Flaky tests refer to tests that intermittently pass or fail, even when there is no change in the codebase or the testing environment. These unpredictable behaviors can be frustrating for developers and QA teams, as they undermine the reliability of automated testing and make it difficult to trust the results. In fact, a Google study of its internal testing systems found that 16% of test failures were determined to be flaky rather than actual bugs.
In this guide, we'll dive into the causes of flaky tests, how they can impact your software development process, and most importantly, how to prevent and fix them effectively.
Overview
What Are Flaky Tests?
A flaky test is an automated test that produces inconsistent results, passing on one run and failing on the next, without any change in the underlying code. This unpredictability makes it hard to distinguish genuine bugs from test instability, eroding team confidence in the entire test suite.
What Makes Tests Go Flaky?
The most common culprits are timing and synchronization issues, environmental factors like network instability or resource fluctuations, poorly isolated tests sharing state or data, and non-deterministic application behavior such as async operations or concurrency.
How to Detect and Reduce Flakiness?
Detection relies on running tests multiple times and analyzing result variations, using CI tools with built-in flaky test detection, and monitoring failure rates over time. To reduce flakiness, teams should isolate tests, stabilize test environments, use consistent data generation, and apply retry mechanisms for transient failures.
What are Flaky Tests?
A flaky test, in software testing, is an automated test that demonstrates inconsistent behavior by producing varying outcomes when executed multiple times on the same functionality. These tests are characterized by their unpredictability, as they may pass or fail intermittently without any changes in the code or the application being tested.
Flaky tests can be a challenge for software developers and testers as they make it difficult to determine whether a failure is due to a genuine bug or a result of the test's instability. The unreliability of flaky tests can hinder the testing process, leading to wasted time, effort, and reduced confidence in the accuracy of the testing outcomes.
The fastest way to recognize flaky tests
Flaky tests often share observable "smells":
| Symptom you see in CI | What it often indicates | What to do next |
|---|
| Passes on rerun without code changes | Non-determinism (timing/state/environment) | Start with the triage workflow below |
| Timeouts / "element not ready" | Async wait mistake, unstable UI state | Replace sleeps with condition-based waits |
| Fails only in parallel runs | Shared state, ordering dependency, thread/process interaction | Isolate state + randomize order to surface hidden coupling |
| Hangs / "Jest did not exit" | Leaked async handles (sockets, DB, timers) | Use leak debugging flags; fix teardown |
| UI clicks intercepted / flaky UI actions | animations/overlays/unstable DOM | Use actionability/auto-wait features or explicit waits |
What are the causes of Flaky Tests?
Flaky tests can arise due to a variety of factors. Understanding the root causes is the first step toward solving them. Here are some of the most common reasons:
1. Timing Issues and Race Conditions
One of the most common causes of flaky tests is timing problems, such as race conditions. A race condition happens when the outcome of a process depends on the sequence or timing of other uncontrollable events.
This occurs when multiple tests or threads access shared resources, and the test behavior depends on the timing of those accesses. For example, if two tests are trying to read and write to the same file at the same time, the result may vary depending on which test finishes first. This can lead to inconsistent results, causing tests to fail intermittently.
2. Environmental Dependencies
Tests that rely on external resources, such as databases, APIs, or file systems, are prone to flakiness. Network failures, changes in external services, or inconsistent environments can cause tests to pass one time and fail the next.
3. Concurrency and Parallelism
When tests are run concurrently or in parallel, it can lead to flaky behavior. If tests are not properly isolated, they may interfere with each other and produce inconsistent results.
4. Unreliable Test Data
If your tests rely on specific test data, any inconsistency in the data (such as missing or incorrect values) can lead to flaky behavior. It's essential to ensure the test reliability, which includes checking if your test data is accurate and consistent across test runs.
5. External Factors and Dependencies
Flaky tests can also occur when tests depend on external factors, such as server load or user interactions, that can change from one test run to the next. These factors make it difficult to reproduce failures and are often beyond the control of developers.
Now that we've covered the causes, let's explore how to prevent and fix flaky tests to ensure your automated tests provide reliable and consistent results.
How to detect Flaky Tests?
Detecting flaky tests is a critical aspect of ensuring the reliability and effectiveness of automated testing. Flaky tests, which produce inconsistent results across multiple test runs, can be challenging to identify and address. However, by employing effective detection techniques, software development teams can gain insights into the stability of their tests and take appropriate measures to improve the overall test execution process.
- Statistical Analysis: Analyzing historical test results, identifying failure patterns, and calculating the probability of flakiness can detect flaky tests. Statistical methods help identify patterns and anomalies in test data, providing insights for further investigation and resolution.
- Test Reruns and Variability: Running tests multiple times and analyzing result variations can indicate flakiness. Inconsistent outcomes across runs suggest potential flakiness. Observing variability helps pinpoint unreliable tests and take appropriate action.
- Custom Test Annotations: Using annotations or markers to flag and track flaky behavior aids detection. Custom annotations specifically designed for flaky tests allow easy tracking and monitoring over time, revealing trends and patterns.
- Continuous Integration Tools: Leveraging Continuous Integration tools with built-in flaky test detection simplifies the process. Automatic analysis identifies tests with inconsistent behavior. Integration with version control and reporting enables efficient identification and resolution of flaky tests.
- Active Monitoring and Reporting: Continuous monitoring and comprehensive reports help identify and track flaky tests. Monitoring test runs and recording metrics like failure rates detects flakiness proactively. Detailed reports offer insights into test suite stability and reliability.
How to Manage Flaky Tests?
Most teams manage flaky tests like a leaky roof. They wait for it to rain, then scramble for buckets. Effective management starts before the test fails.
1. Treat Your Test Suite as a Living System
Tests degrade over time as codebases evolve, environments drift, and dependencies change. Schedule deliberate test health reviews, not just post-incident retrospectives. Audit execution history for instability patterns regularly.
2. Detect Flaky Tests Before They Break Builds
The moment a test shows non-deterministic behavior, it needs to be flagged and triaged. TestMu AI's Test Intelligence uses machine learning to analyze historical execution data and surface tests showing early signs of instability, shifting the conversation from "why did CI fail again?" to "which tests are trending toward instability?"
3. Debug Flaky Tests With Data, Not Guesswork
Root cause analysis is difficult because flaky failures are not always reproducible on demand. Error classification and log trend analysis help find patterns across failures, whether the flakiness stems from a timing issue, a specific command, or a platform anomaly.
4. Quarantine Flaky Tests Instead of Ignoring Them
When a flaky test cannot be immediately fixed, remove it from the CI/CD pipeline critical path while it is investigated. Quarantine comes with a ticket, an owner, and a deadline. Ignored tests become permanent noise that desensitizes teams to real failures.
5. Document Every Flaky Test Resolution
Capture the root cause, environment conditions, fix, and version control history for every resolved flaky test. The next engineer who sees a similar failure gets a starting point instead of a blank slate. Teams using AI-native test intelligence can auto-log failure context directly from execution data, reducing documentation overhead.
6. Make Flaky Test Ownership Cross-Functional
A timing-dependent test might indicate an unstable API owned by the backend team. An environment-specific failure might point to infrastructure drift. Resolving flakiness at scale requires developers, QA, and infrastructure teams working from shared dashboards. Tools like TestMu AI Test Analytics surface anomalies across browsers, devices, and environments so no single team is flying blind.
How to Fix Flaky Tests?
Flakiness in automated tests can be a significant hurdle in achieving reliable and consistent testing outcomes. To mitigate the impact of flaky tests and improve the stability of automated testing, it is essential to implement effective strategies. These strategies target the underlying causes of flakiness and help ensure more dependable and accurate test results. Reducing flakiness requires a combination of proactive measures and best practices. Here’s a list of strategies that help reduce flakiness:
- Test Isolation: Test isolation plays a critical role in reducing flaky tests by eliminating interference from external dependencies and unpredictable factors. When tests are tightly coupled with shared environments or third-party services, even minor inconsistencies can lead to intermittent failures. As highlighted below:
Flaky tests are a real challenge, especially when they sneak into production. From my experience, the first step is identifying patterns. Many flaky tests fail intermittently due to timing issues, environment dependencies, or external service calls. Once you pinpoint these causes, you can apply targeted strategies. For instance, using test isolation and mocking external services reduces unpredictability.
- VP of Product Strategy at Action1, with over a decade of experience leading QA and automation teams in complex software environments. - Clearing Test Dependencies: Ensuring proper cleanup of test dependencies and resources after each test execution can prevent interference between tests. By removing any residual state or dependencies, tests start with a clean slate, minimizing the chances of flakiness due to leftover artifacts.
- Stabilizing the Environment: Stabilizing the test environment by addressing environmental factors and minimizing fluctuations can improve test reliability. This includes factors such as network stability, server load, or system configurations. A stable and controlled environment reduces the chances of flakiness caused by external influences.
- Consistent Test Data Generation: Using consistent and reproducible test data generation techniques, such as using fixed data sets or mock data, can help reduce flakiness. When tests rely on predictable and consistent data, they are less likely to produce inconsistent results and exhibit flakiness.
- Synchronization Techniques: Applying synchronization techniques, like explicit waits and timeouts, can address timing and synchronization issues that lead to flakiness. By synchronizing test steps with the application under test, tests can wait for desired conditions before proceeding, reducing the chances of flakiness caused by timing mismatches.
- Employ Test Retry Mechanisms: Introducing retry mechanisms in tests to rerun flaky test cases can help ensure accurate and consistent results. By retrying the test a certain number of times, intermittent issues causing flakiness may resolve, leading to more reliable outcomes.
How to Maintain a Flaky Test-Free Test Suite?
Having a reliable and stable test suite is crucial for ensuring accurate and dependable test results. Flaky tests can undermine the effectiveness of the testing process, leading to wasted time, unreliable outcomes, and reduced confidence in the software being tested. To mitigate the impact of flakiness and maintain a flaky test-free test suite, it is essential to establish proactive maintenance practices and implement effective strategies.
- Regular Test Maintenance: Conduct regular test maintenance activities, such as reviewing and updating tests, removing redundant tests, and resolving flakiness issues. By regularly maintaining tests, teams can ensure that tests remain relevant, reliable, and free from flakiness, leading to more accurate and actionable results.
- Continuous Monitoring: Implement a system for continuous monitoring of test execution and results to promptly identify and address flaky tests. By continuously monitoring test runs and analyzing results in real-time, teams can quickly detect flakiness and take immediate corrective actions, reducing its impact on the testing process.
- Collaboration with Team Members: Foster collaboration between developers, testers, and other team members to collectively address and resolve flaky test issues. By encouraging open communication and collaboration, teams can share insights, leverage diverse perspectives, and collectively work towards identifying the root causes of flakiness and implementing effective solutions.
- Collecting and Analyzing Test Metrics: Collect and analyze relevant test metrics, such as test execution times, failure rates, and flakiness patterns, to gain insights and make data-driven decisions. By tracking and analyzing test metrics, teams can identify trends, patterns, and correlations that provide valuable insights into the nature and extent of flakiness, enabling targeted efforts for improvement.
- Test Case Prioritization: Prioritize test cases based on their criticality and impact on the system under test. By prioritizing tests, teams can allocate more resources and attention to critical tests, ensuring that they receive thorough testing and addressing any flakiness issues that may arise.
Leveraging TestMu AI’s AI-Native Test Intelligence
TestMu AI's Test Intelligence is designed to reduce flaky tests and enhance test stability. Here are some key features offered by TestMu AI's AI-Native Test Intelligence Platform that can help in addressing flaky tests:
- Intelligent Flakiness Detection: LambdaTest's Test Intelligence uses machine learning algorithms to analyze test execution data and identify flaky tests, prioritizing efforts for resolution.
- Error Classification of Log Trends: Test Intelligence analyzes test logs, classifies errors based on trends, and helps identify recurring issues contributing to flakiness.
- Command Logs Error Trends Forecast: Test Intelligence forecasts error trends by analyzing command logs, providing proactive recommendations to prevent or resolve issues affecting test reliability.
- Anomalies in Test Execution across Platforms: Test Intelligence detects anomalies in test execution across platforms, helping teams prioritize efforts to improve stability for specific environments.
These features provided by TestMu AI's Test Intelligence empower teams to take data-driven actions in identifying, resolving, and preventing flaky tests.
KaneAI by TestMu AI takes this a step further by integrating AI-driven capabilities for test authoring, management, and debugging. It is a GenAI native QA Agent-as-a-Service platform for high-speed quality engineering teams that enables teams to effortlessly create, evolve, and maintain complex test cases using natural language, streamlining the testing process from start to finish.
Key Takeaways
- Flaky tests are inconsistent tests that pass or fail without any code changes, reducing trust in your test suite.
- Timing issues and race conditions are one of the most common causes of flaky behavior.
- External dependencies like APIs and third-party services can introduce unpredictability in test results.
- Hardcoded waits (sleep) often lead to failures — condition-based waits are more reliable.
- Dynamic UI elements can break selectors, making tests unstable across runs.
- Shared test data or environments create dependencies between tests and increase failure risk.
- Tests should always be independent — order dependency leads to unreliable execution.
- Running tests across different browsers, devices, and environments can expose inconsistencies.
- Limited system resources in CI/CD pipelines can impact test performance and stability.
