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An "adequate" test suite should effectively find all inconsistencies betweena system's requirements/specifications and its implementation. Practitionersfrequently use code coverage to approximate adequacy, while academics arguethat mutation score may better approximate true (oracular) adequacy coverage.High code coverage is increasingly attainable even on large systems viaautomatic test generation, including fuzzing. In light of all of these optionsfor measuring and improving testing effort, how should a QA engineer spendtheir time? We propose a new framework for reasoning about the extent, limits,and nature of a given testing effort based on an idea we call the oracle gap,or the difference between source code coverage and mutation score for a givensoftware element. We conduct (1) a large-scale observational study of theoracle gap across popular Maven projects, (2) a study that varies testing andoracle quality across several of those projects and (3) a small-scaleobservational study of highly critical, well-tested code across comparableblockchain projects. We show that the oracle gap surfaces important informationabout the extent and quality of a test effort beyond either adequacy metricalone. In particular, it provides a way for practitioners to identify sourcefiles where it is likely a weak oracle tests important code.

Mind the Gap: The Difference Between Coverage and Mutation Score Can Guide Testing Efforts