The pattern I’ve seen in lots of other organizations: When a few tests intermittently give different answers, people attribute the intermittence to the tests, quickly lose trust in the entire suite, and increasingly discount failures.
If we’re going to attend to every failure in the larger suite, then we won’t suffer that fate, and I’m in favor of deleting the Flaky tag. Dale > On Jul 5, 2018, at 8:15 PM, Dan Smith <dsm...@pivotal.io> wrote: > > Honestly I've never liked the flaky category. What it means is that at some > point in the past, we decided to put off tracking down and fixing a failure > and now we're left with a bug number and a description and that's it. > > I think we will be better off if we just get rid of the flaky category > entirely. That way no one can label anything else as flaky and push it off > for later, and if flaky tests fail again we will actually prioritize and > fix them instead of ignoring them. > > I think Patrick was looking at rerunning the flaky tests to see what is > still failing. How about we just run the whole flaky suite some number of > times (100?), fix whatever is still failing and close out and remove the > category from the rest? > > I think will we get more benefit from shaking out and fixing the issues we > have in the current codebase than we will from carefully explaining the > flaky failures from the past. > > -Dan > > On Thu, Jul 5, 2018 at 7:03 PM, Dale Emery <dem...@pivotal.io> wrote: > >> Hi Alexander and all, >> >>> On Jul 5, 2018, at 5:11 PM, Alexander Murmann <amurm...@pivotal.io> >> wrote: >>> >>> Hi everyone! >>> >>> Dan Smith started a discussion about shaking out more flaky DUnit tests. >>> That's a great effort and I am happy it's happening. >>> >>> As a corollary to that conversation I wonder what the criteria should be >>> for a test to not be considered flaky any longer and have the category >>> removed. >>> >>> In general the bar should be fairly high. Even if a test only fails ~1 in >>> 500 runs that's still a problem given how many tests we have. >>> >>> I see two ends of the spectrum: >>> 1. We have a good understanding why the test was flaky and think we fixed >>> it. >>> 2. We have a hard time reproducing the flaky behavior and have no good >>> theory as to why the test might have shown flaky behavior. >>> >>> In the first case I'd suggest to run the test ~100 times to get a little >>> more confidence that we fixed the flaky behavior and then remove the >>> category. >> >> Here’s a test for case 1: >> >> If we really understand why it was flaky, we will be able to: >> - Identify the “faults”—the broken places in the code (whether system >> code or test code). >> - Identify the exact conditions under which those faults led to the >> failures we observed. >> - Explain how those faults, under those conditions. led to those >> failures. >> - Run unit tests that exercise the code under those same conditions, >> and demonstrate that >> the formerly broken code now does the right thing. >> >> If we’re lacking any of these things, I’d say we’re dealing with case 2. >> >>> The second case is a lot more problematic. How often do we want to run a >>> test like that before we decide that it might have been fixed since we >> last >>> saw it happen? Anything else we could/should do to verify the test >> deserves >>> our trust again? >> >> >> I would want a clear, compelling explanation of the failures we observed. >> >> Clear and compelling are subjective, of course. For me, clear and >> compelling would include >> descriptions of: >> - The faults in the code. What, specifically, was broken. >> - The specific conditions under which the code did the wrong thing. >> - How those faults, under those conditions, led to those failures. >> - How the fix either prevents those conditions, or causes the formerly >> broken code to >> now do the right thing. >> >> Even if we don’t have all of these elements, we may have some of them. >> That can help us >> calibrate our confidence. But the elements work together. If we’re lacking >> one, the others >> are shaky, to some extent. >> >> The more elements are missing in our explanation, the more times I’d want >> to run the test >> before trusting it. >> >> Cheers, >> Dale >> >> — >> Dale Emery >> dem...@pivotal.io >> >> — Dale Emery dem...@pivotal.io
