Avoiding Useless Mutants
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. Previous research has focused mainly on detecting useless mutants only after they are generated and compiled. In this paper, we introduce a strategy to help developers with 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. To the best of our knowledge, we introduce 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 industrial-scale 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 with 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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Conference DayTue 24 OctDisplayed time zone: Tijuana, Baja California change
13:30 - 15:00
|Four Languages and Lots of Macros: Analyzing Autotools Build Systems|
Jafar M. Al-KofahiIowa State University, USA, Suresh KothariIowa State University, USA, Christian KästnerCarnegie Mellon University, USADOI Authorizer link
|Avoiding Useless Mutants|
Leonardo FernandesFederal University of Pernambuco, Brazil, Márcio RibeiroFederal University of Alagoas, Brazil, Luiz CarvalhoFederal University of Alagoas, Brazil, Rohit GheyiFederal University of Campina Grande, Brazil, Melina MongioviFederal University of Campina Grande, Brazil, Andre Santos, Ana CavalcantiUniversity of York, UK, Fabiano FerrariFederal University of São Carlos, Brazil, José Carlos MaldonadoUniversity of São Paulo, BrazilDOI Authorizer link
|Silverchain: A Fluent API Generator|
Tomoki NakamaruUniversity of Tokyo, Japan, Kazuhiro IchikawaUniversity of Tokyo, Japan, Tetsuro YamazakiUniversity of Tokyo, Japan, Shigeru ChibaUniversity of Tokyo, JapanDOI Authorizer link
|Parser Generation by Example for Legacy Pattern LanguagesShort paper|
Vadim ZaytsevRaincode Labs, BelgiumDOI Authorizer link