Empirical Analysis of Mutation Operator Selection Strategies for Genetic Improvement

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)