What is Flaky Tests?
Flaky Tests are automated tests that pass or fail inconsistently without any code changes, making them unreliable indicators of software quality.
How Does Flaky Tests Work (or Fail)?
They typically fail due to external factors like timing issues, resource contention, asynchronous operations, or environmental inconsistencies, not actual code defects.
What Are the Benefits of Addressing Flaky Tests?
- Higher confidence in CI/CD test results.
- Streamlined release processes.
- Improved developer productivity by reducing reruns.
- Better software quality assurance.
How Can Flaky Tests Reduce Mean Time to Resolution?
By eliminating flakiness, teams can trust test failures to represent real issues, which speeds up debugging and fixing without wasting time diagnosing non-existent problems.
What are the Challenges of Flaky Tests?
- Difficult to reproduce consistently.
- Time-consuming to debug and stabilize.
- May mask real bugs if ignored.
Leading Tools – of Flaky Tests
These tools are designed to detect, manage, or mitigate flaky tests — automated tests that yield inconsistent results without code changes:
- Test Retry Libraries (e.g., Jest Retry, Cypress Retry) – Automatically re-run failed tests to reduce false negatives and flag unstable tests.
- Buildkite Analytics – Surfaces flaky test trends and failure patterns across CI pipelines to help teams identify root causes.
- Google’s FlakyBot – Used in large-scale test systems to detect flaky tests by statistically analyzing test outcomes across runs.
Other Great Observability Tools for Flaky Test Prevention
These tools help uncover deeper instability or runtime risks that often manifest as flaky tests:
- LOCI – Analyzes compiled binaries during CI to detect behavioral anomalies and software instability before tests run, helping teams reduce the occurrence of flakiness at the source.
- Datadog
- Honeycomb
