Predictive test selection for SUSE Manager
an idea by jordimassaguerpla
Updated over 4 years ago. 6 hacker ♥️. 3 followers. Has no hacker: grab it!

I once had a bad dream.

I started good, a sunny day. I had just fixed an issue and push it to my fork, in order to create a Pull Request. I was happy. It felt awesome to have found a fix so elegant. Two lines of code.

But then, something happened. A cloud appear in the sky and partially hide the sun. github triggered the end to end test suite. You could see the waiting icon, you could almost hear the engines starting, ... a lightning and a thunder appeared in the sky, the sky turned dark and the e2e test started in our private jenkins server.

1 hour passed by... 2 hours .... the e2e tests still running ... 4 hours, not even 50%. Finally, I had to go, get some dinner, get some rest, so I decided to look at it the next day.

Sleep was not good. Dreaming the tests would fail and the deadline be missed .... I couldn't sleep no more so I wake up early in the morning, before sunrise. The computer still opened was lightning up the room. Out, the storm turned out to be a heavy rain. And the tests did not passed! 4 hours and half after starting them, so half an hour after leaving for dinner, and there was a fail test.

Finding out what went wrong took 2 hours...it was not even related to the code, but an infrastructure issue. A timeout when connecting to a mirror which was being restarted when the tests run. Apparently we hit a maintenance window.

Damn! So let's just "restart" the tests. This time, I decided to put an alarm 4 hours after and switch to another task, just to avoid the anxiety of the previous evening looking at the results. 4 hours passed by, and the tests are good. It is not raining anymore, a git of sun is filtering throw the window, hope is in the air. 4 more hours, and the tests still have not failed. Going to dinner and bed again and putting the alarm early in the morning. First thing in the morning I looked at the tests ... running again, feels good. All tests have passed and there is only one final "cleanup" state. Shower, breakfeast, sun is shining again!

Finally, all is green, so let's go and push the merge button ... oh oh ... clouds hide the sun again ... where is the merge button?? no way, there is a conflict with the code in master and I can't merge!!! I can't merge!!! shocked, I looked into the history ... John merged a PR overnight that was touching the same file ... hopeless I start crying on my desk...

Scared?

This is not so different on what could happen if we were running the whole e2e test suite in the SUSE Manager (uyuni) Pull Requests. However, not running any e2e tests has also a bad consequence. We find the issues after code has been merged in master, and then we spend days looking at what could have caused this, reverting commits, and starting again.

However, if we could run a subset of the e2e tests, we could shorten the time it takes and so run them at the PR level, so no broken code could get merged.

The thing is , how do we select which tests to run?

This project is about using Machine Learning to do predictive test selection, thus selecting which tests to run based on the history of previous test runs.

Inspired by: https://engineering.fb.com/2018/11/21/developer-tools/predictive-test-selection/

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Hack Week 20

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  • over 4 years ago: PSuarezHernandez liked this project.
  • almost 5 years ago: llansky3 liked this project.
  • almost 5 years ago: ories liked this project.
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  • almost 5 years ago: RDiasMateus liked this project.
  • almost 5 years ago: pagarcia liked this project.
  • almost 5 years ago: jordimassaguerpla added keyword "ci" to this project.
  • almost 5 years ago: jordimassaguerpla added keyword "qa" to this project.
  • almost 5 years ago: jordimassaguerpla added keyword "ai" to this project.
  • almost 5 years ago: jordimassaguerpla added keyword "ml" to this project.
  • almost 5 years ago: jordimassaguerpla originated this project.


    • Comments

    • llansky3
      almost 5 years ago by llansky3 | Reply

      This could have some wider potential - clever picking of tests to shorten the time yet maximize the findings (and learning that in time based on test execution history to predict). I can see how that saves tons of money in cases where test on a real industrial machines need to be executed (e.g. cost of running engine on a test bed is 5-10k$ per day so there is huge difference if you need to run for 1 or 3 days)

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