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What makes a test flaky?
John Micco’s 2016 account of Google’s experience defines a flaky result as one in which “the same test exhibits both a passing and a failing result with the same code.” Fuchsia’s policy uses a similar standard: a test sometimes passes and sometimes fails when run using the exact same revision. The key condition is unchanged code; a failure after a code change may instead be a genuine regression. Neither definition identifies the cause by itself.
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Flakiness erodes the value of a test suite in two directions. A false failure wastes time and can make developers less likely to trust future failures. A flaky test can also pass when it should fail, allowing a real bug through. Fuchsia’s policy warns that flakes can let real bugs slip past its commit queue, devalue useful tests, and increase queue failures and latency.
How widespread is test flakiness?
Historical Google observations illustrate why teams take the problem seriously, but they are not current industry-wide estimates. In 2016, Micco reported that about 1.5% of all test runs produced a flaky result, almost 16% of Google’s tests had some level of flakiness, and about 84% of observed post-submit transitions from pass to fail involved a flaky test. These figures use different denominators and describe Google’s experience at that time; they should not be combined or treated as estimates for another team.
A separate Google Research summary reported that 4.56% of test failures across Google’s TAP continuous-integration executions were attributed to flaky tests during a 15-month window. That figure has a different scope from the 2016 observations and is not directly comparable. The practical lesson is not that a particular percentage of your suite must be flaky; it is that intermittent outcomes can materially undermine CI signals.
What a flaky-test finder should establish
The title of a tool does not establish how it detects flakes, how accurate it is, or what it costs to run. At minimum, a useful finding should help an engineer see why a test is considered suspicious: for example, the relevant runs and outcomes, the code revision, and whether those runs actually held the code constant. A label without enough context can turn an uncertain signal into another kind of noise.
When evaluating a detection approach, consider these questions:
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- Evidence: Can the team inspect the runs behind a flag and confirm that the same test and code revision produced different outcomes?
- Confidence: Does the method distinguish observed inconsistency from a confirmed root cause? A suspicious test is not necessarily understood or fixed.
- Cost: How much extra execution time and CI compute do retries or repeated runs consume?
- Regression risk: Could the policy allow a real failure to pass, or delay discovery until a test fails repeatedly?
- Workflow fit: Does the approach connect to the team’s CI process without leaving quarantined tests invisible?
- Diagnostic value: Does it preserve useful failure details for investigating the test, runner, application, dependencies, or environment?
Where flakiness can originate
Google’s 2021 guidance groups possible causes into four areas. A test can be unreliable because of its own logic; the framework or runner that executes it; the application under test or its dependencies; or the operating system, hardware, and network. A finder can point to a pattern, but these categories are places for engineers to investigate—not checks that any particular tool is known to perform automatically.
Test setup, state, and order
Look for setup, initialization, cleanup, or test-data assumptions that leave shared state behind. A test may pass alone but fail after another test changes a shared resource, or depend on a previous run having prepared something that it does not create itself. Run the test independently and compare that result with its behavior in the suite; the difference can reveal order dependence or hidden state.
Timing and asynchronous behavior
Inspect timeouts, race conditions, and assumptions about when asynchronous work finishes. An arbitrary sleep may appear to stabilize a test, but it can still fail when the system is slower than expected and can make every run slower. Prefer explicit synchronization on the application state the test needs—for example, waiting for the expected condition—rather than waiting a fixed amount of time.
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Runner and system readiness
Check whether the framework allocated enough resources and whether the system under test started successfully. A test failure caused by a runner that could not provide the expected resources is different from an application regression, even if both appear as red CI results. Preserve execution details that let the team distinguish them.
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Consider environmental dependencies the test does not control, including the operating system, hardware, and network, as well as services or components the application relies on. If a test assumes an external dependency is available or behaves identically on every run, that assumption can produce intermittent outcomes unrelated to the code change under review.
Best Value
Retries and quarantine: useful safeguards, not repairs
Rerunning failures can reduce false alarms, but a retry is not proof that the original failure was harmless. Requiring repeated failures before reporting a problem can also delay discovery of a genuine regression. The retry policy should therefore make the initial failure and subsequent outcomes visible, rather than quietly converting an inconsistent result into a pass.
Quarantine can remove a highly flaky test from the critical path and reduce disruption while engineers investigate. Fuchsia’s policy is explicit that removing flakes from the critical path should happen quickly, but the tests must not be ignored afterward. A quarantined test that no one owns or monitors can hide a race or real bug just as effectively as a retry policy can hide an intermittent regression.
A practical investigation workflow
- Confirm the comparison. Check that the differing results came from the same test at the same code revision. If the code changed, assess the failure as a possible regression rather than assuming it is a flake.
- Collect the run context. Keep the failing and passing outcomes, execution order, runner details, and relevant environment information together. This gives the investigator evidence to compare rather than a bare “flaky” label.
- Isolate the test. Run it independently and compare that result with its behavior in the full suite. If isolation changes the outcome, investigate order dependence and shared state.
- Trace the failure across the four cause areas. Check test setup and cleanup, framework resources and startup, application and dependency behavior, and environmental conditions such as operating system, hardware, or network.
- Replace timing guesses with conditions. Where a test relies on a fixed delay, synchronize on the actual application state it needs. Then run it repeatedly in the relevant CI context to see whether the change addresses the inconsistency.
- Keep mitigation visible until repair. If retries or quarantine are needed to protect the critical path, record the test’s status and keep it under investigation. Restore it to normal gating only when the team has addressed the cause and regained confidence in its result.
What success looks like
Finding a suspicious test is the start of the work, not the outcome. A useful process connects inconsistent runs to a reproducible cause, repairs that cause where possible, and keeps any temporary mitigation visible. The goal is not merely fewer red builds: it is a test suite whose failures developers can treat as evidence again.
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