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A check that has only reported success has not yet shown that it can detect the failure it is meant to catch. To validate it, give it a known failure, confirm that it flags the problem, and trace that signal to the person or process expected to respond.
Why a green result is not proof
A passing check tells you what happened on the cases it observed. It does not, by itself, show that the check would notice an incorrect result. The check may not actually run, may inspect the wrong representation of the data, or may report success even when its internal test found a problem.
In “Ways of Checking,” Phronesis describes scripts that accumulated “BAD” results but still reported success because failure state was lost in a subshell. It also describes checks searching for a literal form that differed from what the framework actually emitted. In both situations, a green result did not establish that the check was observing and reporting the intended condition.
It helps to separate three questions:
- Execution: Did the check run, and did it reach the relevant test or system?
- Sensitivity: Does the check detect the specific fault it is meant to catch?
- Consequence: Does a detected failure reach someone or something able to act on it?
Test a check with a known failure
Choose a controlled fault whose expected effect is clear. Run the check and verify that it produces a failure—not merely a warning or a green status. Then follow the resulting signal through the normal workflow, such as a build failure, alert, or review task, and confirm it reaches its intended destination.
- Name the failure mode. State the concrete behavior or condition that the check is supposed to catch.
- Introduce a controlled failing case. Use a safe, reversible example that should trigger the check. For a test suite, this may be a deliberate code change; for another kind of check, it may be a known-bad input.
- Run the check in the relevant environment. Confirm it runs against the deployed system or the representation that system actually uses, rather than a convenient but different format or layer.
- Inspect the result. Make sure the known fault changes the result to failure and that the failure is not swallowed, overwritten, or reduced to a message that downstream automation ignores.
- Trace the response. Check that the failure reaches the person or process meant to prevent harm, and restore the controlled change afterward.
A failed challenge is useful evidence: it shows the check can detect that particular seeded fault under those conditions. It does not establish that the check catches every possible failure.
Use mutation testing to probe a test suite
Mutation testing applies this idea systematically to software tests. A mutation-testing tool makes small changes to code—for example, negating a conditional—and runs the test suite. If a test fails, the mutation is “killed.” If the suite still passes, the mutant “survives,” suggesting the tests may not distinguish the changed implementation from the original in that case.
Google’s explanation of mutation testing frames it as a way to evaluate whether tests detect injected faults. A surviving mutant is a prompt to investigate, not automatic proof that a test is defective: the change may be behaviorally equivalent, irrelevant to a requirement, or otherwise not worth adding a test for.
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Review surviving mutants against the behavior the code is meant to provide. Ask whether a user-visible or otherwise important requirement changed, whether the suite should detect that change, and whether a focused test can express the requirement. A mutation score can help direct attention, but it cannot replace that judgment.
Account for cost and noise
Mutation tools may need to run tests repeatedly, so broad mutation generation can consume substantial execution and review effort. Filtering or prioritizing mutations can make the work more practical, but the resulting set is still a sample of possible changes—not a complete inventory of real faults.
In a 2021 experiment reported by Google Testing Blog author Goran Petrovic, a bug was coupled with a mutation in around 70% of cases. In the same experiment, more than 90% of lines had either all generated mutants killed or none, and the study involved 33 million test-suite executions. These figures describe that experiment and its code base; they are not a universal success rate, threshold, or expected result for every project.
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Coverage and passing tests answer different questions
Coverage can show which lines or other measured elements tests exercised. Passing status shows that the tests completed without reporting failure. Neither alone establishes that the tests assert meaningful behavior. A test can execute a line without checking whether its result is correct.
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Google’s coverage guidance points to mutation testing as a way to probe whether tests both exercise covered lines and assert on failures. Treat coverage as information about reach, not as proof of test sensitivity.
Best Value
Decide whether a validation is useful
Before adding more checks or tests, judge the validation along five dimensions:
- Failure mode: Is the fault specific and relevant to a real requirement?
- Sensitivity: Does introducing that fault make the check fail?
- Representation and placement: Does the check inspect the format, layer, and environment that matter in actual use?
- Consequence: Does the failure signal reach an actionable destination?
- Cost and noise: Is the effort to run and review the validation proportionate, and are the flagged changes meaningful?
The useful outcome is not simply more red results or a higher score. It is evidence that a check detects a relevant fault and that its signal can prompt the intended response.
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