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How much flakiness do you tolerate in end to end tests?
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this post was submitted on 30 Aug 2023
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I think/hope that the wording you used was a mistake.
Whenever we discover flakiness, we try to fix it immediately. When there is no time for the fix (which is more than often the case) we create a ticket that vanishes in the backlog.
For a long time the company I currently work at didn't have end to end tests save unit tests for a lot of their code.
Through a push of newcomers we finally managed to add end to end tests to many more parts of the code. However, these are still not properly documented. Some end to end tests overlap and some only cover a small part of one larger functionality. That is why we often find bugs that were introduced by us, because we had no end to end tests covering those parts.
We used to run end end tests only every night on the whole product. They usually take an hour or more to complete. This takes too long to run them before each merge. However, we have them organized enough such that for sub-product A we can run the sub-product A end to end tests only before each merge where we assume that we did only touch code affecting sub-product A. In case the code changes affected some other parts of the product, the nightly tests help us out. We are doing this in my team for a long while now. But we just recently started to establish this procedure in the other teams of the company, too.
My experience with E2E testing is that the tools and methods necessary to test a complex app are flaky. Waits, checks for text or selectors and custom form field navigation all need careful balancing to make the test effective. On top of this, there is frequently a sequentiality to E2E tests that causes these failures to multiply in frequency, as you're at the mercy of not just the worst test, but the product of every test in sequence.
I agree that the tests cause less flakiness in the app itself, but I have found smokes inherently flaky in a way that unit and integration tests are not.
Okay I must admit that I do not have much experience with smoke and integration tests. We run end to end tests only and skip running the other two types entirely. They would be covered by the end to end tests anyways.
Perhaps I am lucky in that our software doesn't require us to use many waits at all. Most things are synchronous and those that are not mostly have API endpoints where the status of the process an be safely queried, i.e. a
wait(1000)and hope for the best is not necessary, but ratherdo wait(1000) until isFinished().And yes, for us it is also a mess of errors popping up when one step in a pipeline fails, where many tests rely on this single step. I don't know whether there is a way to approach this issue neatly. This is surely a chance in the market to be taken.
I'm a fan of randomizing the test order. That helps catch ordering issues early.
Also, it's usually valuable to have E2E tests all be as completely independent as possible so it's impossible for one to affect another. Have each one spin up the whole system, even though it takes longer. Use more parallelism, use dozens of VMs each running a fraction of the tests rather than trying to get the sequential time down.
The problem with randomising the test order is that it compromises the reproducibility of results. If there are ordering issues, then your tests will sometimes fail and sometimes pass, but will developers look at that and think "ah there must be an ordering issue" or will they think "damn these flaky tests, guess I'd better rerun the pipeline"?
Wherever possible, this is a good idea. The campsite rule - tests don't touch data they didn't bring with them - helps as well.
However, many end to end tests exist as a pipeline, especially for entities that are core to the business function of the app. Cramming all sequentiality into single tests will give you all the problems described, but in a giant single test that you need to fish out the result for.
End-to-end tests are basically non-deterministic state machines. Flakiness can come from any point in the test: bad tests, bad state management, conflicting tests, network hiccups, etc.
Your goal is to reduce every single point of that flakiness. Just make sure you keep track of it. Sometimes flakiness in tests is really pointing at flakiness in the product itself.
Some things that can help reduce that flakiness:
Polling is certainly useful, but at some point introducing reliability degrades effectiveness. I certainly want to know if the app is unreachable over the open internet, and I absolutely need to know if a partner's API is down.