If all your tests pass, how do you know they’d catch a bug? A green run means the tests that ran did not report a failure for the code, inputs, environment, and checks used in that run. It does not prove the software is defect-free or that the tests would notice a plausible defect.
What a passing test run actually tells you
A test result is evidence about a bounded set of conditions: which tests were selected and executed, what inputs they used, what environment they ran in, and what outcomes their assertions considered correct. Change any of those, and the result may change.
The International Software Testing Qualifications Board states the principle plainly: “Testing can show that defects are present, but cannot prove that there are no defects.” That wording appears in its Certified Tester Foundation Level syllabus, version 2018 v3.1.1, released 1 July 2021. Read the ISTQB syllabus and certification information.
Exhaustively checking every input and precondition combination is generally infeasible except in trivial cases, so teams choose what to test using risk, priorities, and test-design techniques. A suite can be green while an untested input, state transition, dependency, or user workflow still contains a defect.
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Coverage shows execution, not whether a test would notice a bug
Code coverage can help answer, “Did this test run that line or branch?” It cannot, on its own, answer, “Would the test fail if that behavior were wrong?” A test may execute a function but assert nothing meaningful about its result; a weak assertion can let an incorrect outcome pass.
Google’s guidance on coverage recommends treating it as a way to find untested code, not as a standalone measure of test quality. Edge cases and meaningful assertions still matter. Google Testing Blog: Code Coverage Best Practices.
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A useful anecdote illustrates the distinction. In a September 2026 DEV Community article, Hamber reported writing four tests for an audio-glitch fix in GoGBA. The author said all four passed, but none exercised the function that configured the fix, and two still passed after the fix was disabled. This is the author’s account, not an independently verified reproduction; its practical lesson is to check that tests reach the behavior they claim to cover and depend on the fix working. Hamber’s GoGBA testing account.
Mutation testing asks whether tests detect deliberately introduced faults
Mutation testing probes a suite by making small changes to code—such as changing a condition or operation—and checking whether tests fail. Google author Goran Petrovic describes it as “a method of evaluating test quality by injecting bugs into the code and seeing whether the tests detect the fault or not.” Google Testing Blog: Mutation Testing.
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- A killed mutant changes behavior and causes one or more tests to fail, showing that the suite is sensitive to that particular change.
- A surviving mutant does not trigger a failure. It may point to a missing test or an assertion that is too weak—but it may also be equivalent to the original code for the tested behavior, or otherwise unproductive. Review is needed before treating every survivor as a defect in the suite.
Google’s 2021 article reports an experiment that executed 33 million test suites while examining mutants related to historical bug fixes. In that experiment, the authors reported that a bug was coupled with a mutation in around 70% of cases; in more than 90% of cases, either all mutants on a line were killed or none were. These are observations from Google’s setup, including its mutation-filtering heuristics and codebase context—not predictions of what another team’s tests will catch.
Mutation testing can be expensive to run and can produce noisy results that require engineering judgment. It is one probe of test sensitivity, not a certification that a suite catches every realistic defect.
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How to make a green suite more informative
- Start from the requirement or invariant. Write down what must be true from the user’s or system’s perspective before encoding the check. A test derived only from the current implementation can confirm that implementation’s behavior without showing that it matches the intended behavior.
- Make the assertion specific. Check an observable outcome that matters: a returned value, persisted state, emitted event, or user-visible result. Avoid assertions so broad that incorrect behavior can satisfy them. Google’s guidance on actionable test failures recommends precise invariants that make failures useful without making tests brittle. Google Testing Blog: Test Failures Should Be Actionable.
- Choose edge cases by risk. Add cases for boundary values, invalid inputs, error paths, state changes, and interactions that could plausibly break the requirement. Since exhaustive combinations are usually impractical, prioritize consequential failure modes rather than trying to test every possible combination.
- Confirm the test reaches the behavior. Trace the test’s setup and execution path to the code or integration point it is meant to exercise. For a fix, ask whether removing or reversing that fix would make the test fail.
- Use the right evidence for the question. Unit tests can check local behavior; integration or acceptance tests can check interactions and user-visible workflows; coverage can reveal execution gaps; mutation tests can probe whether selected changes trigger failures; and requirements review can expose expectations the tests never encoded. None is a universal quality score.
Which testing evidence answers which question?
| Evidence | What it helps reveal | What it cannot establish alone |
|---|---|---|
| Execution coverage | Which code ran under selected tests. | That the expected behavior was asserted or that the tests would reject a wrong result. |
| Mutation testing | Whether tests detect particular injected code changes. | That all realistic defects would be detected; surviving mutants also need review for equivalence or low value. |
| Integration or acceptance tests | Whether selected components or workflows behave as expected in the tested setup. | That untested environments, paths, or requirements are correct. |
| Requirements and invariant review | Whether test expectations correspond to intended behavior and meaningful constraints. | That the implementation satisfies those expectations in every possible condition. |
These methods have different costs, runtime, scope, and potential brittleness. The useful question is not which single number proves quality, but what uncertainty remains after the evidence you have gathered.
What you can conclude from a green build
Say that the selected tests passed in the tested environment for the exercised cases. You can reasonably treat that as increased confidence within that scope. Do not translate it into “there are no bugs” or “the suite will catch any regression”: those claims go beyond what a finite set of checks demonstrates.
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