Every software team eventually faces the same frustrating problem: tests that pass one minute and fail the next without any code changes. According to a comprehensive ACM developer survey, 59% of developers deal with flaky tests on a monthly, weekly, or daily basis. Of those who encounter them, 79% rate flaky tests as a moderate or serious problem, not a minor inconvenience.
What makes this worse? Research shows that 75% of flaky tests are flaky from the moment they're added to the codebase, meaning teams inherit problems that could have been prevented. These unreliable tests drain productivity and can cost organizations millions of dollars each year.
This guide breaks down everything you need to know about flaky tests, from understanding why they happen to building systems that detect and eliminate them before they derail your workflow.
What Is the Meaning of Flaky Tests?
The flaky test's meaning is straightforward: a flaky test produces inconsistent results without any changes to the code being tested. Unlike deterministic tests that always pass or always fail given the same conditions, flaky tests behave unpredictably. Understanding this flaky test's meaning is essential because these tests undermine the fundamental purpose of automated testing.
Key characteristics that define flaky tests include:
Non-deterministic behavior: The same test produces different outcomes across multiple runs
No code correlation: Failures occur without any modifications to the test code or production code
Intermittent patterns: Tests may pass ten times consecutively, then fail unexpectedly
Environment sensitivity: Results often depend on factors outside the test itself
Why Do Tests Become Flaky? Common Root Causes
Flaky testing occurs due to several well-documented root causes. Identifying which category your flaky tests fall into is the first step toward fixing them effectively.
Timing and Race Conditions
Asynchronous operations are the biggest culprit behind flaky behavior in modern applications.
Premature assertions: Your test clicks a button and immediately checks for a result, but the backend hasn't finished processing. Sometimes the response arrives in time; sometimes it doesn't
Environment-dependent timing: The test passes on your fast development machine but fails on the overloaded CI server, where processing takes longer
Application-level races: If two threads or processes compete for shared resources, the outcome depends on execution timing. Your test might catch the application in a consistent state most of the time, but occasionally witness a race condition
Network variability: API calls to databases and services respond at varying speeds depending on load, network conditions, and server state
Environmental Dependencies
Tests that depend on specific environmental conditions often behave differently across machines and runs.
File system assumptions: A test relying on files existing in certain locations might pass locally but fail in a fresh CI environment where those files don't exist
Time-based failures: Tests that check timestamps might break around daylight saving time transitions or month boundaries
Resource availability: Network-dependent tests might fail when internet connectivity fluctuates or when CI runners have different memory allocations
Configuration drift: Development machines, staging environments, and CI servers rarely have identical configurations, leading to subtle behavioral differences.
Shared State and Test Isolation
Tests that share state with each other create ordering dependencies that cause intermittent failures.
Database contamination: Test A modifies a database record that Test B expects to find in its original state. When tests run in a certain order, everything works. Change the order, and failures appear.
Global variable pollution: Tests that modify global state without cleanup can affect subsequent tests unpredictably.
Interaction complexity: With thousands of tests, interaction effects become impossible to track mentally. A new test might conflict with an existing test that runs nearby in execution order.
Cached state issues: Application caches that persist between tests can cause one test's data to leak into another test's assertions.
Resource Constraints and Timeouts
CI environments rarely mirror local development machines perfectly, creating resource-related flakiness.
Performance differences: A test with a two-second timeout might pass comfortably on your laptop but fail when the CI server is under heavy load from parallel jobs.
Resource exhaustion: Tests creating many database connections, file handles, or network sockets might exhaust system limits on constrained CI runners.
Cleanup failures: Resources not properly released after tests can accumulate, causing later tests to fail even though they work fine in isolation.
Memory pressure: Tests that allocate significant memory might trigger garbage collection pauses or out-of-memory conditions on memory-limited CI environments.
Flaky Test Examples: What They Look Like in Code
Seeing concrete flaky test examples helps you identify similar patterns in your own codebase. These scenarios appear frequently across different frameworks and languages.
Example 1: The Timing Trap
A common flaky testing scenario involves checking UI state before asynchronous operations complete.
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This test fails intermittently because it checks for the success message immediately after clicking, without waiting for the server response and DOM update. On fast systems, it passes. On slower CI runners, the message hasn't appeared yet when the assertion runs.
Example 2: The Date Boundary Problem
Tests involving date logic often fail at unexpected times.
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This test passes most of the day but fails when run close to midnight. If the subscription was created at 11:59 PM and the assertion runs at 12:01 AM, the day boundary has crossed, and the subscription appears expired.
Example 3: The Shared Database Record
Tests that share database fixtures create ordering dependencies.
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When these tests run in different orders, one will fail. The second test expects the default name but finds "Updated Name" if the first test ran before it.
Example 4: The Port Conflict
Tests that spin up servers on fixed ports conflict when run in parallel.
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When tests run sequentially, this works fine. In parallel execution, multiple tests try to bind port 3000 simultaneously, causing random failures depending on which test grabs the port first.
Example 5: The External API Dependency
Tests calling real external services inherit their reliability characteristics.
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This test fails whenever the weather API is slow, rate-limited, or experiencing downtime. Your test reliability now depends on a third-party service you don't control.
Flaky Test Detection: Finding Problems Before They Spread
Effective flaky test detection requires systematic approaches rather than ad-hoc investigation. Organizations use several proven strategies, each with distinct trade-offs between accuracy and resource consumption.
Repeated Execution Strategies
The most straightforward detection method runs each test multiple times and flags inconsistent results.
Multi-run verification: Running a test five to twenty times reveals intermittent failures that single runs miss. If a test passes nine times but fails once, you've identified a flaky candidate
Pre-merge detection: Some teams run detection on new tests before they merge into the main branch, catching flakiness before it affects everyone
Periodic sweeps: Running detection across the entire test suite during off-hours identifies tests that have become flaky over time due to codebase changes
Balancing thoroughness and time: Too few repetitions miss intermittent failures; too many make detection impractically slow. Most teams find that five to twenty repetitions provide reasonable confidence
Historical Analysis
Tracking test results over time reveals patterns that single runs miss.
Long-term pattern recognition: A test failing once every fifty runs might not trigger alerts from repeated execution, but analyzing weeks of CI history shows the pattern clearly.
Failure rate dashboards: Build visualizations showing failure rates per test over time. Tests with fluctuating rates, not steady near zero or one hundred percent, deserve investigation.
Impact quantification: Historical data helps prioritize fixes. A rarely-flaky test that blocks deployments might cause more total delay than a frequently-flaky test everyone knows to retry.
Regression detection: Tracking when tests became flaky helps identify which code changes introduced the problem.
Correlation Detection
Advanced flaky test detection looks for correlations between failures and environmental factors.
Dependency tracking: Did the test start failing when you upgraded a library? Correlating failure onset with dependency change points toward version-related issues.
Infrastructure patterns: Does the test fail more often on specific CI worker nodes? This suggests hardware-specific issues or resource contention on particular machines.
Temporal correlations: Failures clustering at certain times, during business hours, at the end of the month, or around midnight suggest time-dependent or load-dependent issues.
Parallel execution effects: Tests that fail only during parallel runs likely have isolation problems or resource conflicts with other tests.
Flaky Test Management: Strategies for Scale
Once identified, flaky test management becomes critical for maintaining healthy CI/CD pipelines. Without systematic management, flaky tests accumulate and gradually erode trust in your entire test suite.
Quarantine and Track
Removing flaky tests from your critical path prevents them from blocking development while preserving them for future fixing.
Immediate isolation: The moment you identify a flaky test, move it into a quarantine system. This removes it from blocking your main pipeline while preserving the test logic.
Metadata tracking: Good quarantine systems track why each test was quarantined, when it happened, and who owns fixing it. Without this, quarantined tests become forgotten tests.
Time limits: Set deadlines for quarantine. A test sitting unfixed for weeks should trigger escalation. Either commit to fixing it or remove it permanently with documentation about lost coverage.
Visibility maintenance: Keep quarantined tests visible in dashboards and reports. Hidden problems don't get fixed.
Ownership and Accountability
Clear ownership ensures flaky tests receive appropriate attention rather than becoming everyone's problem and therefore no one's priority.
Assigned responsibility: Every test should have an owner, either an individual developer or a team. When a test becomes flaky, the owner gets notified and takes responsibility for resolution.
Flexible resolution: Ownership doesn't mean personally fixing every flaky test. Owners ensure appropriate attention, fixing it themselves, delegating to relevant experts, or making a case for deprioritization.
Organizational incentives: Some teams tie flaky test metrics to team health dashboards. Teams consistently producing or failing to fix flaky tests see their reliability scores drop.
Escalation paths: Define what happens when owners don't address flaky tests within expected timeframes. Clear escalation prevents tests from languishing indefinitely.
Prioritization Frameworks
Not all flaky tests deserve equal urgency, and limited engineering time requires smart allocation.
Impact assessment: A flaky test blocking production deployments demands immediate attention. A flaky test in a peripheral feature running nightly can wait.
Multi-factor scoring: Build prioritization criteria considering impact on development velocity, flakiness frequency, cost of potential missed bugs, and estimated fix difficulty.
Avoid easy-fix bias: Don't always fix the easiest tests first. Easy fixes feel productive, but might not reduce overall pain if hard-to-fix tests cause the most disruption.
Regular reprioritization: As your codebase and team change, flaky test priorities shift. Review and adjust priorities periodically rather than following a static list.
Building Reliability Culture
Technical solutions alone cannot solve flaky testing challenges. Organizations must build engineering cultures that prioritize test reliability alongside feature development.
Dedicated maintenance time: Allocate sprint capacity specifically for test reliability improvements
Developer education: Training on async handling, test isolation, and mock management prevents flakiness at the source
Code review focus: Reviewers should flag unbounded waits, shared state access, and implicit order dependencies
Trend monitoring: Dashboards tracking flakiness over time help identify problem areas before they become critical
Prevention: How to Write Tests That Stay Reliable
Preventing flaky tests from entering your codebase yields higher returns than detection and remediation. These best practices address the most common causes of test instability.
Test Isolation Techniques
Proper isolation ensures each test operates independently, eliminating order dependencies and shared state problems.
Independent state creation: Each test should create its required data, execute, and clean up afterwards
Unique identifiers: Use generated IDs for test data to prevent collisions between parallel test runs
Mock reset verification: Ensure all mocks are properly reset between tests to prevent state carryover
Database transactions: Wrap tests in transactions that roll back, ensuring a clean state for subsequent tests
Async and Timing Best Practices
Proper handling of asynchronous operations eliminates a major category of flaky testing failures.
Explicit awaits: Use proper await patterns instead of arbitrary sleep statements
Polling with timeouts: Wait for specific conditions with sensible maximum wait times
Event-based synchronization: Trigger assertions based on events rather than elapsed time
Deterministic random seeds: Log or fix random seeds so failures from specific values can be reproduced
Mock time sources: Abstract system clocks behind interfaces to test time-sensitive logic reliably
Infrastructure Recommendations
Standardizing execution environments minimizes environmental factors that cause inconsistent results.
Containerization: Run tests in containers with consistent resources and dependencies
Resource specifications: Document minimum requirements and configure CI to provision adequate capacity
Strategic retries: Implement retry logic for known transient failures while logging occurrences for investigation
External dependency mocking: Mock network calls and third-party APIs in unit tests; use circuit breakers in integration tests
The Real Cost of Ignoring Flaky Tests
Neglecting test reliability creates compounding problems that extend far beyond immediate frustration. Understanding these costs helps justify investment in proper flaky test management.
Trust erosion: Developers begin ignoring test results entirely, dismissing real bugs as "probably just flaky"
Productivity drain: Atlassian reports 15% of Jira backend failures from flaky tests, wasting 150,000+ developer hours annually
Masked defects: Real bugs slip through when teams assume failures are flakiness-related
Deployment delays: GitLab found 36% of developers experience delayed releases due to test failures at least monthly
Research from Mozilla found that after systematically addressing flaky tests, developer confidence in their test suite increased by 29%, leading to faster issue resolution and fewer escaped bugs reaching production.
Taking Action on Test Reliability
Flaky tests affect every software organization regardless of size or technical sophistication. The difference between teams that struggle and those that succeed lies in systematic approaches to detection, management, and prevention.
Start by measuring your current flakiness rates across repositories. Implement detection tooling that provides visibility into which tests are unreliable. Establish clear ownership and resolution timelines for identified flaky tests. Train your team on prevention best practices to reduce new flaky tests entering the codebase.
For teams looking to accelerate this process, AI-powered testing platforms offer a faster path forward. Supatest.ai provides drop-in replacements for traditional frameworks like Playwright and Cypress, with built-in flaky test detection and self-healing capabilities that keep your CI pipelines stable without constant manual intervention.
The investment in test reliability pays dividends across your entire delivery lifecycle. Teams with reliable test suites deploy with confidence, catch bugs earlier, and spend their time building features rather than investigating phantom failures. A test suite that cannot be trusted provides no value, but one that your team believes in becomes a genuine competitive advantage.
FAQs
What is the flake test?
A flake test is an automated test that gives inconsistent results even when the code hasn't changed. It passes sometimes and fails other times, making it unreliable for determining whether your code actually works. The term comes from the unpredictable, "flaky" behavior these tests exhibit, you never know what result you'll get when you run them.
What is a flakey or flaky test?
"Flakey" and "flaky" are two spellings for the same thing, a test with non-deterministic outcomes. "Flaky" is the more common spelling in engineering circles. These tests behave like an unreliable signal: sometimes they correctly indicate problems, and sometimes they raise false alarms. The key characteristic is that running the exact same test against unchanged code produces different pass/fail results at different times.
What causes flaky tests?
Flaky tests are usually caused by timing issues, shared state, or environmental differences. Timing problems occur when tests check results before asynchronous operations finish. Shared state happens when one test affects data that another test depends on. Environmental differences arise when tests behave differently on local machines versus CI servers due to variations in memory, processing speed, or network conditions. External dependencies like third-party APIs also introduce flakiness since those services may respond inconsistently.
How to resolve flaky tests?
Start by reproducing the flaky behavior through repeated test runs to identify patterns. Replace hardcoded wait times with condition-based waits that check for specific states before proceeding. Isolate each test's data so tests don't affect each other. Mock external services to eliminate dependency on unpredictable third-party responses. Use containers to create consistent test environments across all machines. While working on fixes, quarantine flaky tests to prevent them from blocking your pipeline.
How do flaky tests affect CI/CD pipelines?
Flaky tests disrupt CI/CD pipelines by causing random failures that block code merges and deployments, even when nothing is broken. Teams waste time rerunning pipelines, waiting for lucky passes, and investigating false alarms. This erodes trust in automation, developers start ignoring failures or clicking retry without investigating. The unpredictability makes it impossible to know if a green build means working code or just a run where flaky tests happened to pass.
What is the difference between a flaky test and a failing test?
A failing test fails consistently every time you run it, pointing to a definite problem in your code or test logic. A flaky test fails inconsistently, it might pass three times, then fail once, with no code changes in between. Failing tests are easier to debug because you can reliably reproduce the issue. Flaky tests are harder because the failure conditions are unpredictable. Failing tests demand code fixes; flaky tests require investigation into timing, environment, or test design issues.
Should you delete or fix flaky tests?
It depends on what the test covers. If the flaky test protects critical functionality like payments or authentication, invest time in fixing it, the risk of missing bugs in that area outweighs the debugging effort. If the test covers minor features or duplicates other coverage, deletion might be the practical choice. Always quarantine flaky tests immediately to unblock your pipeline while you decide. Document any deleted tests, so you know what coverage was lost. The worst option is leaving flaky tests active while training your team to ignore their failures.
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