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Empirical Analysis of Mutation Operator Selection Strategies for Genetic Improvement

Smigielska, M; Blot, A; Petke, J; (2021) Empirical Analysis of Mutation Operator Selection Strategies for Genetic Improvement. In: 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). IEEE Green open access

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Abstract

Genetic improvement (GI) tools find improved program versions by modifying the initial program. These can be used for the purpose of automated program repair (APR). GI uses software transformations, called mutation operators, such as deletions, insertions, and replacements of code fragments. Current edit selection strategies, however, under-explore the search spaces of insertion and replacement operators. Therefore, we implement a uniform strategy based on the relative operator search space sizes. We evaluate it on the QuixBugs repair benchmark and find that the uniform strategy has the potential for improving APR tool performance. We also analyse the efficacy of the different mutation operators with regard to the type of code fragment they are applied to. We find that, for all operators, choosing expression statements as target statements is the most successful for finding program variants with improved or preserved fitness (50.03%, 33.12% and 23.85% for deletions, insertions and replacements, respectively), whereas choosing declaration statements is the least effective (3.16%, 10.82% and 3.14% for deletions, insertions and replacements).

Type: Proceedings paper
Title: Empirical Analysis of Mutation Operator Selection Strategies for Genetic Improvement
Event: The 10th International Workshop on Genetic Improvement @ ICSE 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/GI52543.2021.00009
Publisher version: https://doi.org/10.1109/GI52543.2021.00009
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.
UCL classification: UCL
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/10122957
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