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Code coverage is often treated as a scorecard for test quality, but its real strength lies elsewhere. Coverage is most useful when viewed as a signal of change-related risk rather than as a direct measure of how well the system is tested.

When coverage suddenly drops after a commit, it usually indicates newly introduced paths that are not exercised by existing tests. When it spikes unnaturally, it can signal superficial tests added only to satisfy thresholds. In both cases, the coverage trend reveals more about the nature of change than about test completeness.

Stable coverage over time tends to correlate with stable behavior. When teams refactor aggressively without updating tests, coverage fluctuations often expose fragile areas in the codebase. These areas deserve deeper inspection—not because coverage is low, but because behavior may no longer be well protected.

Threshold-based enforcement can also distort intent. When teams aim to “meet coverage,” tests are written to execute code rather than validate outcomes. Coverage still increases, yet the system becomes more vulnerable to logical regressions. Treating coverage as an informational metric instead of a gate helps teams focus on meaningful test improvements.

Used thoughtfully, code coverage highlights where change introduces uncertainty. It helps reviewers ask better questions during pull requests and guides regression strategy without pretending to define quality. Coverage doesn’t tell teams whether the code is correct—it tells them where correctness is least certain.