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    Accepted author manuscript, 551 KB, PDF-document

DOI

Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, its scalability issue remains in practice. In this paper, we introduce a novel method to speed up mutation testing based on state infection information. In addition to filtering out uninfected test executions, we further select a subset of mutants and a subset of test cases to run leveraging data-compression techniques. In particular, we adopt Formal Concept Analysis (FCA) to group similar mutants together and then select test cases to cover these mutants. To evaluate our method, we conducted an experimental study on six open source Java projects. We used EvoSuite to automatically generate test cases and to collect mutation data. The initial results show that our method can reduce the execution time by 83.93% with only 0.257% loss in precision.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages103-109
Number of pages7
ISBN (Electronic)9781509066766
DOIs
Publication statusPublished - 13 Apr 2017
Event10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017 - Tokyo, Japan
Duration: 13 Mar 201717 Mar 2017

Conference

Conference10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017
CountryJapan
CityTokyo
Period13/03/1717/03/17

    Research areas

  • Cost reduction, Data compression, Mutation testing, State infection

ID: 45811966