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Mutation-aware fault prediction

Bowes, D; Hall, T; Harman, M; Jia, Y; Sarro, F; Wu, F; (2016) Mutation-aware fault prediction. In: Zeller, A and Roychoudhury, A, (eds.) ISSTA 2016: Proceedings of the 25th International Symposium on Software Testing and Analysis. (pp. pp. 330-341). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ≤ 0.05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.

Type: Proceedings paper
Title: Mutation-aware fault prediction
Event: 25th International Symposium on Software Testing and Analysis (ISSTA 2016)
ISBN-13: 9781450343909
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2931037.2931039
Publisher version: http://dx.doi.org/10.1145/2931037.2931039
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Software Fault Prediction, Software Defect Prediction, Mutation Testing, Software Metrics, Empirical Study
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1508137
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