Introduction: The Nightmare of Flaky Tests
Imagine this: Your team has a solid CI/CD pipeline in place, with automated tests running smoothly—until they don’t. A test that passed yesterday is now failing, and nobody changed the code. After multiple reruns, the test magically passes again. Welcome to the frustrating world of flaky tests.
Flaky tests are tests that produce inconsistent results, sometimes passing and sometimes failing without any meaningful code changes. They erode trust in test suites, waste engineering time, and slow down development. In this post, we’ll explore why flaky tests occur, how to systematically identify them, and—most importantly—how to fix them.
Why Are Flaky Tests a Big Problem?
Flaky tests introduce multiple challenges, including:
- False Failures: Engineers waste time investigating test failures that aren’t actually caused by defects in the application.
- Blocked CI/CD Pipelines: Flaky tests can cause unnecessary build failures, slowing down deployments.
- Erosion of Trust: When developers stop trusting automated tests, they start ignoring failures, leading to real bugs slipping through.
- Increased Cost: Debugging and rerunning tests repeatedly consumes valuable development hours and computational resources.
A single flaky test may not seem like a major issue, but when they multiply across a test suite, they become a bottleneck for productivity and software quality.
Common Causes of Flaky Tests
Flakiness in tests can stem from various sources. Some of the most common culprits include:
1. Timing and Asynchronous Issues
Tests that rely on UI updates, network requests, or background processes may complete before the expected data is available.
Example: Waiting for an element to appear without using a proper wait strategy.
2. Race Conditions
When multiple processes or threads execute in an unpredictable order, tests may pass or fail depending on execution timing.
Example: A test checking database updates before they are fully committed.
3. Test Data Dependency
Shared test data can cause failures if one test modifies the data before another test runs.
Example: A test that expects a database record to exist but another test deletes it.
4. Environmental Differences
Tests running in different environments (e.g., local vs. CI/CD) may behave differently due to varying configurations.
Example: A test that passes locally but fails in CI because of different timezone settings.
5. Poorly Designed UI Tests
End-to-end (E2E) tests that interact with dynamic UIs without proper synchronization often lead to flakiness.
Example: Clicking a button before it is fully rendered.
Tips to Identify Flaky Tests
1. Use Test Analytics & Flake Detection Tools
Many CI/CD tools (e.g., GitHub Actions, CircleCI, Jenkins) provide test result histories to track inconsistencies.
Specialized tools like Flaky Test Reporter (Jest) or Test Retry Plugins (Cypress, Playwright) help detect unstable tests.
2. Run Tests Multiple Times
Flaky tests often fail intermittently. Running tests in parallel or multiple times in a loop (e.g., jest --retries=5) can reveal inconsistencies.
If a test fails randomly, it’s likely flaky.
3. Categorize and Track Flaky Tests
- Maintain a list of suspected flaky tests and their failure patterns.
- Use tags like
@flakyto track them in test reports. - Log test failures with timestamps, screenshots, and system states for debugging.
Example: In Cypress, you can tag tests dynamically and track flakiness:
it('should load data correctly', { tags: ['flaky'] }, () => {
cy.visit('/dashboard');
});
4. Reproduce Failures in a Controlled Environment
If a test fails inconsistently, try to isolate and reproduce the failure:
• Lock test dependencies (same browser, OS, database state).
• Slow down execution using debugging tools to observe timing issues.
• Use deterministic test data (avoid dynamic API responses, timestamps).
5. Introduce Artificial Load to Test Stability
Some flaky tests only fail under high CPU or memory load.
• Simulate real-world stress scenarios (e.g., network delays, memory spikes).
• Run tests on different devices and OS configurations.
• Monitor how system load affects test execution time.
Example: In Jenkins, use “Load Simulators” to increase concurrent processes while tests run to expose timing-related issues.
Tips to Fix Flaky Tests
Once flaky tests are identified, the next step is fixing them to prevent unreliable test results from impacting development. Here are proven strategies to eliminate flakiness and build a robust, stable test suite.
1. Use Explicit Waits Instead of Hardcoded Timeouts
Many UI and end-to-end (E2E) tests fail due to timing issues, where elements may not be fully rendered before the test interacts with them.
• Avoid hardcoded delays which introduce inconsistency.
• Use explicit waits that check for the expected condition before proceeding.
Example (Cypress):
cy.get('.button', { timeout: 10000 }).should('be.visible').click();
Example (Playwright):
await page.waitForSelector('.button', { timeout: 5000 });
2. Make Tests Independent from Each Other
Flakiness often occurs when tests share state, causing dependencies between them. To fix this:
• Ensure tests run in isolation (e.g., reset databases, use unique test data).
• Avoid relying on previous test runs to set up required conditions.
• Use test fixtures to ensure a consistent state.
3. Use Mocking & Stubbing for External Dependencies
Tests that rely on external APIs, third-party services, or databases are prone to flakiness due to network delays, outages, or unpredictable responses.
• Mock API responses instead of making real network calls.
• Stub database queries to return consistent results.
4. Ensure a Stable Test Environment
Flaky tests often fail due to inconsistent environments across different test runs.
• Use Docker or virtualized environments to standardize testing conditions.
• Ensure test runners use the same browser, OS, and screen resolution in CI/CD.
• Lock dependencies using a package lock file (package-lock.json or yarn.lock).