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Two mutation-testing reports can both show 1.000 and still measure different things. The score is a ratio, so its denominator—and the mutant population and status rules behind it—are essential to interpreting the result. Before comparing the reports, find each formula and reconcile its raw counts.
What a mutation score of 1.000 tells you
A score of 1.000 means every mutant counted under that report’s rules was classified as killed or otherwise defeated. It does not, by itself, show that the reports counted the same mutants or that the test suite would detect every real-world defect.
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The conventional mutation score is the number of killed mutants divided by the number of non-equivalent mutants. In that definition, the denominator can include mutants that tests did not cover. A covered-code score instead can divide defeated mutants by the mutants covered by tests, measuring performance on a narrower population. The conventional definition and a covered-code formula described in an academic thesis illustrate why the same displayed value need not represent the same measurement.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhy the denominators can differ
All non-equivalent mutants versus covered mutants
A report using all non-equivalent mutants asks whether the tests defeated the eligible mutants in the run, including those that may be uncovered. A covered-code score asks whether tests defeated the mutants that were covered. If uncovered mutants are excluded from the latter denominator, a 1.000 there does not establish that the broader population was fully defeated.
Equivalent-mutant decisions
Equivalent mutants behave the same as the original program and are excluded from the conventional denominator. Determining equivalence is difficult; the Luxembourg mutation-testing repository notes that the general problem cannot be solved automatically. Reports may therefore differ in which mutants they identify, exclude, or leave unresolved. Do not assume equivalent-mutant handling matches across tools or runs.
What counts as defeated
Denominator differences are not the only source of disagreement. In the thesis’s covered-code formula, defeated mutants include killed, timed-out, and error outcomes. That is a specific formula, not a universal rule for all tools. Inspect each report’s definitions for killed, defeated, timeout, error, skipped, and uncovered statuses before comparing its score.
Reconcile the reports using counts, not rounded scores
For each report, record the formula and the population behind it. Use the tool’s report or documentation for the specific version and run; the title alone does not identify either report’s rules.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- Write down the formula. Identify whether the score is conventional, covered-code, or tool-specific, and what its numerator and denominator represent.
- Record run scope and generated mutants. Note whether the run covers a project, module, changed code, or selected operators, and capture the total generated mutants.
- Break down the outcomes. Record killed or defeated, surviving, uncovered, excluded, and equivalent mutants. Also capture timeout and error counts, and establish how each is treated in the score.
- Check selection and execution rules. Look for sampling, filtering, or incremental execution that might have changed which mutants entered the report.
- Recalculate from raw counts. Apply each report’s stated formula and compare the populations. If the denominators count different populations, report the scores as different measures—even if both display 1.000.
What can—and cannot—be concluded from this case
The general explanation is clear: denominator conventions and status rules can make two 1.000 scores represent different measurements. But without the tools, versions, project scope, raw counts, and report excerpts, it is not possible to determine which specific report is labeled correctly or reproduce either denominator. To settle the discrepancy, obtain those details and check the documentation for each tool version.
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