-width="491" height="320" src="https://cdn.shopify.com/s/files/1/2091/2603/products/Radical_2.png?v=1605874983" width="211" /></a></div><br /><p><br /></p><p>Radical Candor is a book I got to after years of getting references to it from various places - blogs, other books, people. It means that I had high expectations *and* was pretty sure I wouldn't be surprised by the content. Not an easy place for a book to be. Despite those difficult starting conditions, it manages to live up to the reputation it has, and to pack the information in a useful, coherent way.</p><p>I've listened to an audiobook of the 2nd edition, and it starts with an attempt to defuse a common backlash of the 1st edition: Radical candor is not a permission to be an arsehole, nor is it an invitation to be cruel. The main reason of being truly candid with someone is because we care. It is this care that drives us to provide feedback even if it's painful, and to make sure the recipient is able to make use of it. The book cover is providing a neat summary of the main idea behind this book. Relevant behavior is measured on two axes: Caring personally and challenging directly. Caring personally is being interested in the well being of the person you are working with (in the book, the people you manage). Not offending them, providing them with opportunities to improve, etc. Challenging directly, on the other hand, is about getting things done - pointing out mistakes, being accurate and concise, regardless of how people feel about it. </p><p>Those axes create four distinct categories:</p><p></p><ul style="text-align: left;"><li>Manipulative insincerity: Low caring, low challenging. This is where you show no care for the other person and for the mission - you avoid conflict by not giving someone difficult feedback, but having no problem backstabbing them when they are not around. You might want to be on that person's good side, to pat their ego or simply to get that person off your hands. If you see someone being sweet and enthusiastic with someone, only to sigh and roll their eyes the moment the other person leaves - that's it. </li><li>Ruinous empathy: High caring, low challenging. The intentions behind this are kind - not making someone feel bad, giving them leeway since they are in a difficult time, or simply wanting to avoid conflict. The end result though is indistinguishable from manipulative insincerity, as in both cases you will keep silent or give underserved praise. The fact that you mean well doesn't really matter, as the road to hell is paved with good intentions and in this case, hiding the mess under the carpet will come back to bite both of you in the rear.</li><li>Obnoxious aggression: Low caring, high challenging. Surprisingly, this is actually the second best place to be in. People getting the business end of this behavior might cry, stress out or feel attacked, but things actually get done and either improves or breaks completely. If people can grow the necessary thick skin to survive, they will get direct feedback and could build on it to improve. Don't expect employee retention to be high, though, as this assault on the employees ego and confidence will tire most of them enough to quit.</li><li>Radical candor: High caring, high challenging. This is the sweet spot between obnoxious aggression and ruinous empathy. You give employees actionable feedback and help them process it and improve from it. You take care not to say "This code is shit", but rather "This code isn't good enough, it should be broken to smaller functions, improve variable names and care more about log levels, you usually do better". </li></ul><div>It's important to notice that there isn't a recipe for being radically candid with someone, as what matters is not what was said but rather what was heard and understood. One person will understand "this work is shit, take the time to do it well" as a personal judgement and will be discouraged, while another would see it as an honest evaluation of the work and an appreciation of their skills when not under pressure. The first might be at a loss about what is wrong and would require more direct guidance such as "it works, but will be hell to maintain unless we clean up the wording and build a better structure while the second might get annoyed with you explaining the obvious and micromanaging them. the difference could be rooted in personal preferences on getting feedback, but it can also be a result of how much the person trusts you, how confident they are in their current situation and skills or how they woke up this morning. Complicated? it sure is. You will make mistakes, and the only question is how many and how severe they will turn out to be. A strong relationship is a good buffer to absorb such mistakes, so it is worth investing in it from the onset. </div><div><br /></div><div>After the reader (listener, in my case) has understood both what is radical candor and why is it important, it's time to implement those ideas. The book goes on to discuss strategies of creating a radically candid culture around you, peppered with examples from the author's experience that helps understanding the theory as well as the need for anyone to devise their own strategies. I won't go into all of the details as the book does it way better than I could (plus, the author has put a lot of effort into it, go read her work, no mine) but all in all, it's definitely a book I'll listen to again, and it helped me frame office (and personal) communication in a helpful manner.</div><div><br /></div><div>At the end of the audiobook there was a preview of another book: "How to Root Out Bias, Prejudice, and Bullying to Build a Kick-ass Culture of Inclusivity". The text there is a little bit less polished and I'm not sure I got the the bottom of what was communicated to me, but even so it was very moving, which is what to be expected from an introduction section to a book (as I believe this text to be) I found myself wondering several times "What the F?? which decent human being would behave that way? there's bias, and there's straight out harassment". So, this is a solid "maybe" with good potential. </div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-46456785784450075432022-07-16T23:53:00.002+03:002022-07-16T23:53:20.457+03:00How to botch a consulting gig<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://images.pexels.com/photos/4101206/pexels-photo-4101206.jpeg?cs=srgb&dl=pexels-cottonbro-4101206.jpg&fm=jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="800" data-original-width="533" height="320" src="https://images.pexels.com/photos/4101206/pexels-photo-4101206.jpeg?cs=srgb&dl=pexels-cottonbro-4101206.jpg&fm=jpg" width="213" /></a></div><br /><p></p><p style="text-align: right;"><a href="https://always-fearful.blogspot.com/2022/07/blog-post.html" target="_blank"> גרסה עברית</a></p><p> The company where I work in is undergoing a process change. More specifically, it's branded as an "Agile transformation", which in my eyes is a rather necessary step, as we are dealing with the pains of growing from a small startup to a larger organization, and the informal channels that were so great before are now creating chaos and making it that much harder to get work done. In order to do this properly, we have brought some external coaches to help us figure out the right process for us. It started pretty well: The VP of engineering has stated our goal in a town-hall meeting, and introduced the consultants who were to start working soon. At least to me, the message was pretty clear, and it was nice to focus our attention on our working process. </p><p>Then, at least from my perspective it went downhill. However, before I go on to identify the mistakes I saw I want to clarify my point of view - I am an individual contributor in the company, one of about 70 (If I have my numbers right). I'm not part of the leadership team and did not attend many of the meetings, so I'm assuming I don't see many of the constraints. </p><p>With this disclaimer out in the open, the list of botches is short but seemingly fatal.<br /></p><p>After the first announcement came more than a month of silence. We've seen the consultants wander around in the corridors or sitting with upper management in meetings, but nothing was transmitted down. We were left in the dark about the prep work they were probably doing. This long period of time meant that momentum was lost after a good pitch and it also gave people time to speculate, to worry and to build a negative image of the consultants. After all, when you see someone every office day, you expect to have at least a rough idea about what they do. If you can't get that, assuming this person does nothing is the default. This dark period was long enough that I went to the PM (who had a strong part in coordinating this transition) when they plan to start working, which I only did after someone in my team asked me whether such conduct is normal.</p><p>They were, probably, gathering information and building a strategy for the organization, which leads me to the second botch: They talked almost exclusively to management. I know that they asked for some names of non-managers to talk to, but have no idea whether they followed up with any of them. I know that my name was given to them and that I was not approached . Talking to non managers in the information gathering phase is critical, as it enables you to have a more complete picture of the organizational state, as well as make sure people feel their voice is heard. </p><p>It's not very surprising that the next fail point was the solution presentation. </p><p>On the face of it, it was done as it should - they have set time with each team to present the new way we plan to work, giving each team time to ask questions, to understand how it should impact their work and focus on the points most relevant to them. However, the result was adding injury to insult. The meeting was based on a template presentation that looked like it was taken from scrum-101, dwelling on explaining the basic scrum terms and process and ignoring the elephant in the room: The problem all teams are feeling very painfully is the communication disconnect between various teams and groups. Any decent solution would at least acknowledge this problem, even if it was only to say "after completing this first step, here's our current thoughts of what we are hoping to do, and that's why it is not the first step. Another problem in the meeting was that it all felt like a shallow sprinkle of scrum on the structural and procedural problems we have,trying no to rock the boat (I'm guessing) and hoping for a miracle. Under the very wide umbrella of "this is the first step, let's roll with it and improve on what we get" we are keeping the silo-structured teams, putting the team leads into both scrum-master and product owner roles and did not even hint about how will the backlog be populated or prioritized, not to mention how do we plan to help teams start working together on the smallest value unit that can arrive to production - which we call "Epic", just to confuse people who have some previous scrum experience. Add to that a consultant that is not well versed in the relevant literature to the domain (when I mentioned that having the same person act as scrum master and product owner is usually considered as problematic and asked for the reason behind this choice, I got a response of "I'm not familiar with such articles", which meant I had to spend a whole 30 seconds of google to find 3 such examples) and you might understand that different teams came out of this meeting with either with a feeling of "ok, so nothing is going to change" or just disappointed and feeling that the whole charade is not addressing any of the important issues.</p><p>Whatever credit the consultants might have had before this meeting was now thoroughly obliterated.</p><p>Despite that, I'm optimistic about the process we are undergoing. Even if the consultants won't be able to recover from this poor start, and even if they will make every other possible mistake, Just by bringing them in the organization has created a space to talk about the process, to introspect and see the pain points that teams are experiencing, and ultimately, we have some very bright people with diverse experience working together and those two facts alone will lead the organization to a better place.</p><p><br /></p><p>So, to sum the points you might want to remember in your next consultancy gig:</p><p>1. Collect information from all levels of the org. Meeting with 12 teams to a 2 hours round table each will take you roughly a week. You probably need less than that. </p><p>2. Transparency isn't enough - be vocal about your work. People, not only those that write you the check, should have the feeling that you are working and have an idea what about.</p><p>3.Understand the problems people feel, and address them even if you believe the problem is something else. In the latter case, share your understanding and test it. </p><p>4. Tailor your out-of-the-box content to your audience.<br /></p><p><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-11212654791930091942022-07-16T23:53:00.001+03:002022-07-16T23:53:14.454+03:00איך להיכשל כיועץ<p dir="rtl" style="text-align: right;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://images.pexels.com/photos/4101206/pexels-photo-4101206.jpeg?cs=srgb&dl=pexels-cottonbro-4101206.jpg&fm=jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="800" data-original-width="533" height="320" src="https://images.pexels.com/photos/4101206/pexels-photo-4101206.jpeg?cs=srgb&dl=pexels-cottonbro-4101206.jpg&fm=jpg" width="213" /></a></div><br /> <a href="https://always-fearful.blogspot.com/2022/07/how-to-botch-consulting-gig.html" target="_blank">English version</a><br /><p></p><p dir="rtl" style="text-align: right;">התחלנו שינוי ארגוני בעבודה. ספציפית, אנחנו עוברים למשהו שדומה יותר לסקראם. האמת? יש לנו לא מעט מה להרוויח מכזה מעבר - זה עוד צעד שאנחנו עושים כחלק מהמעבר מסטארט-אפ קטן שרץ מהר ושובר הכל לחברה בוגרת שמסוגלת לתכנן קדימה בצורה אחראית כלפי הלקוחות שלה. אפשר לומר שערוצי התקשורת הלא רשמיים שעבדו נהדר בחברה קטנה כבר לא מחזיקים מים לנוכח הררי העבודה שאנחנו עושים. יש יותר מוצרים, יותר צוותים, יותר לקוחות וכל זה אומר שיש הרבה יותר מקום להפיל דברים בין הכיסאות, כך ששינוי כלשהו בהחלט נדרש. כדי לבצע את השינוי כהלכה, הבאנו יועצים חיצוניים שיעזרו לנו להימנע מטעויות נפוצות ולמצוא את התהליך שיתאים לצרכים הספציפיים שלנו. אחלה.אפילו התחלנו ממש ברגל ימין - בפגישה של כל המחלקה הציג המנהל את הכיוון החדש שאנחנו רוצים ללכת בו, איך זה הולך לעזור לנו והציג את היועצים שיתחילו ממש או-טו-טו. המסר היה ברור והמיקוד בשיפור התהליך היה מאוד חיובי בעיני. </p><p dir="rtl" style="text-align: right;">משם, עד כמה שאני יכול לומר. הכל הידרדר. <br />לפני שאתחיל לפרט מה בדיוק השתבש, חשוב לי לומר שאני מציין דברים מנקודת המבט שלי - עובד מן המניין שאינו מנהל, כך שלא הייתי חשוף לחלק ניכר מהדיונים או לאילוצים השונים שהם פעלו תחתיהם. בהחלט ייתכן שמה שאני רואה כהחמצה אדירה זו התוצאה האפשרית הטובה ביותר בהינתן המצב.</p><p dir="rtl" style="text-align: right;"></p><p dir="rtl" style="text-align: right;">בכל מקרה, אחרי ההבהרה הזו, אני רוצה לפרט את רשימת הכשלים, שבעוד שהיא קצרה, היא גם קטלנית לתהליכים כאלה. </p><p dir="rtl" style="text-align: right;">בראש ובראשונה, היה יותר מחודש של דממה. ראינו את היועצים מסתובבים במשרד, יושבים לפגישות עם ההנהלה, מציגים מצגות, אבל בפועל - הם דיברו עם מעט מאוד אנשים (אני לא יודע על אחד שאינו מנהל שדיברו איתו, אני יודע שהם קיבלו כמה שמות ולכן בהחלט ייתכן שהם עשו זאת, אבל אחד השמות האלה היה שלי, אז הם בוודאות לא דיברו עם כל מי שהם קיבלו את שמו), שום דברים לא קרה ולאף אחד לא היה מושג מה הם עושים כאן. עבודת ההכנה שהם ככל הנראה עשו נשארה במסתרים. תקופת הדממה הזו לא באה בחינם. בכל רגע שעבר הם איבדו עוד קצת מהמומנטום הראשוני ונתנו עוד זמן לאנשים לתהות "אז מה היועצים האלה עושים כאן?" לדאוג מהשינוי המתקרב, להעלות השערות לא מבוססות ולבנות תמונה שלילית של היועצים. אחרי הכל, אנחנו יודעים, בערך, מה העבודה של כמעט כל מי שאיתנו במשרד. אם אין לנו אפילו תמונה כללית של "מה עושה ישראל ישראלי בעבודה" אנחנו מניחים שהוא בטלן שלא עושה כלום. למעשה, אחרי זמן מה ואחרי שאלה מצד אחד מחברי הצוות (שרצה לדעת אם התנהלות כזו היא נורמלית אצל יועצים), הלכתי לprogram manager שלנו (איך מתרגמים את זה לעברית?) שיש לו חלק משמעותי במעבר הזה ושאלתי אותו מתי הם מתחילים לעבוד. במקרה, זמן קצר אחר כך נתקלתי באחד היועצים במטבח ושאלתי אותו את אותה שאלה, בעיקר כדי לשקף לו מה התחושה בקרב העובדים שהוא לא מדבר איתם.</p><p dir="rtl" style="text-align: right;">כאמור, מה שהם כנראה עשו בזמן שמתחילת העבודה שלהם ועד אותו רגע היה כנראה לאסוף מידע ולבנות תוכנית, מה שמוביל לכשל השני. הם דיברו בעיקר עם מנהלים. לאסוף מידע מהצוותים ולהבין את נקודת המבט שלהם ואת הלחצים המופעלים על הצוותים השונים היה יכול לתת להם תמונה שלמה יותר של עולם הבעיה, אבל גם לתת לאנשים תחושה שמקשיבים להם ושמנסים לפתור את הבעיות שמציקות להם. שיתוף מוקדם, גם אם הוא מזוייף לגמרי, מונע תחושה של "הנחתה" ומאפשר לנו לבדוק את הרעיונות שלנו לפני שהתחייבנו אליהם. אולי הצגה מוקדמת של הרעיונות הייתה יכולה להציף נקודות שכדאי להתייחס אליהן, אולי שיחה עם הצוותים הייתה מצביעה על הפיל שבחדר. זה גם לא מאוד יקר לאסוף את המידע הזה - אם מקדישים שעתיים לשולחן עגול עם כל אחד משנים עשר הצוותים אנחנו מקבלים עשרים וארבע שעות. זה שבוע של אדם אחד. לגמרי משהו ששלושה אנשים יכולים לעשות ביומיים.</p><p dir="rtl" style="text-align: right;">זה, כמובן, מביא אותנו לכשל האחרון (עד כה) - הצגת הפיתרון. </p><p dir="rtl" style="text-align: right;">לא הרבה אחרי ששאלתי מתי הם מתחילים לעבוד נקבעה לכל צוות ישיבת התנעה בה יוצג הכיוון שאליו אנחנו רוצים ללכת. באופן שטחי, זה בדיוק הדבר הנכון לעשות: לתת לכל צוות את המרחב שלו לשאול, להבין, להשמיע חששות ולקבל מענה לדאגות הספציפיות שלו. בפועל, התוצאה הסופית הייתה גרועה יותר מאי עשייה. הפגישות היו מצגת "מבוא לסקראם תיאורטי" גנריות והתעכבו על הסבר של כל מיני מונחים ותפקידים, ואיך עובד סקראם בתוך צוות. מה לא היה שם? הפיל שבחדר. שום דבר שרלוונטי להקשר הספציפי שלנו. הבעיה הכי משמעותית שמשפיעה על כל הצוותים. קצרים בתקשורת בין-צוותית ובין קבוצתית. כל פיתרון שנעשה עם מינימום של השקעה היה לכל הפחות מתייחס לזה. גם אם רק כדי לומר "חבר'ה, את הבעיה הזו נצטרך לפתור אחר כך, הנה רעיונות שאולי יתאימו, נראה אחרי השלמת הצעד הראשון. אם אתם רוצים לדעת למה זה לא הדבר הראשון שאנחנו מטפלים בו, נשמח לדבר איתכם".<br />בעיה נוספת שהייתה במפגישה היא שלא הייתה בה שום בשורה - רוב האנשים יצאו ממנה בתחושה של חוסר משמעות. שאנחנו הולכים להתיז קצת בושם בניחוח סקראם על המבנה הקיים שלנו ולקוות שבדרך נס זה יפתור את הבעיות שאנחנו חווים. זה היה נראה כאילו מטרת הצעד הראשון הייתה "לא לטלטל יותר מדי את הסירה" נשארנו עם מבנה של צוותים מבודדים, אנשי הנהלת המוצר אינם חלק רשמי מהתהליך וכל ראש צוות אמור לתפקד גם בתור ראש צוות, גם כסקראם מאסטר וגם כProdocut owner -ו מי שמכיר סקראם ידע לומר שאלו שלושה תפקידים שבדרך כלל נזהרים מאוד שלא לערבב אותם - גם מפאת הזמן שכל תפקיד לוקח, אבל גם בגלל סתירות פנימיות בין התפקידים (יש לציין, כשהעליתי את הנקודה בפני היועץ ושאלתי למה החלטנו להיות חריגים בנקודה הזו, הוא טען שהוא לא מכיר טענות כאלה. לקח לי שלושים שניות בגוגל למצוא לו דוגמאות, אז אשאיר את זה כתרגיל לקורא). איך כל ראש צוות אמור לתפקד כבעל מוצר כשיש תלויות עם צוותים אחרים? מי מתחזק את הבקלוג? איך מתמודדים עם בלת"מים ועיכובים? זה, כמובן, לא היה בפגישה. מי שנכח בפגישה יצא עם תחושה שהכל הולך להישאר בדיוק אותו דבר. מי שהגיע עם ציפיות לשיפור יצא מאוכזב מכך שכל ההצגה הזו לא מנסה אפילו לטפל בנושאים שכואבים לו.</p><p dir="rtl" style="text-align: right;">בקיצור - כל טיפה של קרדיט שאולי הייתה עדיין ליועצים בשלב הזה התאיידה לה אל חלל האוויר. </p><p dir="rtl" style="text-align: right;"> </p><p dir="rtl" style="text-align: right;">למרות זאת, אני די אופטימי ביחס לשינוי שאנחנו עושים בעבודה. גם אם היועצים ימשיכו לעשות עבודה חובבנית ולגרום נזקים, וגם אם הם יעשו כל טעות אפשרית, עצם העובדה שהביאו אותם לארגון יצרה מקום בו אפשר לדבר על התהליך, לבחון לעומק ולראות את הנקודות שכואבות לנו כמחלקה. בסופו של יום, יש לנו אנשים חכמים עם ניסיון מגוון וברגע בו אנחנו מנסים לשפר את התהליך שלנו, זה רק עניין של זמן עד שנצליח.</p><p dir="rtl" style="text-align: right;">אז, אם אתם מוצאים את עצמכם בתפקידי ייעוץ, הנה כמה טיפים שכדאי לא לפספס:</p><ol dir="rtl" style="text-align: right;"><li> אספו מידע מכל מי שרלוונטי. או לפחות ממספיק גורמים מגוונים.</li><li>שקיפות זה לא מספיק. היו קולניים לגבי העבודה שלכם. אנשים, ולא רק אלו שחותמים על הצ'ק שלכם, צריכים להרגיש שהם יודעים בערך מה אתם עושים. </li><li>הבינו מה הבעיות שאנשים חושבים שיש להם. גם אם תגיעו למסקנה שהבעיות האמיתיות נמצאות במקום אחר, תוכלו להתייחס לתחושות האלה. אם אתם חושבים שהבעיה נמצאת במקום אחר - השמיעו קול, תנו לאנשים להגיב ובחנו את התיאוריות שלכם. </li><li>בלי תוכן מוכן מראש. לכל הפחות דאגו להתאים את התוכן שלכם למקם שאתם מייעצים לו. <br /></li></ol><p><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-43257476247795432482022-02-12T19:50:00.000+02:002022-02-12T19:50:05.449+02:00Troubleshooting<p dir="rtl" style="text-align: right;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://i.ytimg.com/vi/t74NetFe1eM/maxresdefault.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="450" data-original-width="800" height="180" src="https://i.ytimg.com/vi/t74NetFe1eM/maxresdefault.jpg" width="320" /></a></div><br /><a href="https://always-fearful.blogspot.com/2022/02/troubleshooting-heb.html"> גרסה עברית</a><br /><p></p><p>Last week I faced a moment of frustration. And not the good "why isn't it working" kind of frustration. That kind I'm used to, and I normally end up learning something. This time I was frustrated with the people I work with. The story is quite simple - we had (yet another) problem in our systems, and they set out to investigate it. I was busy with a super-urgent-clock-is-ticking kind of task, so I wasn't available to help in this investigation. I did try to ask some guiding question, such as "what error do you see in the log", or "can you reproduce it on your system?" but other than that I was doing my best not to get involved. </p><p>After a while they have been struggling with the problem, my manager asked me to time-box 20 minutes for this, as it was blocking most of the team. After checking that the urgent task can wait this long, I took a look. Then I got upset. Two people have been looking on this, and the best they could come up with was to quote a partial error and the step which was failing. No guess of what could have happened, no narrowing down of the problem, a simple "it fails with this message". Yet, when I took a look, it was less than 30 seconds to figure out what was wrong, then perhaps 15 more minutes to find a workaround that is actually working. </p><p>I reflected a bit on this negative feeling - somewhere between disappointment and annoyance - and figured out why I was so upset, and this helped me notice something I didn't see before. </p><p>I was upset because I always assume that the people I'm working with are smart and capable people who are doing the best they can., and any contrary example is touching a raw nerve for me and makes me wonder why I'm bothering investing so much time and effort trying to collaborate with them instead of working individually. Then, after processing it a bit more and recalling the <a href="https://en.wikipedia.org/wiki/Fundamental_attribution_error">fundamental attribution error</a> I could say that it's probably not that the people who failed in a task I found trivial are not smart or that they don't try their best, it's more likely that there are some other factors I'm not aware of that make this behavior reasonable. Both of them had other tasks putting pressure on them, and both are fairly inexperienced - between them they have less than 18 months of experience. In addition, it reminded me that troubleshooting is a skill that needs practice and learning, which prompted this post - I want to share the way I approach troubleshooting, hoping it might help people. </p><p>The first thing worth noticing about troubleshooting is that almost anyone related to software development need to do this quite a lot - programmers, testers, CS, Operations, and however you might call the team managing your pipelines. The second thing worth noticing is that it looks a lot like bug investigation, so being a better troubleshooter will make you a better tester as well. In fact, the main difference between troubleshooting and bug investigation is the goal we have: troubleshooting is about making a problem go away, or at least find a way to do our task around it, where bug investigation is more about understanding the cause and potential impact of such problem, so if a bug just flickers away we'll hunt it down.</p><p>So, how do I troubleshoot? Here's a short guide:</p><ol style="text-align: left;"><li> Is it obvious? Sometimes the errors I get or the symptoms I experience are detailed enough that no investigation is actually needed. I can easily tell what has happened and skip directly to fixing or finding a workaround. <br /></li><li>Can I reproduce it? Does it happen again? if not - great, problem gone. It might come back later, in which case I might try a bit harder to reproduce it or trace its cause, but usually, a first time problem that isn't easily reproducible doesn't really need shooting. I skip to "done for now" and continue with whatever it is that needs doing.</li><li>Create a mental model - what was supposed to happen? Which components are talking with which systems? What relevant environmental factors should be considered? What has recently changed?<br /></li><li>Investigation loop:</li><ol><li>gather information. Google the error message or symptom, gain visibility on the relevant flow, ask around if anyone have seen such a problem, etc.</li><li>Hypothesize. Guess what might be causing the problem. </li><li>Try to disprove the hypothesis: </li><ol><li>Create a minimal reproduction of the problem</li><li>Find contrary evidence in log file, side effects, etc. <br /></li></ol><li>Tweak. Based on my current guesses, try working around or mitigate the cause of failure. I suspect a code change? I'll revert to a previous state. Server can't be reached? I'll tweak in order to gain more information. I might check for ping, or DNS resolution. </li><li>Check to see if problem has gone away. If so - update the theory of what happened and finish.<br /></li><li>Update and narrow the model. Using the information I gained, zoom in on the relevant part of the model and elaborate it. For example, a model starting with "I can't install the product", might narrow to "I have a remnants from a faulty uninstall that are preventing a critical operation" or to "the installation requires active internet connection and the computer has been plugged out", it can be more complicated than that. </li><li>If I can't narrow down the model, or can't come up with a next theory of what might be wrong, I still have two options:</li><ol><li>Hail Mary - I'll change a random thing that I don't expect to help but is related in some way. For instance, I might follow instructions on the internet to find a relevant configuration change, or reboot the system. Who knows? I might be lucky and gain more information, or even make the problem go away for a while. </li><li>Ask for help. Find someone who might have more knowledge than me, or just a fresh perspective, and share my model, failed attempts and guesses I couldn't or didn't act upon, and we'll look at the problem with that person's knowledge and tools. </li></ol></ol><li> Now we know what's wrong, or at least we're confident enough that we know, time to shoot down the problem. Find a way to configure a tool we were using and was causing problems, change the way we operate, sanitize our environment, or whatever will work to our satisfaction. </li></ol><p>That's it. I hope this flow will be helpful, at least to some extent. If you have additional tips on troubleshooting - I'd be happy to hear about them. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-35911431091529899472022-02-12T19:49:00.004+02:002022-02-12T19:49:59.032+02:00תפעול תקלות<p dir="rtl" style="text-align: right;"></p><div style="text-align: center;"><a href="https://i.ytimg.com/vi/t74NetFe1eM/maxresdefault.jpg" imageanchor="1"><img border="0" data-original-height="450" data-original-width="800" height="180" src="https://i.ytimg.com/vi/t74NetFe1eM/maxresdefault.jpg" width="320" /></a></div><br /><p></p><p style="text-align: left;"><a href="https://always-fearful.blogspot.com/2022/02/troubleshooting.html">English version</a><br /></p><p dir="rtl" style="text-align: right;">בשבוע שעבר היה לי רגע של תסכול. לא תסכול טוב של "אבל למה זה לא עובד??" אלא תסכול שקשור לאנשים שאני עובד איתם. הסיפור, בתכל'ס די פשוט - הייתה לנו (עוד) תקלה עם סביבת ההרצה שלנו, ושני אנשים מהצוות ניסו לתקן אותה. אני, מצידי, הייתי עסוק במשימה סופר-דחופה-הדדליין-זה-אתמול, אז לא הייתי זמין לעזור גם כשהם ביקשו עזרה. כן ניסיתי לזרוק איזו שאלה מנחה או שתיים - דברים כמו האם הסתכלתם בלוג? מה הוא מלמד אתכם? האם אתם יכולים לשחזר את הבעיה בסביבה שלכם? האם אתם יכולים להגדיר את הבעיה בצורה יותר מדוייקת מאשר "זה לא עובד" ? אבל חוץ מזה דחיתי בתקיפות את בקשות העזרה שלהם. </p><p dir="rtl" style="text-align: right;">אחרי איזשהו זמן המנהלת שלי ביקשה שאשים את המשימה הדחופה בצד לעשרים דקות כדי לראות אם אני יכול לקדם אותם קצת, כי הבעיה שהם מנסים להתמודד איתה תוקעת את כל הצוות. אז ביררתי שאפשר להתעכב בשעה (כי עד שחוזרים בחזרה לריכוז גם לוקח זמן) ואז הסתכלתי לראות מה קורה שם. ואז התרגזתי. לקח לי שלושים שניות כדי להבין מה הבעיה, ולראות בלוג שיש הפנייה לטיקט ספציפי בגיטהאב (שנכנס לפני שבוע) של אחד הכלים שאנחנו משתמשים בהם והצעה למעקף. אז אוקי, המעקף לא עובד, אבל ברגע שזיהיתי את זה לקח לי עוד רבע שעה למצוא איך להוריד גרסה של הכלי הנ"ל ולפתור את הבעיה. ועדיין, הכי טוב שקיבלתי משני אנשים שישבו לחקור את הבעיה היה "זה נופל, הנה הודעת השגיאה" (מנותקת מכל ההקשר). לא הבנה טובה יותר של מה קורה שם, לא דברים שהם כבר ניסו. כלום. </p><p dir="rtl" style="text-align: right;">אני לא אוהב לכעוס על שותפים לעבודה, אז המחשבה הזו נשארה איתי קצת, והקדשתי לעניין הזה תשומת לב. הבנתי למה זה כל כך הציק לי, וקיבלתי תזכורת למשהו שחמק ממני.הסיבה שמשהו כל כך טריוויאלי (כולה רבע שעה מהחיים שלי, ועוד בזמן עבודה) היא שאני פועל תמיד מתוך הנחה שכל מי שאני עובד איתו הוא גם חכם וגם עושה את המקסימום שהוא יכול. זו הנחה חזקה יחסית, אבל היא חלק חשוב ממה שגורם לי ליהנות בעבודה. כשדוגמאות בסגנון הזה מערערות אותה, אני תוהה בשביל מה אני משקיע כל כך הרבה מאמץ בניסיון לעבוד יחד איתם במקום פשוט להתקדם לבד עם משימות, ושיסתדרו. התבשלתי קצת במיץ של עצמי, ואחרי שעיבדתי את הנושא עוד קצת והזכרתי לעצמי את <a href="https://he.wikipedia.org/wiki/%D7%98%D7%A2%D7%95%D7%AA_%D7%94%D7%99%D7%99%D7%97%D7%95%D7%A1_%D7%94%D7%91%D7%A1%D7%99%D7%A1%D7%99%D7%AA" target="_blank">טעות הייחוס הבסיסית</a> (ההסבר בויקיפדיה קצת מעורפל, <a href="https://www.tntforthebrain.com/2006/07/fundamental-attribution-error.html?_escaped_fragment_=/2006/07/fundamental-attribution-error.html" target="_blank">כאן</a> יש הסבר טוב יותר), הבנתי שנכון יותר לומר ששני האנשים שלא הצליחו להתמודד עם משימה שאני מקטלג כטריוויאלית לא חיפפו, אלא עשו כמיטב יכולתם בנסיבות בהן מצאו את עצמם. יש לפחות שני גורמים שאני יכול לחשוב עליהם שתורמים לתוצאה הזו - לשניהם יש מלא משימות אחרות דחופות לא פחות מאשר המכשול הזה שנפל עליהם, ולשניהם ביחד יש רק טיפה יותר משנה של ניסיון, כך שהם עדיין לא ראו ארבע מאות דברים דומים. חוץ מזה, זה הזכיר לי שפתרון תקלות הוא כישור שצריך לפתח, לא משהו שקורה באופן אוטומטי, מה שמביא אותי למטרת הפוסט - אני רוצה לחלוק עם כל מי שיגיע לכאן (כנראה בטעות) את הדרך בה אני מתמודד עם תקלות טכניות בתקווה שזה יעזור למישהו. </p><p dir="rtl" style="text-align: right;">הדבר הראשון שכדאי לשים לב אליו כשמדברים על תפעול תקלות הוא שכמעט כל מי שמתעסק עם תוכנה יצטרך לעשות את זה, ולא מעט. מתכנתים, בודקי תוכנה אנשי DevOps (או איך שלא תקראו לצוות הזה שמתחזק את ג'נקינס בשביל כל הארגון) וכמובן שגם אנשי תמיכה. כולם עושים את זה - כל הזמן. הדבר השני הוא שיש לא מעט קווי דמיון בין תפעול תקלות לבין חקירת באגים, כך ששיפור ביכולת תפעול התקלות תהפוך אתכם גם לבודקי תוכנה טובים יותר. למעשה, ההבדל המרכזי בין שתי הפעילויות הוא המטרה: כשאנחנו מתפעלים תקלה יש משהו שלא עובד לנו, הוא חוסם לנו את הדרך למשהו שאנחנו צריכים לעשות, ואנחנו רוצים לגרום לו לא להיות כאן יותר, או לפחות למצוא דרך לעקוף את המכשול המציק הזה. בחקירת באגים, מצד שני, נרצה להבין מה הם הגורמים לתקלה, מה הנזק הפוטנציאלי וכמה הוא חשוב. כך שאם באג נעלם ככה סתם, אנחנו נצוד אותו, אבל אם תקלה נעלמת בלי שאנחנו יודעים למה, נגיד תודה רבה ונמשיך בחיינו. </p><p dir="rtl" style="text-align: right;"> אז, איך אני ניגש לתקלה? הנה מדריך קצר:</p><ol dir="rtl" style="text-align: right;"><li>האם זה ברור מאליו? לפעמים הודעת השגיאה טובה מספיק, או שראינו כבר משהו ממש דומה, או שהתסמינים פשוט צועקים "זו הבעיה". במקרים כאלה אפשר לדלג על שלב החקירה ולהתחיל לחפש פתרונות. </li><li>האם אני מצליח לשחזר את הבעיה? עדיף - בסביבת העבודה שלי. לא? מצויין, הבעיה הלכה למקום אחר ולא צריך לטפל בה. יכול להיות שהבעיה תחזור אחר כך, ואז אולי אשקיע קצת יותר זמן בניסיונות לשחזר אותה - כי בעיה שחוזרת פעם בחודש ושורפת לי זמן תעלה לי יותר מאשר להשקיע בה יומיים ולפתור אותה אחת ולתמיד. </li><li>בניית מודל. <br />אני מתחיל לבנות לעצמי תמונה בראש - אילו חלקים נעים במערכת? מי מדבר עם מי? מה היה צריך לקרות ובאילו שלבים? אילו משתני סביבה משפיעים על מה שקורה? מה השתנה לאחרונה ויכול להיות קשור?</li><li>לולאת החקירה:</li><ol><li>איסוף מידע. אפשר לחפש את הודעת השגיאה בגוגל, לצלול לתוך הלוגים בחיפושים אחרי מידע נוסף, לנסות ולעקוב אחרי המידע (למשל, שימוש בפרוקסי כדי לראות תעבורת רשת, או הדפסות מסויימות שאמורות לקרות לפני נקודת הכשל)</li><li>ניחוש - על בסיס המודל שיש לנו בידיים, מה יכול לגרום לבעיה? </li><li>ניסיון להפריך את הניחוש:<br /></li><ol><li>שחזור מינימלי שיאפשר לי לבודד את גורם התקלה ולראות שבלעדיו זה לא קורה (אם כן - זה לא זה)</li><li>חיפוש ראיות סותרות במידע שיש לי. </li></ol><li>שפצורים - אני מתחיל לשחק עם כל מיני פרמטרים קשורים לניחוש בתקווה שאחד מהם יעזור לפתור את הבעיה. </li><li>האם זה עבד? האם השינוי שעשיתי גרם לבעיה להיעלם? אם כן, סבבה. הבעיה נפתרה. </li><li>עדכון המודל עם המידע החדש שנאסף בשלבים הקודמים. </li><li>לפעמים נגמרים הרעיונות - חקרתי את כל הכיוונים שהצלחתי לחשוב עליהם, בחנתי את הכל הראיות שמצאתי, וזה לא מתקדם לשום מקום. עדיין יש לי שני כלים בארגז שאפשר להפעיל:</li><ol><li>אללה-הוא-אכבר: אני אעשה משהו שאין לו באמת סיכוי לעבוד, או אשנה איזה פרמטר אקראי שמצאתי בגוגל ולא נראה קשור.מי יודע, אולי יהיה לי מזל וזה יוסיף לי מידע, או אפילו, חלילה, יפתור את הבעיה? </li><li>לבקש עזרה - אין לי את כל הידע בעולם, ולא כל הרעיונות נובעים מהראש שלי. אולי למישהו אחר יהיו רעיונות חדשים שיקדמו אותי? אולי הוא יודע להפעיל כלי רלוונטי שאני לא מכיר? אני אתפוס מישהו, אציג לו בקצרה את ההתקדמות שעשיתי עד עכשיו (את המודל הנוכחי, וכמה צעדים שעשיתי כדי לבסס אותו) ונשב לעבוד ביחד על הבעיה. </li></ol></ol><li>זהו, בשלב הזה אנחנו צריכים כבר לדעת מה הבעיה - גם אם לא את הפיתרון עבורה. עכשיו זה רק החלק של למצוא איך לעקוף את גורם הבעיה ולגרום למה שאנחנו צריכים לעבוד. זה יכול להיות משהו קצר ופשוט כמו "לוודא שתיקייה מסויימת קיימת לפני שמריצים פקודה" או משהו קצת יותר כבד כמו לקמפל מחדש ספריית קוד-פתוח שאנחנו משתמשים בה כי יש שם באג. בדרך כלל, אם יודעים מה הבעיה, לא מאוד מורכב לפתור אותה. </li></ol><p dir="rtl" style="text-align: right;">זהו, פלוס מינוס. אני מקווה שזה יהיה שימושי למישהו. וכמובן - אם יש לכם דרכים נוספות להתמודד עם תקלות, אשמח לשמוע עליהן. <br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-7221956419134179830.post-46880303641854818532022-01-23T20:05:00.000+02:002022-01-23T20:05:03.133+02:00A world class test team?<p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://imgs.xkcd.com/comics/duty_calls.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="330" data-original-width="300" height="330" src="https://imgs.xkcd.com/comics/duty_calls.png" width="300" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: xx-small;">Source: XKCD</span><br /></td></tr></tbody></table><br /> </p><p>I've read Simon Prior's blog post <a href="https://simon-prior.uk/2022/01/21/building-a-world-class-test-team/" target="_blank">Building a world class test team</a>, and I didn't like what I've seen. </p><p>There's the overuse of my <a href="https://always-fearful.blogspot.com/2019/03/quality-in-closet.html">least favorite 4 letter word</a>, but putting that aside, I found that I'm disagreeing with the definitions he's using. His idea of a "good test team" is a safety net role that enables others to disregard their responsibilities and directly contribute to worse software. The "great" test team is the first thing that starts to get close to an almost decent team, as he's added "preventing defects" which I chose to interpret generously as being involved in crafting the requirements and planning the work with the other members of the software engineering group. <br />Then, the "World class test team" is complete contradiction. It's described as "all of the above" AND some properties of what I would call pretty good testing - not being the scapegoat, coaching, improving other functions work - a lot of things that are not very compatible with owning the large part of the testing effort. Sure, I can imagine this working in some places, but it would be like hammering a bolt that doesn't fit well into place - it will work in the end, but you've invested more effort than you should have, and it will be a mess to deal with it every time you need to change something. </p><p>Instead, I want to suggest another definition - in almost all cases, a world-class testing team is one that has disbanded (or is actively working towards that goal). As much as I like being a tester by role as well as by identification, my main aspiration is to bring the place I work in to a state where they don't need dedicated testers (note - I said "don't need",I didn't mean "just get rid of") - the way to achieve this is to embed the testing mindset and activities as part of every step in the process, from feature definition to deployment. Something very much like <a href="https://danashby.co.uk/2016/10/19/continuous-testing-in-devops/" target="_blank">Dan Ashby's model</a> of where testing fit in DevOps. It will take a while to help build the necessary infrastructure needed for self-testing and even longer to instill a culture where testing is part of being a professional, but if we want "world class" testing, one that people can learn from, it's this property we are looking for. </p><p><br /></p><p>In addition to this, I found two points of disagreement with the practical advice of building a good test team. The first one is using "9 to 5 testers" as a derogatory term. It's covered beneath some layers of "there's nothing wrong with it", but still confounds working reasonable hours with box-ticking and lack of development. When building a team, respecting their private time is super important, and accommodating improvement within regular working hours is key to building the team. want to fend off stagnation and rigidness? find a term that will not push towards expecting people to put their free time for the business. </p><p>The second issue is the advice to diversify with hiring. It's not a bad idea, but in many cases it's not a viable option - by my experience, finding senior testers is way more difficult than finding experienced coders, and it seems that it is so <a href="https://visible-quality.blogspot.com/2022/01/in-search-of-contemporary-exploratory.html" target="_blank">in other countries as well</a><br /></p><p> </p>Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-7221956419134179830.post-82309695324658778602022-01-17T10:04:00.002+02:002022-01-17T10:04:15.357+02:00Deep work - Book review<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://impartiallyderivative.com/wp-content/uploads/2018/10/DeepWork-6.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="800" data-original-width="600" height="320" src="https://impartiallyderivative.com/wp-content/uploads/2018/10/DeepWork-6.jpg" width="240" /></a></div><br /> <p></p><p><br /> <br /><br />Following a recommendation from <a href="http://www.tomercode.com/2021/01/blog-post.html" target="_blank">Tomer Cohen</a> (He wrote in Hebrew), I finally got to listen to Cal Newport's "Deep Work: Rules for Focused Success in a Distracted World", and my reaction to it is quite ambivalent.<br /><br />The book starts with a very tempting, yet quite weak proposition of value: Deep work is valuable, and its value increases with the proliferation of knowledge work in today's world. On the other hand, increasing connectivity and interruptions makes this particular skill rarer, so it can make you stand out in your profession and earn more. It then moves on to claim that deep work will also make you happier. Basically - it's a panacea to all of the modern world's ailments. And, like the lovely book "Calling Bullshit:The art of skepticism in a data driven world" has reminded us - great claims requires great deal of evidence. Not only that this book does not provide that sort of evidence, it goes on to actually show a counter-example, acknowledging that deep work is not the only way to wealth, and dismissing it with a statement along the lines of "even though there are other ways to wealth, it does not mean that deep work isn't a way to put you ahead of everyone else (perhaps except the CEO that provided the counter-example, but for most of us it's not an option)". Convinced? Neither was I. <br /><br />But, before I go on rambling about deep work, it might be worth it to define the term. The definition used in the book is: "Professional activities performed in a state of distraction-free<br />concentration that push your cognitive capabilities to their limit. These efforts create new value, improve your skill, and are hard to replicate". long story short - deep work is hard, valuable mental work.<br /><br />Anyway, while the evidence fall short of the grand claims, the book actually tells a compelling story, and if we accept some of the assumptions made hastily - its conclusions are inevitable. For instance, one reason that deep work should make one happier goes as follows: Quote the work in Winifred Gallagher's RAPT to base that people happiness has more to do with what they focus on than with what objectively happens to them, state (assume) that deep work is focusing on more meaningful activity than the shallow trudge of interrupts, conclude that since one spends more time focused on meaningful events, they will feel a greater sense of meaning in life, or quote Mihaly Csikszentmihalyi's "Flow", assume that deep work is more prone to lead a person to the sweet spot between challenge and success to greatly increase one's chances of experiencing flow, and since being in a state of flow is known to increase happiness, so does deep work. <br />As you might have noticed, the assumptions are quite plausible. good enough to be motivating. One issue it sidesteps completely is the question "are there paths other than deep work that are as likely to generate comparable levels of happiness or value? How do we identify we are on such a path or that we might benefit more from one?"<br /><br />The rest of the book is about tips to incorporate deep work in our day to day. Most advice here is at least convincing, and the author takes sufficient time to base some of the claims - shutting down interruptions and educating your environment can work in most contexts, focus is a skill that needs constant training and is prone to deterioration, actually resting and not doing any work after the work day is a way to increase productivity, and so on. My unease on this part has more to do with the totality of the approaches described in the book. Commitment to deep work is subjugating one's entire being: The default is being in a state of deep work, pausing only for as brief periods as necessary. A lot of it feels like what you might expect in a time-management book, only that the focus is not about "not wasting time", but rather about constantly training your deep working skill. I got tired only from trying to grok the message. <br /><br />This fatalistic approach is a bit much, but it helps to emphasize that deep work is demanding, even if I believe that it can be done in a less extreme way it is still useful to learn the purists approach, where it is the clearest. It also helps to see various examples of building a schedule for deep work. It can be getting up early to spend a couple of hours in full focus before the day starts for other people who will create interference, it can be planning work so that we divide our calendar time between weeks of solitary focus and those of interruption heavy busy-work. Then there's the part where I felt I was being cheated - after explaining at length how deep work is something that takes time to start (research I think I recall from other places states that it takes about 15 minutes to enter "the zone") and that 4 half-hours of deep work are not as effective as 2 straight hours of it, the author goes to describe the "journalistic" approach which is based on the assumption that people trained in deep work can skip this time and just dive into deep work immediately whenever they have a spare 10 minutes or more. It really felt as a way to say "I'm doing deep work" while ditching aside all of the principles mentioned in the book before. It was also an unpleasant surprise to hear that this is the author's main mode of work - Preaching for deep work and claiming to be quite adept at it, then redefining it as "I do deep work when I decide to do it and have free time from my other distractions" sure does feel like hypocrisy. <br /><br /><br />That being said, I still took some insights with me.<br /><br />First of all, I'm still debating with myself about the actual value of deep work in my context - much of my day is about helping others, jumping for a short period and dropping a question that might set things on a better path, mentoring others to do the actual work themselves. Sure, some of the work is done by me, and I find great joy in being able to still contribute directly, but my added value there is not always significant - I might do things faster or a bit better than the less experienced people in my team, but in the time it takes me to do one unit of work myself, I can probably help three team mates to do better work and complete 3 units of work that will be 80% as good as if I would have done it. So, where do I see more value? If I'd try to frame this in deep-work lingo, I'd borrow some of Kahneman's Thinking fast and slow and claim that by practicing, I managed to move some of the skills that require deep work and concentration to the more automatic parts of my mind and now I provide value by doing deep work in a shallow fashion - using those automatic skills to improve my environment. This means that my skill is based on deep work, and even if I believe that most of the value I provide is collaborative and interruption heavy (collaboration can, in rare cases, be deep work in itself, but the book usually treats it as solitary work), I should still invest some time in deep work to expand the base I'm building upon and to make sure I'm still connected to the work. <br /><br />It also serves as reminder - not everything I do is equally important, and there are some things such as e-mail or instant messaging that can be pushed aside instead of sapping my attention. <br />Another thing that intrigues me is the claim that we are addicted to interruptions - I'm definitely going to try some of the tactics to train my mind to be more focused, such as defining breaks from concentration (perhaps using pomodoro) and learn to stick to them - no breaks until the buzzer. </p><p>I think that the most fitting analogy to this book would be an olympic runner sharing tips on how to become a better runner. This athlete will surely recognize that the amount of effort needed to get to their level is ridiculous, but even when they will try to dial down the effort, they cannot unsee what they know - What you eat, how you sleep, how balanced are your core muscles, all of this has an impact on your running. Getting advice from someone like that will create the impression that there is nothing more important in life, which is true for very specific cases. <br />This is why I probably won't invest the effort to become a professional deep-worker who's managed by the hunt for deep work, it might be relevant for highly competitive fields such as academia or writing, but I suspect the reward is much smaller in software development where good employees are rather hard to find. A focused deep worker might be a lot better than me, but the difficulty of measuring productivity and the fact that most places require the sort of work that my skills are more than enough for - it will be like purchasing a Ferrari to stand in traffic. It *can* get to silly speed, but you would do just the same with a car tenth of that price. I need and want to be good at my work, trying to be the best of the best is not worth the effort. Instead, I'll treat it the same way I treat my bicycle. It's fun, it builds some sort of muscle, and I gain a lot from this as a hobby. <br /></p>Unknownnoreply@blogger.com0