Flaky Test Detection

Flaky test detection helps you identify tests that produce inconsistent results on the same code, allowing you to improve the reliability of your test suite and reduce false positives in your CI pipeline.

A flaky test is one that fails on some runs of a commit (SHA1) and passes on others. If a test runs twice on the same commit and once fails while the other succeeds, it’s considered flaky because the outcome is not consistent with the same code. A test whose results differ without ever failing, such as one that is skipped in one run and passes in another, stays healthy.

Below is a simplified visualization of flakiness: two executions of the same tests on the exact same commit (same SHA1) produce different results. This inconsistency is what flags the test as flaky.

Flaky tests are problematic because they:

• Create false positives that block legitimate deployments
• Reduce confidence in your test suite
• Waste developer time investigating non-issues
• Can mask real bugs when they fail intermittently

Mergify compares each uploaded test result against the results it already holds for the same test, on the same commit, in the same pipeline and job. When two results disagree, Mergify records the disagreement and reports the test as flaky once one of the two is a failure. Any repeat execution counts: a retried job, a re-run workflow, or a job that runs the suite several times in a row.

Detection does not depend on the scheduled runs that the setup guides below describe. Running your suite several times on the same commit produces the evidence deliberately, on a schedule you choose, instead of waiting for a rerun to expose the problem. Keeping the job name identical to the one used on your pull requests matters for the same reason: results are only compared within a single pipeline and job.

See CI-specific setup guides:

Once your flaky test detection is running, Mergify analyzes every uploaded report and flags the tests whose results disagree on a single commit.

Test-level results live in Test Insights, not in CI Insights: its Detection page lists the flaky and broken tests found across your repositories and reports each one’s impact: the share of its executions that failed, as low, medium, or high.

Flakiness at the job level stays in CI Insights, on the Jobs page.

• High Flakiness Rate: Tests that fail inconsistently across runs on the same commit
• Recent Flakiness: Newly introduced flaky behavior that may indicate recent code changes
• Critical Path Tests: Flaky tests in important workflows that could block deployments
• Patterns: Flakiness that occurs under specific conditions (time of day, load, etc.)

When flaky tests are identified:

1. Prioritize by Impact: Focus on tests that affect critical workflows first

2. Investigate Root Causes: Look for timing issues, external dependencies, or race conditions

3. Improve Test Reliability: Add proper waits, mocks, or test isolation

4. Monitor Progress: Use CI Insights to verify that fixes reduce flakiness over time

Understanding common causes can help you fix flaky tests more effectively:

• Race conditions: Tests that depend on timing between operations

• Insufficient waits: Tests that don’t wait long enough for operations to complete

• Timeouts: Tests with hardcoded timeouts that may vary in different environments

• Network calls: Tests that make real HTTP requests
• Database state: Tests that depend on specific database state
• File system: Tests that read/write files without proper cleanup

• Shared state: Tests that affect each other’s state
• Order dependencies: Tests that only pass when run in a specific order
• Resource conflicts: Tests competing for the same resources

• System load: Tests sensitive to CPU or memory usage
• Date/time dependencies: Tests that depend on current time
• Random data: Tests using non-deterministic random values

To prevent flaky tests:

1. Use deterministic data: Replace random values with fixed test data
2. Mock external dependencies: Isolate tests from network, database, and file system
3. Implement proper waits: Use explicit waits instead of fixed sleeps
4. Clean up after tests: Ensure each test starts with a clean state
5. Make tests independent: Each test should be able to run in isolation
6. Use stable selectors: In UI tests, use reliable element selectors
7. Handle async operations: Properly wait for asynchronous operations to complete