Optimizing Mutation Testing by Discovering Dynamic Mutant Subsumption Relations

One recent promising direction on reducing costs of mutation analysis is to identify redundant mutations, i.e., mutations that are subsumed by some other mutations. Previous works found out redundant mutants manually through the truth table. Although the idea is promising, it can only be applied for logical and relational operators. In this paper, we propose an approach to discover redundancy in mutations through dynamic subsumption relations among mutants. We focus on subsumption relations among mutations of an expression or statement, named here as "mutation target." By focusing on targets and relying on automatic test generation tools, we define subsumption relations for dozens of mutation targets in which the MUJAVA tool can apply mutations. We then implemented these relations in a tool, named MUJAVA-M, that generates a reduced set of mutants for each target, avoiding redundant mutants. We evaluated MUJAVA and MUJAVA- M using classes of five open-source projects. As results, we analyze 2,341 occurrences of 32 mutation targets in 168 classes. MUJAVA-M generates less mutants (on average 64.43% less) with 100% of effectiveness in 20 out of 32 targets and more than 95% in 29 out of 32 mutation targets. MUJAVA-M also reduced the time to execute the test suites against the mutants in 52.53% on average, considering the full mutation analysis process.