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Hi Pablo, Thanks for the update, I'm looking into it.There is one more important factor to choose which strategy to put the further effort on. Do they perform similarly time-wise?
I mean, you now ran the same sets of tests on both strategies. Did it take approximately the same time? And in case you measured it, what about 3000 + 1 rounds, which is closer to the real-life test case?
And what absolute time does one round take? I realize it depends on the machine and other things, but roughly -- is it seconds, or minutes, or tens of minutes?
We should constantly watch it, because the whole point is to reduce test execution time; but the test execution time will include using the tool, so if it turns out that it takes as much time as we later save on tests, doing it makes little sense.
Regards, Elena On 27.07.2014 11:51, Pablo Estrada wrote:
Hello Elena, Concluding with the results of the recent experimentation, here is the available information: I have ported the basic code for the 'original' strategy into the core-wrapper architecture, and uploaded it to the 'master' branch. Now both strategies can be tested equivalently. Branch: master <https://github.com/pabloem/Kokiri> - Original strategy, using exponential decay. The performance increased a little bit after incorporating randomizing of the end of the queue. Branch: core-wrapper_architecture <https://github.com/pabloem/Kokiri/tree/core-wrapper_architecture> - 'New' strategy using co occurrence between file changes and failures to calculate relevance. I think they are both reasonably useful strategies. My theory is that the 'original' strategy performs better with the input_test lists is that we now know which tests ran, and so only the relevance of tests which ran is affected (whereas previously, all tests were having their relevance reduced). The tests were run with *3000 rounds of training* and *7000 rounds of prediction*. I think that now the most reasonable option would be to gather data for a longer period, just to be sure that the performance of the 'original' strategy holds for the long term. We already discussed that it would be desirable that buildbot incorporated functionality to keep track of which tests were run, or considered to run (since buildbot already parses the output of MTR, the changes should be quite quick, but I understand that being a production system, extreme care must be had in the changes and the design). Finally, I fixed the chart comparing the results, sorry about the confusion yesterday. Let me know what you think, and how you'd like to proceed now. : ) Regards Pablo On Sat, Jul 26, 2014 at 8:26 PM, Pablo Estrada <polecito.em@xxxxxxxxx> wrote:Hi Elena, I just ran the tests comparing both strategies. To my surprise, according to the tests, the results from the 'original' strategy are a lot higher that the 'new' strategy. The difference in results might come from one of many possibilities, but I feel it's the following: Using the lists of run tests allows the relevance of a test to decrease only if it is considered to run and it runs. That way, tests with high relevance that would run, but were not in the list, don't run and thus are able to be hit their failures later on, rather than losing relevance. I will have charts in a few hours, and I will review the code more deeply, to make sure that the results are accurate. For now I can inform you that for a 50% size of the running set, the 'original' strategy, with no randomization, time factor or edit factor achieved a recall of 0.90 in the tests that I ran. Regards Pablo On Thu, Jul 24, 2014 at 8:18 PM, Pablo Estrada <polecito.em@xxxxxxxxx> wrote:Hi Elena, On Thu, Jul 24, 2014 at 8:06 PM, Elena Stepanova <elenst@xxxxxxxxxxxxxxxxwrote:Hi Pablo, Okay, thanks for the update. As I understand, the last two graphs were for the new strategy taking into account all edited files, no branch/platform, no time factor?- Yes, new strategy. Using 'co-occurrence' of code file edits and failures. Also a weighted average of failures. - No time factor. - No branch/platform scores are kept. The data for the tests is the same, no matter platform. - But when calculating relevance, we use the failures occurred in the last run as parameter. The last run does depend of branch and platform.Also, if it's not too long and if it's possible with your current code, can you run the old strategy on the same exact data, learning/running set, and input files, so that we could clearly see the difference?I have not incorporated the logic for input file list for the old strategy, but I will work on it, and it should be ready by tomorrow, hopefully.I suppose your new tree does not include the input lists? Are you using the raw log files, or have you pre-processed them and made clean lists? If you are using the raw files, did you rename them?It does not include them. I am using the raw files. I included a tiny shell (downlaod_files.sh) that you can execute to download and decompress the files in the directory where the program will look by default. Also, I forgot to change it when uploading, but in basic_testcase.py, you would need to erase the file_dir parameter passed to s.wrapper(), so that the program defaults in looking for the files. Regards Pablo
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