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DOI

Mutation testing is widely considered as a high-end test coverage criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, in practice, the scalability issue remains. In this paper, we explore whether we can use compression techniques to improve the efficiency of strong mutation based on weak mutation information. Our investigation is centred around six mutation compression strategies that we have devised. More specifically, we adopt overlapped grouping and Formal Concept Analysis (FCA) to cluster mutants and test cases based on the reachability (code coverage) and necessity (weak mutation) conditions. Moreover, we leverage mutation knowledge (mutation locations and mutation operator types) during compression. To evaluate our method, we conducted a study on 20 open source Java projects using manually written tests. We also compare our method with pure random sampling and weak mutation. The overall results show that mutant compression techniques are a better choice than random sampling and weak mutation in practice: they can effectively speed up strong mutation 6.3 to 94.3 times with an accuracy of >90%.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Software Testing, Verification, and Validation (ICST)
PublisherIEEE
Pages274-284
ISBN (Electronic)978-1-5386-5012-7
DOIs
Publication statusPublished - 2018
Event11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018 - Vasteras, Sweden
Duration: 9 Apr 201813 Apr 2018

Conference

Conference11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018
CountrySweden
CityVasteras
Period9/04/1813/04/18

ID: 45542574