Flaky tests

What’s a flaky test?

It’s a test that sometimes fails, but if you retry it enough times, it passes, eventually.

What are the potential cause for a test to be flaky?

Unclean environment

Label: flaky-test::unclean environment

Description: The environment got dirtied by a previous test. The actual cause is probably not the flaky test here.

Difficulty to reproduce: Moderate. Usually, running the same spec files until the one that’s failing reproduces the problem.

Resolution: Fix the previous tests and/or places where the environment is modified, so that it’s reset to a pristine test after each test.


  • Example 1: A migration test might roll-back the database, perform its testing, and then roll-up the database in an inconsistent state, so that following tests might not know about certain columns.
  • Example 2: A test modifies data that is used by a following test.
  • Example 3: A test for a database query passes in a fresh database, but in a CI/CD pipeline where the database is used to process previous test sequences, the test fails. This likely means that the query itself needs to be updated to work in a non-clean database.

Ordering assertion

Label: flaky-test::ordering assertion

Description: The test is expecting a specific order in the data under test yet the data is in a non-deterministic order.

Difficulty to reproduce: Easy. Usually, running the test locally several times would reproduce the problem.

Resolution: Depending on the problem, you might want to:

  • loosen the assertion if the test shouldn’t care about ordering but only on the elements
  • fix the test by specifying a deterministic ordering
  • fix the app code by specifying a deterministic ordering


  • Example 1: Without specifying ORDER BY, database will not give deterministic ordering, or data race happening in the tests.
  • Example 2.


Label: flaky-test::dataset-specific

Description: The test assumes the dataset is in a particular (usually limited) state, which might not be true depending on when the test run during the test suite.

Difficulty to reproduce: Moderate, as the amount of data needed to reproduce the issue might be difficult to achieve locally.

Resolution: Fix the test to not assume that the dataset is in a particular state, don’t hardcode IDs.


  • Example 1: The database is recreated when any table has more than 500 columns. It could pass in the merge request, but fail later in master if the order of tests changes.
  • Example 2: A test asserts that trying to find a record with an nonexistent ID returns an error message. The test uses an hardcoded ID that’s supposed to not exist (e.g. 42). If the test is run early in the test suite, it might pass as not enough records were created before it, but as soon as it would run later in the suite, there could be a record that actually has the ID 42, hence the test would start to fail.

Random input

Label: flaky-test::random input

Description: The test use random values, that sometimes match the expectations, and sometimes not.

Difficulty to reproduce: Easy, as the test can be modified locally to use the “random value” used at the time the test failed

Resolution: Once the problem is reproduced, it should be easy to debug and fix either the test or the app.


  • Example 1: The test isn’t robust enough to handle a specific data, that only appears sporadically since the data input is random.

Unreliable DOM Selector

Label: flaky-test::unreliable dom selector

Description: The DOM selector used in the test is unreliable.

Difficulty to reproduce: Moderate to difficult. Depending on whether the DOM selector is duplicated, or appears after a delay etc. Adding a delay in API or controller could help reproducing the issue.

Resolution: It really depends on the problem here. It could be to wait for requests to finish, to scroll down the page etc.


  • Example 1: A non-unique CSS selector matching more than one element, or a non-waiting selector method that does not allow rendering time before throwing an element not found error.
  • Example 2: A CSS selector only appears after a GraphQL requests has finished, and the UI has updated.


Label: flaky-test::datetime-sensitive

Description: The test is assuming a specific date or time.

Difficulty to reproduce: Easy to moderate, depending on whether the test consistently fails after a certain date, or only fails at a given time or date.

Resolution: Freezing the time is usually a good solution.


  • Example 1: A test that breaks after some time passed.

Unstable infrastructure

Label: flaky-test::unstable infrastructure

Description: The test fails from time to time due to infrastructure issues.

Difficulty to reproduce: Hard. It’s really hard to reproduce CI infrastructure issues. It might be possible by using containers locally.

Resolution: Starting a conversation with the Infrastructure department in a dedicated issue is usually a good idea.


  • Example 1: The runner is under heavy load at this time.
  • Example 2: The runner is having networking issues, making a job failing early

Quarantined tests

When a test frequently fails in master, create a ~"failure::flaky-test" issue.

If the test cannot be fixed in a timely fashion, there is an impact on the productivity of all the developers, so it should be quarantined by assigning the :quarantine metadata with the issue URL, and add the ~"quarantined test" label to the issue.

it 'succeeds', quarantine: 'https://gitlab.com/gitlab-org/gitlab/-/issues/12345' do
  expect(response).to have_gitlab_http_status(:ok)

This means it is skipped unless run with --tag quarantine:

bin/rspec --tag quarantine

Once a test is in quarantine, there are 3 choices:

  • Fix the test (that is, get rid of its flakiness).
  • Move the test to a lower level of testing.
  • Remove the test entirely (for example, because there’s already a lower-level test, or it’s duplicating another same-level test, or it’s testing too much etc.).

Automatic retries and flaky tests detection

On our CI, we use RSpec::Retry to automatically retry a failing example a few times (see spec/spec_helper.rb for the precise retries count).

We also use a custom RspecFlaky::Listener. This listener runs in the update-tests-metadata job in maintenance scheduled pipelines on the master branch, and saves flaky examples to rspec/flaky/report-suite.json. The report file is then retrieved by the retrieve-tests-metadata job in all pipelines.

This was originally implemented in: https://gitlab.com/gitlab-org/gitlab-foss/-/merge_requests/13021.

If you want to enable retries locally, you can use the RETRIES environment variable. For instance RETRIES=1 bin/rspec ... would retry the failing examples once.

To generate the reports locally, use the FLAKY_RSPEC_GENERATE_REPORT environment variable. For example, FLAKY_RSPEC_GENERATE_REPORT=1 bin/rspec ....

Usage of the rspec/flaky/report-suite.json report

The rspec/flaky/report-suite.json report is:

Problems we had in the past at GitLab

Order-dependent flaky tests

These flaky tests can fail depending on the order they run with other tests. For example:

To identify the tests that lead to such failure, we can use scripts/rspec_bisect_flaky, which would give us the minimal test combination to reproduce the failure:

  1. First obtain the list of specs that ran before the flaky test. You can search for the list under Knapsack node specs: in the CI job output log.
  2. Save the list of specs as a file, and run:

     cat knapsack_specs.txt | xargs scripts/rspec_bisect_flaky

If there is an order-dependency issue, the script above will print the minimal reproduction.

Time-sensitive flaky tests

Array order expectation

Feature tests

Capybara expectation times out

Hanging specs

If a spec hangs, it might be caused by a bug in Rails:


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