What are Flaky Tests?

You run your test suite, and a test fails. You rerun it without changing anything, and it passes. This is what engineers call a flaky test. It’s a test that produces inconsistent results on identical code and configuration. This non-deterministic behavior means the test failure doesn't point to a real bug but rather a problem with the test itself or the environment it runs in.

Every engineering team that runs automated tests at any scale has dealt with flakiness. And most have developed the same bad habit in response: rerun the test suite, ignore the failure, and merge the code anyway.

Flaky tests share a few common root causes.

• Race conditions are one of the biggest. Test results become unpredictable when tests depend on timing or the order of asynchronous operations.

• External dependencies, such as APIs, databases, or third-party services, are another cause of instability. If a network call times out or a service returns slowly, the test fails due to latency or availability issues, not because of your code changes.

• Test environment inconsistencies are equally common. A test that passes on one machine and fails on another often points to differences in configuration, test data, or resource availability.

• Order dependency occurs when one test’s outcome depends on state left behind a previous test. The test passes in isolation but breaks unpredictably when run as part of a full test suite, making the root cause almost impossible to trace without test-level observability.

Test flakiness is a cause for concern because it erodes trust in your entire testing process.

Developers stop paying attention to tests if they’re unreliable. Failures get dismissed with a rerun instead of being investigated. Retries pile up, burning CI/CD pipeline compute and stretching feedback loops. Over time, the test suite becomes noise rather than signal, and the team loses the ability to distinguish a flaky test from a real regression.

This can be particularly damaging at enterprise scale. More code changes mean more test execution, more flakiness, and more wasted cycles. CI pipelines slow down, developers context-switch while waiting, and release velocity stalls. And all of this happens because the team can't trust its own test results.

Fixing flaky tests starts with finding them. That means investing in flaky test detection: identifying which tests produce inconsistent results and quarantining them so they stop blocking the pipeline while you investigate the root cause. From there, remediation typically involves stabilizing test environments, removing or mocking external dependencies, cleaning up shared test data, and eliminating order dependency between test cases.

At scale, this becomes a significant engineering problem in its own right. In large codebases with thousands of test cases, manually triaging which failures are flakes and which are real bugs is a significant time sink for engineering teams.

Modern test intelligence tools can automate flaky test detection and surface the worst offenders by impact on your CI pipeline. They help teams focus their debugging time on handling failures that represent real regressions rather than environmental noise.

CloudBees Smart Tests does this automatically: detecting flaky tests, grouping failures by root cause, and routing them to the right owner so engineering time goes toward fixing real bugs, not chasing noise.