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Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies

Guizzo, G; Sarro, F; Krinke, J; Vergilio, SR; (2020) Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies. IEEE Transactions on Software Engineering 10.1109/TSE.2020.3002496. (In press). Green open access

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Abstract

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark for a total of 4,800 experiments, which results are evaluated with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes, and they also obtain statistically significantly better results than state-of-the-art strategies in 88% of the cases with large effect sizes for 95% of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95% of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.

Type: Article
Title: Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TSE.2020.3002496
Publisher version: https://doi.org/10.1109/TSE.2020.3002496
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: Mutation Testing, Mutant Reduction, Software Testing, Grammatical Evolution, Hyper-Heuristic, Search Based Software Testing, Search Based Software Engineering
UCL classification: UCL
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/10100736
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