10 Ways to Reduce Flaky Tests in Your CI/CD Pipeline

Flaky tests are a major headache for CI/CD pipelines, causing random failures, delayed releases, and wasted resources. Here's how you can tackle them:

• Automate Flaky Test Detection: Tools like Jenkins Flaky Test Handler spot inconsistencies early.
• Leverage AI for Insights: AI tools predict flaky behavior and identify root causes.
• Isolate Test Environments: Use Docker or cloud-based setups to avoid interference.
• Handle Async Operations: Replace fixed waits with dynamic conditions.
• Write Independent Tests: Eliminate dependencies between test cases.
• Implement Smart Retry Logic: Use retries with limits and backoff strategies.
• Control Test Order: Sequence tests to avoid resource conflicts.
• Quarantine Unstable Tests: Isolate flaky tests and track fixes.
• Monitor Test Results Over Time: Analyze trends to catch recurring issues.
• Review Test Quality Regularly: Audit metrics like pass rates and flakiness rates.

These strategies can reduce failures, save time, and improve trust in your pipeline. For example, Spotify cut flaky tests from 4.5% to 0.4%, saving 120 developer hours weekly.

Strategy	Key Benefit
Automate Flaky Detection	Identify issues early
Use AI Tools	Predict and resolve flakiness
Isolate Environments	Reduce interference
Handle Async Operations	Improve test reliability
Write Independent Tests	Eliminate dependencies
Smart Retry Logic	Efficiently manage retries
Control Test Order	Avoid resource conflicts
Test Quarantine Systems	Isolate problematic tests
Monitor Test Results	Spot recurring patterns
Review Test Quality	Maintain pipeline stability

Start with automated detection and environment isolation, and build from there to make your CI/CD pipeline more reliable.

Common Mistakes that make test cases flaky and how to avoid them

1. Set Up Automatic Flaky Test Detection

Automated detection systems are a game-changer for managing flaky tests. They:

• Spot inconsistent test behavior early, preventing disruptions in your development workflow.
• Offer insights to guide decisions on test maintenance.
• Act as early warning systems for potential problems.

For example, Spotify's custom detection tool cut test suite time by 22% and reduced false failures by 35% within just three months. Here's how to get started:

1. Configure CI to Rerun Failed Tests
   Use tools like Flaky Test Handler (Jenkins) or TestGrid (CircleCI) to rerun failed tests. These tools analyze test executions statistically, helping you pinpoint tests that fail inconsistently without any code changes.
2. Set Failure Thresholds
   Define clear thresholds, such as a failure rate greater than 5% without code changes, to flag flaky tests objectively. This aligns with the multi-layered approach mentioned earlier.

Advanced detection systems (discussed further in Section 2) bring even more capabilities, such as:

• Recognizing patterns in common failure scenarios.
• Calculating flakiness rates using statistical methods.
• Triggering automated quarantines for problematic tests.
• Monitoring trends over time to identify long-term issues.

To make these systems even more effective:

• Integrate with notification tools to keep teams informed.
• Automate alerts so new flaky tests are addressed immediately.
• Track detection metrics to refine and improve the system over time.

This detection setup lays the groundwork for more advanced solutions, like AI-driven analysis (covered in the next section) and quarantine systems (explored in Section 8).

2. Use AI Tools for Test Analysis

AI-powered tools take flaky test management to the next level by offering predictive insights based on test execution data. These tools analyze historical patterns to uncover issues that manual reviews might miss.

What AI Tools Can Do

• Spot Patterns and Predict Issues
  AI systems track test execution patterns and can predict flaky behavior with up to 85% accuracy ^[7].
• Identify Root Causes Quickly
  By linking failures to factors like system resources, network conditions, or code dependencies, AI tools can cut debugging time by as much as 60% ^[2]. This is especially helpful in CI/CD workflows where fast feedback is essential.

How to Get Started

To make the most of AI analysis, follow these steps:

1. Integrate AI tools into your testing pipeline.
2. Optimize how you collect and store test data.
3. Regularly refine your AI models to improve accuracy.

Tips for Success

• Start with test suites that are high-priority or resource-intensive.
• Cross-check AI findings with known flaky tests to verify accuracy.
• Focus on alerts that affect critical parts of your pipeline.

Popular AI Solutions

• Bugster: Offers AI-driven testing with features like adaptive tests and CI/CD compatibility (bugster.dev).

Companies using tools like Bugster have reported cutting maintenance hours by 30–50% ^[9]. The trick is to choose solutions that deliver real-time insights without slowing down your workflow.

3. Keep Test Environments Separate

Environmental isolation plays a key role in solving flaky test issues by eliminating interference at the source. This approach ensures consistent test results, directly addressing the "eroded trust" problem mentioned earlier. Research shows that organizations adopting environment separation see a 40-60% drop in flaky tests ^[2].

Strategies for Isolating Environments

One popular solution is Docker, with 78% of organizations using Docker containers reporting better test reliability ^[11]. While virtual machines provide complete isolation, they often demand more resources compared to container-based solutions ^[2]. For larger test suites, cloud-based environments offer a flexible way to allocate resources while maintaining isolation.

Tips for Implementation

To successfully establish isolated environments:

• Automate infrastructure setup: Use tools like infrastructure-as-code to create consistent setups every time.
• Reset environments regularly: Ensure environments are reset between test runs to avoid leftover data interfering with results.

Challenges to Watch For

Teams often encounter issues like high resource costs and configuration drift. To address these, consider dynamic provisioning to optimize costs and use version-controlled configurations to maintain consistency.

With isolated environments in place, you're better prepared to tackle async operations, which we'll dive into next.

4. Handle Async Operations Correctly

After setting up isolated test environments (Section 3), it's time to tackle asynchronous operations - a common culprit behind CI/CD flakiness ^[7][11].

Smarter Waiting Techniques

Ditch fixed sleep times and opt for explicit wait conditions that adapt to the system's state. This not only cuts down false negatives but also improves test efficiency. Here's how you can do it:

// Avoid this approach
// setTimeout(() => checkResult(), 5000);

// Use conditional waiting instead
await waitForCondition(async () => {
  const result = await checkResult();
  return result.status === 'complete';
}, maxRetries = 3, interval = 1000);

Dealing with Race Conditions

Timing issues can wreak havoc on async tests. Use these methods to keep things in order:

• Execute promises in sequence.
• Manage states with event-driven logic.
• Apply retries that consider the current context.
• Ensure resources are cleaned up properly after use.

Async Tools at Your Disposal

Modern testing frameworks come with built-in features to handle async operations effectively:

Tool	Async Feature
Cypress	Auto-waiting mechanisms
TestCafe	Smart assertions
Playwright	Network request handling

Boosting Performance

Consider these techniques to optimize performance when working with async operations:

• Use dynamic timeouts with exponential backoff for polling.
• Implement retry logic for tasks that depend on network responses.

Best Practices for Error Handling

Comprehensive error management is essential for async operations. Here's an example:

try {
  await asyncOperation();
  expect(result).toBe(expected);
} catch (error) {
  throw new Error('Async operation failed unexpectedly');
}

With a solid approach to async handling, you're ready to move on to designing independent test cases - a key step in reducing flakiness further.

5. Write Independent Test Cases

Let's dive into an essential strategy for reducing test flakiness: creating independent test cases. These are self-contained tests that run reliably, no matter the state or order of other tests.

Why Test Isolation Matters

Microsoft's engineering team showed how impactful test independence can be. By refactoring their tests for better isolation, they cut test maintenance costs by 40% ^[2][3][4]. This also ties into their finding that 4.9% of CI failures were due to test instability.

Here’s how you can turn a dependent test into an independent one:

// Before: Relies on shared state
test('user can update profile', async () => {
  const user = globalTestUser; // Problem: shared state dependency
  await updateProfile(user, newData);
  expect(user.profile).toEqual(newData);
});

// After: Self-contained and independent
test('user can update profile', async () => {
  const user = await createTestUser(); // Creates fresh test data
  const newData = generateUniqueProfileData();
  await updateProfile(user, newData);
  expect(user.profile).toEqual(newData);
  await cleanupTestUser(user); // Cleans up after itself
});

Key Elements of Independent Tests

To make your tests truly independent, focus on these two essential practices:

• Unique Data Generation: Ensure each test generates its own data.
• Dependency Injection: Mock external dependencies to isolate functionality.

Component	How to Implement It
Unique Data Generation	Create fresh, unique data for each test
Dependency Injection	Use mocks for external systems or services

Using Test Containers for Better Isolation

Tools like TestContainers can help by providing temporary, disposable environments for your tests. They ensure that each test starts with a clean slate.

How to Measure Test Independence

Keep an eye on these metrics to evaluate your progress:

• Flakiness rates when no code changes are made
• Time required for test setup
• Success rates of parallel test execution

6. Add Smart Test Retry Logic

Once you've handled async operations with proper waiting techniques (see Section 4), the next step is to introduce smart retry logic. This helps tackle any remaining instability. While earlier sections focus on preventing issues, smart retries act as a safety net for unavoidable flaky tests. According to Google's research, 84% of transitions from passing to failing tests are due to flaky behavior, not actual bugs ^[10].

Key Components of Smart Retry Systems

Component	Purpose	Implementation
Failure Analysis	Categorize test failures	Use error signatures to classify failures
Historical Tracking	Spot patterns of flakiness	Store test results with relevant metadata
Environmental Monitoring	Detect system-level issues	Monitor real-time infrastructure metrics

Best Practices from Industry Leaders

Building on the detection systems discussed in Section 1, here are some retry practices to consider:

• Use exponential backoff - increase delays between retries gradually ^[2].
• Limit retries to 2-3 attempts to avoid hiding real problems.
• Set maximum retry limits to prevent endless loops.

Advanced Retry Strategies

To get the most out of your retry logic:

• Track retry patterns for individual tests to identify recurring issues.
• Use specific retry policies tailored to different types of failures.
• Set up alerts to notify you when retry thresholds are exceeded.

These strategies not only improve test reliability but also prepare you for managing test execution order, which will be discussed in the next section.

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7. Control Test Execution Order

Managing the order in which tests run is crucial to avoiding resource conflicts, especially during parallel execution. This approach helps tackle the issue of "higher compute costs" mentioned earlier by minimizing retries caused by execution clashes ^[7].

Smart Test Sequencing

Effective test sequencing starts with understanding how resources are used and where conflicts might arise. Here are some ways to organize your tests:

• Group tests based on shared resource needs and dependencies.
• Run independent tests first, followed by those that have dependencies.
• Use setup and teardown methods to reset environments between tests.
• Categorize tests by type (e.g., unit vs. integration) and execution speed.

Tools and Techniques for Implementation

Modern CI/CD tools and testing frameworks offer features to help with test ordering:

• Framework annotations: Use tools like TestNG's @Test(priority=) or JUnit 5's @Order annotations to control the sequence.
• CI/CD pipeline controls: Take advantage of Jenkins Pipeline stages or GitLab CI job dependencies to manage workflows ^[2].
• Test suites: Organize tests into separate suites based on their resource requirements.
• Randomized execution: Occasionally shuffle the test order to uncover hidden dependencies.

Tips for Smooth Execution

To ensure your test order control strategy works effectively:

• Design tests to safely run in parallel.
• Align the level of parallelism with available resources.
• Actively monitor resource usage during test runs.

This structured approach lays the groundwork for Section 8, which focuses on isolating problematic tests to maintain stability and reliability.

8. Set Up Test Quarantine Systems

After organizing test execution order (see Section 7), the next step is to establish quarantine systems for handling persistently unstable tests. Using your automated detection setup from Section 1, these systems can isolate problematic tests, reducing the repeated failures that drive up compute costs.

How to Build a Quarantine Process That Works

Set up quarantine systems to automatically manage flaky tests by:

• Identifying unstable tests based on recurring failure patterns.
• Generating investigation tickets for each flagged test.
• Tracking resolution progress to ensure accountability.
• Setting deadlines for cleanup and resolution.

For example, Facebook isolates tests that fail in just 1% of daily runs ^[12]. LinkedIn's automated system not only quarantines flaky tests but also creates investigation tickets for them ^[13]. Meanwhile, Etsy enforces a strict two-week deadline to resolve quarantined tests, keeping their test flakiness rate under 1% ^[4].

How to Manage Quarantined Tests

Microsoft's Bing team has integrated quarantine systems into their daily workflows. They use automated detection and conduct weekly reviews, demonstrating how this approach can minimize pipeline disruptions ^[5].

What to Monitor and Measure

Use a dedicated database to track quarantined tests. Focus on metrics like:

• Historical failure trends.
• Root causes of instability.
• Proposed solutions and their outcomes.
• Time each test spends in quarantine.
• Efforts to fix the issue and their results.

Integrating Quarantine into Your Workflow

To make quarantine systems effective, include these elements:

• Automated Detection: Quickly identify and isolate unstable tests.
• Clear Ownership: Assign team members to investigate and resolve quarantined tests.
• Regular Reviews: Schedule weekly check-ins to assess progress.
• Detailed Metrics: Monitor how long tests stay in quarantine and the success rates of fixes.

Once a test is quarantined, keep a close eye on its behavior over time. In Section 9, we'll dive deeper into setting up comprehensive monitoring systems for this purpose.

9. Track Test Results Over Time

Keeping a close eye on test results over time is crucial for maintaining stable CI/CD pipelines. By analyzing historical data, you can uncover subtle patterns that might otherwise slip through the cracks.

How to Track Test Results Effectively

Focus on tracking key metrics for each test, such as:

• Pass/fail rates: Monitor how often tests succeed or fail.
• Execution time trends: Look for changes in how long tests take to run.
• Failure conditions: Note environmental factors during failures.
• Code changes: Link failures to specific code updates.
• Retry attempts: Track how often tests need to be rerun.

A great example of success: Spotify cut flaky tests from 4.5% to just 0.4% in three months by analyzing data from 50,000 daily test executions. This saved their developers 120 hours every week.

Making the Most of Historical Data

Adjust your analysis window based on how often you release updates:

Development Cycle	Analysis Window	Focus Areas
Daily Releases	2-4 weeks	Recent issues and immediate patterns
Weekly Releases	1-2 months	Links between code changes and failures
Monthly Releases	3-6 months	Long-term trends and seasonal patterns

Using Machine Learning to Improve Testing

Machine learning can take your test tracking to the next level by:

1. Spotting Anomalies
ML models can sift through historical data to flag unusual patterns, helping you address potential issues early.

2. Predicting Test Instability
By studying past trends, ML can forecast which tests might become flaky, allowing you to fix them proactively.

3. Pinpointing Root Causes
Advanced algorithms can connect test failures to specific environmental conditions or code changes, speeding up troubleshooting.

Tools to Help You Track Test Results

Here are some popular tools for test result tracking:

• Jenkins Test Results Analyzer: Great for visualizing historical trends.
• Azure DevOps Test Analytics: Offers detailed insights into execution patterns.
• Allure TestOps: Provides advanced tracking and reporting features.
• TestRail: Excellent for managing test cases and analyzing historical data.

It's worth noting that only 0.15% of test failures usually point to actual bugs. The rest are often flaky tests ^[7]. Make sure your tracking aligns with the failure analysis methods discussed in Section 6.

Tips for Retaining Useful Data

To ensure your data remains actionable, follow these practices:

• Tag results with the corresponding code versions.
• Document any infrastructure changes.
• Use data aging policies to manage storage.
• Maintain detailed change logs.
• Regularly update mappings between tests and code.

This approach lays the groundwork for better quality control and smoother test reviews, as covered in Section 10.

10. Check Test Quality Regularly

In addition to the long-term tracking discussed in Section 9, make it a habit to conduct regular quality audits. This helps identify and address flakiness issues before they escalate. For example, one team managed to cut flaky tests by 80% by reviewing key stability metrics every two weeks ^[9].

Key Metrics to Monitor

Focus on these metrics to ensure your tests align with the failure thresholds from Section 1 and the retry logic in Section 6:

Metric	Target
Pass Rate	>99%
Flakiness Rate	<1%

Scheduling Quality Checks

Plan your test quality reviews at different intervals to cover all bases:

Review Type	Frequency	Focus Areas
Team Reviews	Weekly	Flaky test patterns, coverage gaps
Deep Analysis	Monthly	Trend analysis, improvement plans

Addressing Quality Issues

Use the prioritization framework from Section 6 to tackle issues effectively:

• High Priority: Tests that fail consistently
• Medium Priority: Tests with unpredictable execution times
• Low Priority: Small coverage gaps

"Proactive quality reviews help maintain the pipeline stability we established through test quarantines (Section 8) and environment isolation (Section 3)."

Test Rerun Options: Selective vs. Full Suite

After setting up quarantines (Section 8) and quality checks (Section 10), the next step is to refine your failure response with smart rerun strategies.

Selective Reruns: Focused and Efficient

Selective reruns concentrate only on the failed tests, cutting down on execution time and resource use. For example, CircleCI users have reported 92% faster execution times ^[10], and Microsoft's Azure DevOps team managed to reduce their CI duration by 40% while still catching 98% of issues ^[6].

Here’s a sample configuration for selective reruns in CircleCI:

- run:
    command: circleci tests split test/**/*.js | xargs npm test

Full Suite Reruns: Thorough but Time-Intensive

Full suite reruns, while slower, ensure every aspect of your code is validated. Netflix’s engineering team found that running full test suites periodically helped uncover intermittent issues missed by selective reruns ^[3].

Comparing the Two Approaches

The table below highlights the key differences between selective and full suite reruns, based on practical data:

Factor	Selective Reruns	Full Suite Reruns
Time Efficiency	3-13x faster than full suite [10]	Full duration (baseline)
Resource Usage	Lower, targeted resources	Higher, full environment
Issue Detection	98% of issues [6]	100% (complete coverage)
Best Use Cases	Daily development, fast feedback	Release candidates, periodic checks

Each approach has its place, depending on your goals and the stage of development.

Hybrid Implementation Strategy

A combined approach often works best. As one expert noted:

"Proactive quality monitoring combined with intelligent rerun strategies helped maintain the pipeline stability achieved through test quarantines (Section 8) and environment isolation (Section 3)."

Monitoring and Optimization

To ensure stability and efficiency, keep an eye on key metrics:

• Execution times and success rates: Track how long tests take and how often they pass.
• Resource usage: Monitor the resources consumed by each rerun strategy.
• Issue detection rates: Document how many issues each approach catches.
• Cost analysis: Evaluate the financial impact of your rerun strategy.

These practices will help you fine-tune your pipeline for both performance and reliability.

Conclusion

Reducing flaky tests in CI/CD pipelines can lead to clear, measurable improvements. For example, Datadog introduced automatic flaky test detection and saw a 30% drop in false negatives and a 25% boost in pipeline efficiency ^[1]. Their experience highlights how combining different strategies can significantly improve pipeline stability.

Although setting up detection systems (see Section 1) and quarantine processes (see Section 8) requires upfront effort, the results speak for themselves:

Impact Area	Improvement Achieved
Build Failures	40% reduction [8]
Pipeline Duration	30% shorter
Developer Productivity	25% increase

Long-term success depends on balancing technical solutions with process adjustments. Regularly monitoring test suite stability helps ensure these gains continue to grow over time ^[2][9].

For teams looking to improve, strategies like better test design (see Section 5) and leveraging AI-powered analysis (see Section 2) can deliver real improvements in pipeline reliability. Teams that adopt these approaches often see faster deployments, happier developers, and more dependable software delivery pipelines.

FAQs

How do you test for flakiness?

Testing for flaky behavior involves using a mix of strategies. Google’s engineering team, after analyzing over 16% of their test suite for flakiness ^[7], suggests two main approaches:

Method	Benefit
Repeated Execution	Detects inconsistent test outcomes quickly.
Historical Analysis	Identifies recurring patterns of intermittent failures.

These techniques complement automated detection systems (see Section 1) and historical tracking methods (see Section 9).

How do you stop flaky tests?

To address flaky tests, various strategies discussed in Sections 3 through 6 can be applied. These include using async handling patterns (Section 4), isolating environments (Section 3), and implementing smart retries (Section 6). These approaches have shown success in reducing flaky test occurrences across different systems ^[11].