🔍 Introduction
In a stable codebase, nothing should fail — and yet, some tests do. They pass one moment, fail the next, without any code changes. These unpredictable failures are known as flaky tests — and while they may seem like minor annoyances, they slowly erode trust in your test suite, reduce team confidence, and introduce hidden costs into your delivery pipeline. Most dangerously, flaky tests are often tolerated or ignored, slowly undermining the very foundation of test automation. In this blog post, we’ll take a closer look at what causes flaky tests, how they affect CI/CD pipelines, and how to build long-term strategies to detect, manage, and reduce them.
⚠️ What Is a Flaky Test?
A flaky test is a test that produces inconsistent results — it passes sometimes and fails at other times, even when the underlying code remains unchanged. Developers often assume these failures are temporary glitches, rerun the test, and move on. But over time, this behavior leads to a broken feedback loop.
Common consequences include:
• Reduced confidence in automated test results
• Developers ignoring failed tests (“it’s probably just flaky”)
• Slower and less reliable deployments
• Increased manual verification effort
🧪 Common Causes of Flaky Tests
1. Timing and Synchronization Issues
One of the most frequent causes. For example, a test might try to interact with a UI element before it has fully rendered.
2. Environmental Dependencies
Network latency, server load, memory leaks, or other non-deterministic factors can cause inconsistent test behavior.
3. Shared State Between Tests
When multiple tests depend on the same data or environment, they can interfere with each other, causing unpredictable results.
4. Third-Party Services
Tests that rely on external APIs or services might fail due to network hiccups, rate limits, or service unavailability.
5. Improper Setup or Teardown
If the system state isn’t correctly cleaned up after each test, the next test may start in an unexpected condition.
🧩 The CI/CD Impact
In a CI/CD environment, flaky tests can do real damage. They create noise in the pipeline, delay releases, and increase the cognitive load on developers and testers. Instead of enabling fast feedback, flaky tests often lead to:
• Unnecessary reruns and wasted computing resources
• Delayed builds or blocked merges
• Increased false positives or false negatives
• Teams silently accepting failed tests as “normal”
This leads to a dangerous normalization of failure — where no one trusts the tests anymore.
🔧 Strategies to Mitigate Flaky Tests
1. Track and Tag Flaky Tests
Use tags or metadata to identify flaky tests. Build dashboards to monitor flakiness trends over time.
2. Stability Metrics
Measure how frequently each test passes vs. fails. Any test below a certain threshold (e.g., 95% pass rate) should be flagged for review.
3. Test Isolation
Ensure each test runs independently and doesn’t depend on shared data, state, or side effects from previous tests.
4. Smart Waiting and Synchronization
Avoid static waits. Use dynamic wait mechanisms (e.g., wait until element is visible, API responds, or condition is met).
5. Stable Test Environments
Use dedicated environments or containers for testing. Avoid using shared resources unless properly isolated.
6. Root Cause Analysis
Don’t just rerun flaky tests. Investigate the failure, determine the root cause, and either fix the test or remove it until stable.
💡 Conclusion
Flaky tests are more than just a nuisance — they’re a threat to your testing culture. Over time, they weaken your delivery process, undermine your automation investment, and drain team morale. Fixing flaky tests requires discipline, observability, and a willingness to pause and reflect on what “automated quality” really means. A test suite that can’t be trusted is worse than no automation at all — it’s an illusion of safety.