(sorry, forgot the list in my reply, resending)
Hi Pablo,
On 03.08.2014 17:51, Pablo Estrada wrote:
Hi Elena,
One thing that I want to see there is fully developed platform mode. I
see
that mode option is still there, so it should not be difficult. I
actually
did it myself while experimenting, but since I only made hasty and crude
changes, I don't expect them to be useful.
I'm not sure what code you are referring to. Can you be more specific on
what seems to be missing? I might have missed something when migrating
from
the previous architecture...
I was mainly referring to the learning stage. Currently, the learning
stage is "global". You go through X test runs, collect data, distribute it
between platform-specific queues, and from X+1 test run you start
predicting based on whatever platform-specific data you have at the moment.
But this is bound to cause rather sporadic quality of prediction, because
it could happen that out of 3000 learning runs, 1000 belongs to platform A,
while platform B only had 100, and platform C was introduced later, after
your learning cycle. So, for platform B the statistical data will be very
limited, and for platform C there will be none -- you will simply start
randomizing tests from the very beginning (or using data from other
platforms as you suggest below, which is still not quite the same as pure
platform-specific approach).
It seems more reasonable, if the platform-specific mode is used, to do
learning per platform too. It is not just about current investigation
activity, but about the real-life implementation too.
Lets suppose tomorrow we start collecting the data and calculating the
metrics.
Some platforms will run more often than others, so lets say in 2 weeks you
will have X test runs on these platforms so you can start predicting for
them; while other platforms will run less frequently, and it will take 1
month to collect the same amount of data.
And 2 months later there will be Ubuntu Utopic Unicorn which will have no
statistical data at all, and it will be cruel to jump into predicting there
right away, without any statistical data at all.
It sounds more complicated than it is, in fact pretty much all you need to
add to your algorithm is making 'count' in your run_simulation a dict
rather than a constant.
So, I imagine that when you store your metrics after a test run, you will
also store a number of test runs per platform, and only start predicting
for this particular platform when the count for it reaches the configured
number.
Of the code that's definitely not there, there are a couple things that
could be added:
1. When we calculate the relevance of a test on a given platform, we
might
want to set the relevance to 0, or we might want to derive a default
relevance from other platforms (An average, the 'standard', etc...).
Currently, it's just set to 0.
I think you could combine this idea with what was described above. While
it makes sense to run *some* full learning cycles on a new platform, it
does not have to be thousands, especially since some non-LTS platforms come
and go awfully fast. So, we run these no-too-many cycles, get clean
platform-specific data, and if necessary enrich it with the other
platforms' data.
2. We might also, just in case, want to keep the 'standard' queue for
when
we don't have the data for this platform (related to the previous point).
If we do what's described above, we should always have data for the
platform.
But if you mean calculating and storing the standard metrics, then yes --
since we are going to store the values rather than re-calculate them every
time, there is no reason to be greedy about it. It might even make sense to
calculate both metrics that you developed, too. Who knows maybe one day
we'll find out that the other one gives us better results.
It doesn't matter in which order they fail/finish; the problem is, when
builder2 starts, it doesn't have information about builder1 results, and
builder3 doesn't know anything about the first two. So, the metric for
test
X could not be increased yet.
But in your current calculation, it is. So, naturally, if we happen to
catch the failure on builder1, the metric raises dramatically, and the
failure will be definitely caught on builders 2 and 3.
It is especially important now, when you use incoming lists, and the
running sets might be not identical for builders 1-3 even in standard
mode.
Right, I see your point. Although if test_run 1 would catch the error,
test_run 2, although it would be using the same data. might not catch the
same errors if the running set makes it such that they are pushed out due
to lower relevance. The effect might not be too big, but it definitely
has
potential to affect the results.
Over-pessimistic part:
It is similar to the previous one, but look at the same problem from a
different angle. Suppose the push broke test X, and the test started
failing on all builders (platforms). So, you have 20 failures, one per
test
run, for the same push. Now, suppose you caught it on one platform but
not
on others. Your statistics will still show 19 failures missed vs 1
failure
caught, and recall will be dreadful (~0.05). But in fact, the goal is
achieved: the failure has been caught for this push. It doesn't really
matter whether you catch it 1 time or 20 times. So, recall here should
be 1.
It should mainly affect per-platform approach, but probably the standard
one can also suffer if running sets are not identical for all builders.
Right. It seems that solving these two issues is non-trivial (the
test_run
table does not contain duration of the test_run, or anything). But we can
keep in mind these issues.
Right. At this point it doesn't even make sense to solve hem -- in
real-life application, the first one will be gone naturally, just because
there will be no data from unfinished test runs.
The second one only affects recall calculation, in other words --
evaluation of the algorithm. It is interesting from theoretical point of
view, but not critical for real-life application.
I fixed up the repositories with updated versions of the queries, as well
as instructions in the README on how to generate them.
Now I am looking a bit at the buildbot code, just to try to suggest some
design ideas for adding the statistician and the pythia into the MTR
related classes.
As you know, we have the soft pencil-down in a few days, and the hard one
a week later. At this point, there isn't much reason to keep frantically
improving the algorithm (which is never perfect), so you are right not
planning on it.
In the remaining time I suggest to
- address the points above;
- make sure that everything that should be configurable is configurable
(algorithm, mode, learning set, db connection details);
- create structures to store the metrics and reading to/writing from the
database;
- make sure the predicting and the calculating part can be called
separately;
- update documentation, clean up logging and code in general.
As long as we have these two parts easily callable, we will find a place
in buildbot/MTR to put them to, so don't waste too much time on it.
Regards,
Elena
Regards
Pablo
