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A Comparative Study of C Program Mutation Tools for Effective Mutation Analysis: A Case Study of Proteum and Milu

Yunho Kim, Hyunwoo Kim, Woong-gyu Yang, Moonzoo Kim

http://doi.org/10.5626/JOK.2018.45.4.342

Mutation analysis generates mutants of a target program by applying syntactic changes to the source code and analyzes the difference of execution results of the mutants from those of the original program. For effective mutation analysis, mutant generation tools should be able to generate effective program mutants. For example, a mutant that is semantically equivalent to the original program or another mutant is not an effective mutant, because it does not generate an execution result different from that of the original program or another existing mutant. This paper presents a comparative study of two mutant generation tools for C programs, Proteum and Milu. To generate effective mutants effectively, we generated a canonical form of mutated expressions and removed duplicated mutants that have the same canonical form as that of other mutants. We applied Proteum and Milu to four Linux/Unix utilities in the SIR benchmark and showed that 48.7% and 46.4% of mutants generated by Proteum and Milu were effective mutants on average, respectively.

Mutagen4J : Effective Mutant Generation Tool for Java Programs

Yiru Jeon, Yunho Kim, Shin Hong, Moonzoo Kim

http://doi.org/

Mutation analysis (or software mutation analysis) generates variants of a target program by injecting systematic code changes to the target program, and utilizes the variants to analyze the target program behaviors. Effective mutation analyses require adequate mutation operators that generate diverse variants for use in the analysis. However, the current mutation analysis tools for Java programs have limitations, since they support only limited types of mutation operators and do not support recent language features such as Java8. In this study, we present Mutagen4J, a new mutant generation tool for Java programs. Mutagen4J additionally supports mutation operators recently shown to generate various mutants and fully supports recent Java language features. The experimental results show that Mutagen4J generates useful mutants for analyses 2.3 times more than the existing Java mutation tools used for the study.


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