What Makes a Test “Flaky”?
A flaky test is one that produces different results when run multiple times against the same code. Sometimes it passes, sometimes it fails, with no changes to the underlying application. These inconsistencies can stem from various sources:
- Race conditions and timing issues
- Async operations without proper waiting
- Dependencies on external services
- Order dependency between tests
- Resource limitations or conflicts
While they might seem like a minor annoyance, flaky tests have far-reaching consequences that extend beyond just technical frustration.
The Hidden Costs of Test Flakiness
1. Eroded Developer Trust
When tests frequently produce false failures, developers begin to distrust the entire test suite. This leads to:
- Ignoring legitimate test failures (“It’s probably just flaky”)
- Reduced confidence in making changes
- Skipping test runs to save time
Once trust is broken, the entire value proposition of automated testing collapses.
2. Wasted Engineering Time
Flaky tests waste time through:
- Investigating false failures
- Rerunning builds multiple times
- Debugging intermittent issues
- Waiting for unreliable CI pipelines
For a team of 10 developers, even 30 minutes per day spent on flaky tests represents a significant cost—over 100 engineering hours per month!
3. Delayed Releases
When critical pipelines fail unpredictably:
- Production deployments get delayed
- Feature releases miss deadlines
- Emergency fixes take longer to deploy
These delays directly impact your ability to deliver value to customers quickly.
4. Decreased Morale
Never underestimate the psychological impact of fighting the same unreliable tests repeatedly. Engineers want to build features, not babysit flaky tests. This leads to:
- Frustration and decreased job satisfaction
- Resistance to writing new tests
- Workarounds that weaken testing practices
Strategies for Eliminating Test Flakiness
1. Isolate and Quarantine
First, identify which tests are consistently unreliable:
- Track flaky tests in a dedicated dashboard
- Temporarily quarantine the worst offenders
- Set up auto-retry for suspicious tests
2. Root Cause Analysis
For each flaky test, investigate the underlying cause:
- Add detailed logging around failures
- Use screenshots or video captures for UI tests
- Look for timing assumptions and race conditions
3. Implement Robust Waiting Mechanisms
Many flaky tests result from timing issues:
- Wait for specific conditions, not fixed time periods
- Implement proper retry logic with exponential backoff
- Detect when the application is truly ready for interaction
4. Control Test Environments
Improve stability by controlling variables:
- Use deterministic data instead of random values
- Mock external dependencies when appropriate
- Ensure clean state between test runs
Conclusion: Quality Over Quantity
Having 1,000 flaky tests is worse than having 100 reliable ones. Focus on creating a smaller set of dependable tests that provide consistent signal about your application’s health.
Remember that each flaky test carries an ongoing maintenance cost. Sometimes the best solution is to delete a test that provides more noise than signal, and replace it with a more reliable alternative.
By taking flakiness seriously and investing in test reliability, you can reclaim wasted engineering time, rebuild trust in your test suite, and ultimately ship faster with greater confidence.
