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[Pablo Estrada <polecito.em@xxxxxxxxx>]

Hello Sergei and all,
First of all, I'll explain quickly the terms that I was using:

   - *test_suite, test suite, test case* - When I say test suite or test
   case, I am referring to a single test file. For instance '
   *pbxt.group_min_max*'. They are the ones that fail, and whose failures
   we want to attempt to predict.
   - *test_run, test run* - When I use this term, I refer to an entry in
   the *test_run* table of the database. A test run is a set of
   *test_suites* that run together at a certain time.

I have in place now a basic script to do the simulations. I have tried to
keep the code clear, and I will upload a repository to github soon.
I have already run simulations on the data. The simulations used 2000
test_runs as training data, and then attempted to predict behavior on the
following 3000 test_runs. Of course, maybe a wider spectrum of data might
be needed to truly asses the algorithm.

I used four different ways to calculate a 'relevancy index' for a test:

   1. Keep a relevancy index by test case
   2. Keep a relevancy index by test case by platform
   3. Keep a relevancy index by test case by branch
   4. Keep a relevancy index by test case by branch by platform (mixed)

I graphed the results. The graph is attached. As can be seen from the
graph, the platform and the mixed model proved to be the best for recall.
I feel the results were quite similar to what Sergei encountered.

I have not run the tests on a larger set of data (the data dump that I have
available contains 200,000 test_runs, so in theory I could test the
algorithm with all this data)... I feel that I want to consider a couple
things before going on to big testing:

I feel that there is a bit of a potential fallacy in the model that I'm
following. Here's why:
The problem that I find in the model is that we don't know a-priori when a
test will fail for the first time. Strictly speaking, in the model, if a
test doesn't fail for the first time, it never starts running at all. In
the implementation that I made, I am using the first failure of each test
to start giving it a relevancy test (so the test would have to fail before
it even qualifies to run).
This results in a really high recall rate because it is natural that if a
test fails once, it might fail pretty soon after, so although we might have
missed the first failure, we still consider that we didn't miss it, and
based on it we will catch the two or three failures that come right after.
This inflates the recall rate of 'subsequent' failures, but it is not very
helpful when trying to catch failures that are not part of a trend... I
feel this is not realistic.

Here are changes that I'd like to incorporate to the model:

   1. The failure rate should stay, and should still be measured with
   exponential decay or weighted average
   2. Include a new measure that increases relevancy: Time since last run.
   The relevancy index should have a component that makes the test more
   relevant the longer it spends not running
      1. A problem with this is that *test suites* that might have stopped
      being used will stay and compete for resources, although in reality they
      would not be relevant anymore
   3. Include also correlation. I still don't have a great idea of how
   correlation will be considered, but it's something like this:
      1. The data contains the list of test_runs where each test_suite has
      failed. If two test suites have failed together a certain percentage of
      times (>30%?), then when test A fails, the relevancy test of test B also
      goes up... and when test A runs without failing, the relevancy
test of test
      B goes down too.
      2. Using only the times that tests fail together seems like a good
      heuristic, without having to calculate the total correlation of all the
      history of all the combinations of tests.

If these measures were to be incorporated, a couple of changes would also
have to be considered:

   1. Failures that are* not spotted* *on a test_run* might be *able to be
   spotted *on the *next* two or three or *N test_runs*? What do you think?
   2. Considering these measures, probably *recall* will be *negatively
   affected*, but I feel that the model would be *more realistic*.

Any input on my new suggestions? If all seems okay, I will proceed on to
try to implement these.
Also, I will soon upload the information so far to github. Can I also
upload queries made to the database? Or are these private?

Regards
Pablo


On Wed, May 7, 2014 at 7:41 PM, Sergei Golubchik <serg@xxxxxxxxxxx> wrote:

> Hi, Pablo!
>
> On May 07, Pablo Estrada wrote:
> >
> > So here's what I'll do for the simulations:
> >
> > *1. Calculating the: "Relevancy index"* for a test, I have considered two
> > simple options so far:
> >
> >    - *Exponential decay*: The relevancy index of a test is the *sum over
> >    each failure* of( *exp((FailureTime - CurrentTime)/DecayRate))*. It
> >    decreases exponentially as time passes, and increases if the test
> fails.
> >       - DecayRate is
> >       - i.e. If TestA failed at days 5 and 7, and now is day 9, RI will
> >       be (exp(5-9)+exp(7-9)) = (exp(-4)+exp(-2)).
> >       - The unit to measure time is just seconds in UNIX_TIMESTAMP
> >    - *Weighted moving average*: The relevancy index of a test is:
> *R[now] =
> >    R[now-1]*alpha + fail*(1-alpha)*, where fail is 1 if the test failed
> in
> >    this run, and 0 if it did not fail. The value is between 1 and 0. It
> >    decreases slowly if a test runs without failing, and it increases
> slowly if
> >    the test fails.
> >       - 0 < alpha < 1 (Initially set at 0.95 for testing).
> >       - i.e. If TestB failed for the first time in the last run, and
> again
> >       in this run: R[t] = 1*0.95 + 1*0.5 = 1
> >       - If test B ran once more and did not fail, then: R[t+1] = 1*0.95 +
> >       0*0.5 = 0.95
> >       - The *advantage of this method* is that it doesn't have to look at
> >       the whole history every time it's calculated (unlike the
> exponential decay
> >       method)
>
> you don't need to look at the whole history for the exponential decay.
> Because it is
>
>   exp((FailureTime - CurrentTime)/DecayRate)
>
> You simply have
>
>   R[t] = exp(FailureTime/DecayRate) / exp(t/DecayRate)
>   R[t+1] = R[t] / exp(1/DecayRate)   (if there was no failure)
>
> >       - Much like TCP protocol (
> http://www.cl.cam.ac.uk/~jac22/books/mm/book/node153.html)
> >
> > Regarding the *Relevancy Index*, it can be calculated grouping test
> results
> > in many ways: *Roughly* using test_name+variation, or *more granularly*
> by
> > *including* *branch* and *platform*. I'll add some thoughts regarding
> these
> > options at the bottom of the email.
>
> I've tested these options earlier, you may want to try them all too and
> see which one delivers better results.
>
> > *2. *To* run the simulation*, I'll gather data from the first few
> thousands
> > of test_run entries, and then start simulating results. Here's what I'll
> do:
> >
> >    1. *Gather data *first few thousands of test_run entries (i.e. 4
> >    thousand)
> >    2. After N thousand test_runs, I'll go through the test_run entries
> *one
> >    by one*, and using the data gathered to that point, I will select
> '*running
> >    sets*' of *100* *test suites* to run on each test_run entry. (The
> number
> >    can be adjusted)
>
> Absolutely, it should be. This is the main parameter we can tune, after
> all. The larger your running set is, the better will be the recall.
>
> May be not now but later, but it would be very useful to see these
> graphs, recall as a function of the running set size. It's important to
> know whether by increasing the running set by 10% we get 1% recall
> increase of 70% recall increase (as you've seen, there's a region when
> recall increases very fast as the running set grows).
>
> >    3. If in this *test_run* entry, the list of *failed tests* contains
> >    tests that are *NOT part* of the *running set*, the failure will be
> >    ignored, and so the information of this failure will be lost (not
> used as
> >    part of the relevancy index). *(See Comment 2)*
> >    4. If the set of *failed tests *in the *test_run* entry intersect with
> >    the *running_set*, this is better *recall*. This information will be
> >    used to continue calculating the *relevancy index*.
>
> Could you explain the terminology you're using?
> What is a "test suite" and what is a "test run"?
> How will you calculate the "recall"?
>
> > According to the results obtained from the simulations, we can adjust the
> > algorithm (i.e. to consider *relevancy index by* *platform* and *branch*,
> > etc.)
> >
> > Comments about the *relevancy index:*
> >
> >    - The methods to calculate the relevancy index are very simple. There
> >    are some other useful metrics that could be incorporated
> >       - *Time since last run. *With the current methods, if a*
> > test*completely *stops
> >       running*, it only* becomes less relevant with time*, and so even if
> >       it could expose defects, it doesn't get to run because its
> > relevancy index
> >       is just going down. Incorporating a function that* increases the
> >       relevancy index* as the *time since the last run* *increases* can
> >       help solve this issue. I believe this measure will be useful.
>
> Right. I will not comment on this measure now.
> But I absolutely agree that this is an issue that must be solved.
>
> >       - *Correlation between test failures*. If two tests tend to fail
> >       together, is it better to just run one of them? Incorporating
> > this measure
> >       seems difficult, but it is on the table, in case we should
> consider it.
>
> Agree. Taking correlations into account looks very promising, but it
> does seem to be difficult :)
>
> >    - As you might have seen, I decided to not consider any data concerned
> >    with *code changes*. I'll work like this and see if the results are
> >    satisfactory.
>
> Right!
>
> > Comments regarding *buildbot infrasturcture:*
> > These comments are out of the scope of this project, but it would be very
> > desirable features for the buildbot infrastructure.
> >
> >    - Unfortunately, given the data available in the database, it is NOT
> >    possible to know *which tests ran* on each *test_run*. This
> information
> >    would be very useful, as it would help estimate the *exact failure
> > rate*of a test. I didn't look into the code, but it seems that *class
> >    MtrLogObserver*(
> http://buildbot.sourcearchive.com/documentation/0.8.3p1-1/mtrlogobserver_8py_source.html
> )
> > contains
> >    most of the infrastructure necessary to just add one or two more
> tables (
> >    *test_suite*, and *test_suite_test_run*), some code, and start keeping
> >    track of this information.
> >    - Another problem with the data available in the database is that it
> is
> >    not possible to know *how many test suites exist*. It is only possible
> >    to estimate *how many different test suites have failed*. This would
> >    also be helpful information.
> >    - Actually, this information would be useful not only for this
> project,
> >    but in general for book-keeping of the development of MariaDB.
>
> Regards,
> Sergei
>

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