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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA passing test suite shows that its checks passed for the inputs and conditions they exercised. It does not prove the software is correct: a bug can survive if the tests miss the relevant behavior, fail to assert the right result, or produce an unreliable signal.
What a passing test run actually tells you
A green run is evidence about a specific set of inputs, assertions, and execution conditions. It says those checks did not detect a failure in that run. It cannot establish that every important behavior was tested or that every incorrect result would make a test fail.
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That distinction explains how wrong code can pass: the defect may be outside the paths the tests execute, or the code may execute while the assertions are too weak to distinguish the correct outcome from the wrong one.
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Why coverage does not prove the tests are strong
Code coverage helps show which code ran during a test run. It can reveal unexecuted paths, but execution alone does not show that a test checked the right outcome. A test might run a line of code without asserting a meaningful result.
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Google’s guidance distinguishes coverage from test effectiveness: coverage can help locate gaps, while mutation testing can probe whether tests detect plausible changes to covered code. Google’s coverage best practices explain why a coverage figure is not a stand-alone measure of test quality.
How mutation testing finds tests that miss errors
Mutation testing makes controlled, small changes to code—such as changing a condition—and runs the tests against the altered version. If the tests fail, they detected that change. If they still pass, the mutant survived, which can reveal a missing or ineffective assertion.
Google describes applying mutation testing to code changes during review so developers can see whether their tests catch plausible faults. A surviving mutant is a prompt to inspect the test gap, not automatic proof that a test is needed: some mutations may be redundant or have little practical value. A mutation score is therefore diagnostic evidence, not a correctness guarantee. Google’s discussion of mutation testing describes the approach and its use in code review.
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How flaky tests weaken a green result
A flaky test can pass and fail on the same code, so its result is not a dependable signal that the code changed or that a defect was introduced. A green run that includes unreliable checks is harder to interpret; repeated failures may also obscure genuine regressions.
In a 2016 account, Google’s John Micco reported that about 1.5% of test runs in Google’s corpus were flaky and that about 16% of tests showed some level of flakiness. He also said about 84% of observed transitions from pass to fail involved a flaky test. These are historical figures from Google’s test corpus, not present-day measurements or estimates for the software industry. Micco’s account of flaky tests at Google provides that context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a testing strategy
There is no universal test count or coverage percentage that qualifies a release. The right mix depends on the software, its risks, and the people who rely on it. Google recommends using multiple test levels suited to the system, including unit and integration tests, plus end-to-end tests for critical user journeys. Google’s testing strategy guidance discusses balancing these layers.
- Unit tests: Check focused behavior and edge cases close to the code where faults occur.
- Integration tests: Check that connected components behave correctly together.
- End-to-end tests: Exercise critical user journeys through the system, where failures have direct consequences for users.
Use coverage to find code that tests do not reach, and use mutation testing to investigate whether tests would catch plausible mistakes in important code. Treat flaky tests as a reliability problem, not as dependable evidence of correctness. The release decision should reflect the software’s risks and the strength of the checks—not a single green indicator.
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