Dissecting copy/delete/replace/swap mutations
Sherlock A. Licorish, Markus Wagner · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
Research studies are increasingly critical of publicly available code due to evidence of faults. This has led researchers to explore ways to improve such code, with static analysis and genetic code improvement previously singled out. Previous work has evaluated the feasibility of these techniques, using PMD (a static analysis tool) and GIN (a program repair tool) for enhancing Stack Overflow Java code snippets. Results reported in this regard pointed to the potential of these techniques, especially in terms of GIN's removal of PMD's performance faults from 58 programs. We use a contextual lens to explore these mutations in this study, to evaluate the promise of these techniques. The outcomes show that while the programs were syntactically correct after GIN's mutations (i.e., they compiled), many of GIN's mutations changed the semantics of the code, rendering its purpose questionable. However, certain code mutations tend to retain code semantics more than others. In addition, GIN's mutations at times affected PMD's parsing ability, potentially increasing false negatives. Overall, while these approaches may prove useful, full utility may not be claimed at this time. For enhancing the outcomes of these approaches, we outline ways to improve the utility of these techniques and multiple future research directions.