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@gmail.com> wrote:

Hi Elena,

On Thu, Jul 24, 2014 at 8:06 PM, Elena Stepanova <elenst@montyprogram.com> wrote:

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