The presence of equivalent mutants has long been considered a major obstacle to the widespread adoption of mutation analysis and mutation testing. This paper presents a study on the types and prevalence of equivalent mutants in real-world Java programs. We conducted a ground-truth analysis of 1,992 mutants, sampled from 7 open source Java projects. Our analysis identified 215 equivalent mutants, which we grouped based on two criteria that describe why the mutants are equivalent and how challenging their detection is. From this analysis, we observed that (1) the median equivalent mutant rate across the 7 projects is 2.97%; (2) many equivalent mutants are caused by common programming patterns and their detection is not much more complex than structural pattern matching over an abstract syntax tree. Based on the findings of our ground-truth analysis, we developed Equivalent Mutant Suppression (EMS), a technique that comprises 10 efficient and targeted analyses. We evaluated EMS on 19 open- source Java projects, comparing the effectiveness and efficiency of EMS to two variants of Trivial Compiler Equivalence (TCE), the current state of the art in equivalent mutant detection. Additionally, we analyzed all 9,047 equivalent mutants reported by any tool to better understand the types and frequencies of equivalent mutants found. Overall, EMS detects 8,776 equivalent mutants within 325 seconds; TCE detects 2,124 equivalent mutants in 2,938 hours.

A version control system, such as Git, requires a way to integrate changes from different developers or branches. Given a merge scenario, a merge tool either outputs a clean integration of the changes, or it outputs a conflict for manual resolution. A clean integration is correct if it preserves intended program behavior, and is incorrect otherwise (e.g., if it causes a test failure). Manual resolution consumes valuable developer time, and correcting a defect introduced by an incorrect merge is even more costly.

Mutation testing offers concrete test goals (mutants) and a rigorous test efficacy criterion, but it is expensive due to vast numbers of mutants, many of which are neither useful nor actionable. Prior work has focused on selecting representative and sufficient mutant subsets, measuring whether a test set that is mutation-adequate for the subset is equally adequate for the entire set. However, no known industrial application of mutation testing uses or even computes mutation adequacy, instead focusing on iteratively presenting very few mutants as concrete test goals for developers to write tests.