Abstract
Equivalent mutants are a major nuisance in mutation testing because they introduce a significant amount of bias. But weeding them out is difficult because it requires a detailed analysis of the source code of the base program and the mutant. In this paper we argue that for most applications, it is not necessary to identify equivalent mutants individually; rather it suffices to estimate their number. Also, we explore how we can estimate their number by a cursory/automatable analysis of the base program and the mutant generation policy.
This paper is an extended version of the paper titled: Impact of Mutation Operators on Mutant Equivalence, by I. Marsit, M. N. Omri, J. M. Loh and A. Mili. Proceedings, ICSOFT 2018, Madrid, Spain, July 2018, pages 55–66. This paper extends the original by justifying the definition of the metrics and by showing the details of the regression model.
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This research is partially supported by a grant to the last author from the (US) National Science Foundation, number DGE 1565478.
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Ayad, A., Marsit, I., Mohamed Omri, N., Loh, J., Mili, A. (2019). Using Semantic Metrics to Predict Mutation Equivalence. In: van Sinderen, M., Maciaszek, L. (eds) Software Technologies. ICSOFT 2018. Communications in Computer and Information Science, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-030-29157-0_1
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