well, as I said, I have incorporated a very simple weighted failure rate
into the strategy, and I have found quite encouraging results. The recall
looks better than earlier tests. I am attaching two charts with data
compiled from runs with 3000 training rounds and 2000 simulation (5000 test
runs analyzed in total):
- The recall by running set size (As shown, it reaches 80% with 300
- The index of failure in the priority queue (running set: 500, training
3000, simulation 2000)
It is interesting to look at chart number 2:
The first 10 or so places have a very high count of found failures. These
most likely come from repeated failures (tests that failed in the previous
run and were caught in the next one). The next ones have a skew to the
right, and these come from the file-change model.
I am glad of these new results : ). I have a couple new ideas to try to
push the recall a bit further up, but I wanted to show the progress first.
Also, I will do a thorough code review before any new changes, to make sure
that the results are valid. Interestingly enough, in this new strategy the
code is simpler.
Also, I will run a test with a more long term period (20,000 training,
20,000 simulation), to see if the recall degrades as time passes and we
miss more failures.
On Fri, Jun 27, 2014 at 4:48 PM, Pablo Estrada <polecito.em@xxxxxxxxx>
I took the last couple of days working on a new strategy to calculate the
relevance of a test. The results are not sufficient by themselves, but I
believe they point to an interesting direction. This strategy uses that
rate of co-occurrence of events to estimate the relevance of a test, and
the events that it uses are the following:
- File editions since last run
- Test failure in last run
The strategy has also two stages:
1. Training stage
2. Executing stage
In the training stage, it goes through the available data, and does the
- If test A failed:
- It counts and stores all the files that were edited since the last
test_run (the last test_run depends on BRANCH, PLATFORM, and other factors)
- If test A failed also in the previous test run, it also counts that
In the executing stage, the training algorithm is still applied, but the
decision of whether a test runs is based on its relevance, the relevance is
calculated as the sum of the following:
- The percentage of times a test has failed in two subsequent
test_runs, multiplied by whether the test failed in the previous run (if
the test didn't fail in the previous run, this quantity is 0)
- For each file that was edited since the last test_run, the
percentage of times that the test has failed after this file was edited
(The explanation is a bit clumsy, I can clear it up if you wish so)
The results have not been too bad, nor too good. With a running set of 200
tests, a training phase of 3000 test runs, and an executing stage of 2000
test runs, I have achieved recall of 0.50. It's not too great, nor too bad.
Nonetheless, while running tests, I found something interesting:
- I removed the first factor of the relevance. I decided to not care
about whether a test failed in the previous test run. I was only using the
file-change factor. Naturally, the recall decreased, from 0.50 to 0.39 (the
decrease was not too big)... and the distribution of failed tests in the
priority queue had a good skew towards the front of the queue (so it seems
that the files help somewhat, to indicate the likelihood of a failure). I
attached this chart.
An interesting problem that I encountered was that about 50% of the
test_runs don't have any file changes nor test failures, and so the
relevance of all tests is zero. Here is where the original strategy (a
weighted average of failures) could be useful, so that even if we don't
have any information to guess which tests to run, we just go ahead and run
the ones that have failed the most, recently.
I will work on mixing up both strategies a bit in the next few days, and
see what comes of that.
By the way, I pushed the code to github. The code is completely different,
so may be better to wait until I have new results soon.