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Mutation testing is a program-transformation technique that injects artificial bugs to check whether the existing test suite can detect them. However, the costs of using mutation testing are usually high, hindering its use in industry. Useless mutants (equivalent and duplicated) contribute to increase costs. An equivalent mutant is a mutant that has the same behavior of the original program. This way, this mutant is useless. A recent discovered kind of mutant is a mutant that has the same behavior of another one. They are duplicated and thus one of them is useless. Previous researches have focused mainly on detecting useless mutants only after they are generated and compiled. In this paper, we introduce a strategy to help developers on deriving rules to avoid the generation of useless mutants. To use our strategy, we pass as input a set of programs. For each program, we also need a passing test suite and a set of mutants. As output, our strategy yields a set of useless mutants candidates. After manually confirming that the mutants classified by our strategy as "useless" are indeed useless, we derive rules that can avoid their generation and thus decrease costs. In particular, to the best of our knowledge, we contribute to 37 new rules that can avoid useless mutants right before their generation. We then implement a subset of these rules in the MUJAVA mutation testing tool. Since our rules have been derived based on artificial and small Java programs, we take our MUJAVA version embedded with our rules and execute it in real practice projects. Our rules reduced the number of mutants by almost 13% on average. Our results are promising because (i) we avoid useless mutants generation; (ii) our strategy can help on identifying more rules in case we set it to use more complex Java programs; and (iii) our MUJAVA version has only a subset of the rules we derived.
Mutation testing is a program-transformation technique that injects artificial bugs to check whether the existing test suite can detect them. However, the costs of using mutation testing are usually high, hindering its use in industry. Useless mutants (equivalent and duplicated) contribute to increase costs. An equivalent mutant is a mutant that has the same behavior of the original program. This way, this mutant is useless. A recent discovered kind of mutant is a mutant that has the same behavior of another one. They are duplicated and thus one of them is useless. Previous researches have focused mainly on detecting useless mutants only after they are generated and compiled. In this paper, we introduce a strategy to help developers on deriving rules to avoid the generation of useless mutants. To use our strategy, we pass as input a set of programs. For each program, we also need a passing test suite and a set of mutants. As output, our strategy yields a set of useless mutants candidates. After manually confirming that the mutants classified by our strategy as "useless" are indeed useless, we derive rules that can avoid their generation and thus decrease costs. In particular, to the best of our knowledge, we contribute to 37 new rules that can avoid useless mutants right before their generation. We then implement a subset of these rules in the MUJAVA mutation testing tool. Since our rules have been derived based on artificial and small Java programs, we take our MUJAVA version embedded with our rules and execute it in real practice projects. Our rules reduced the number of mutants by almost 13% on average. Our results are promising because (i) we avoid useless mutants generation; (ii) our strategy can help on identifying more rules in case we set it to use more complex Java programs; and (iii) our MUJAVA version has only a subset of the rules we derived.
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