| Reference : An Investigation of Compression Techniques to Speed up Mutation Testing |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| Security, Reliability and Trust | |||
| http://hdl.handle.net/10993/33866 | |||
| An Investigation of Compression Techniques to Speed up Mutation Testing | |
| English | |
| Zhu [Delft University of Technology] | |
Panichella, Annibale  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Zaidman, Andy [Delft University of Technology] | |
| 2018 | |
| Proceedings of 11th IEEE Conference on Software Testing, Validation and Verification, 2018 | |
| Yes | |
| No | |
| International | |
| 11th IEEE Conference on Software Testing, Validation and Verification | |
| from 09/04/2018 – 13/04/2018 | |
| Västerås | |
| Sweden | |
| [en] Mutation Testing ; Data Compression ; Software Testing | |
| [en] 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 covergae) 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%. | |
| Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab) | |
| Researchers ; Professionals ; Students ; Others | |
| http://hdl.handle.net/10993/33866 |
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