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Diff-based mutation testing is a mutation testing approach that only mutateslines affected by a code change under review. Google's mutation testing serviceintegrates diff-based mutation testing into the code review process andcontinuously gathers developer feedback on mutants surfaced during code review.To enhance the developer experience, the mutation testing service implements anumber of suppression rules, which target not-useful mutants-that is, mutantsthat have consistently received negative developer feedback. However, whileeffective, manually implementing suppression rules require significantengineering time. An automatic system to rank and suppress mutants wouldfacilitate the maintenance of the mutation testing service. This paper proposesand evaluates MuRS, an automated approach that groups mutants by patterns inthe source code under test and uses these patterns to rank and suppress futuremutants based on historical developer feedback on mutants in the same group. Toevaluate MuRS, we conducted an A/B testing study, comparing MuRS to theexisting mutation testing service. Despite the strong baseline, which usesmanually developed suppression rules, the results show a statisticallysignificantly lower negative feedback ratio of 11.45% for MuRS versus 12.41%for the baseline. The results also show that MuRS is able to recover existingsuppression rules implemented in the baseline. Finally, the results show thatstatement-deletion mutant groups received both the most positive and negativedeveloper feedback, suggesting a need for additional context that candistinguish between useful and not-useful mutants in these groups. Overall,MuRS has the potential to substantially reduce the development and maintenancecost for an effective mutation testing service by automatically learningsuppression rules.

MuRS: Mutant Ranking and Suppression using Identifier Templates