Gong, Dunwei and Zhang, Gongjie and Yao, Xiangjuan and Meng, Fanlin (2017) Mutant reduction based on dominance relation for weak mutation testing. Information and Software Technology, 81. pp. 82-96. DOI https://doi.org/10.1016/j.infsof.2016.05.001
Gong, Dunwei and Zhang, Gongjie and Yao, Xiangjuan and Meng, Fanlin (2017) Mutant reduction based on dominance relation for weak mutation testing. Information and Software Technology, 81. pp. 82-96. DOI https://doi.org/10.1016/j.infsof.2016.05.001
Gong, Dunwei and Zhang, Gongjie and Yao, Xiangjuan and Meng, Fanlin (2017) Mutant reduction based on dominance relation for weak mutation testing. Information and Software Technology, 81. pp. 82-96. DOI https://doi.org/10.1016/j.infsof.2016.05.001
Abstract
Context: As a fault-based testing technique, mutation testing is effective at evaluating the quality of existing test suites. However, a large number of mutants result in the high computational cost in mutation testing. As a result, mutant reduction is of great importance to improve the efficiency of mutation testing. Objective: We aim to reduce mutants for weak mutation testing based on the dominance relation between mutant branches. Method: In our method, a new program is formed by inserting mutant branches into the original program. By analyzing the dominance relation between mutant branches in the new program, the non-dominated one is obtained, and the mutant corresponding to the non-dominated mutant branch is the mutant after reduction. Results: The proposed method is applied to test ten benchmark programs and six classes from open-source projects. The experimental results show that our method reduces over 80% mutants on average, which greatly improves the efficiency of mutation testing. Conclusion: We conclude that dominance relation between mutant branches is very important and useful in reducing mutants for mutation testing.
Item Type: | Article |
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Uncontrolled Keywords: | Software testing; Weak mutation testing; Mutant; Reduction; Dominance relation |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Nov 2020 13:29 |
Last Modified: | 30 Oct 2024 17:30 |
URI: | http://repository.essex.ac.uk/id/eprint/29039 |
Available files
Filename: 20092.pdf