An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming

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@Article{han:2024:AS,

• author = "Tiantian Han and Yonghe Chu and Fangzheng Liu",
• title = "An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming",
• journal = "Applied Sciences",
• year = "2024",
• volume = "14",
• number = "18",
• pages = "Article No. 8550",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/14/18/8550",
• DOI = "doi:10.3390/app14188550",
• abstract = "Due to the explosive growth of software quantity and the mixed ability of software developers, a large number of software defects emerge during the later stages of software maintenance. The search method based on genetic programming is one of the most popular in search algorithms, but it also has some issues. The single-objective approach to validate and select offspring patches without considering other constraints can affect the efficiency of patch generation. To address this issue, this paper proposes an automatic software repair method based on Multi-objective Genetic Programming (MGPRepair). Firstly, the method adopts a lightweight context analysis strategy to find suitable repair materials. Secondly, it decouples the replacement statements and insertion statements in the repair materials, using a lower-granularity patch representation method to encode the patches in the search space. Then, the automatic software defect repair is treated as a multi-objective search problem, and the NSGA-II multi-objective optimisation algorithm is used to find simpler repair patches. Finally, the test case filtering technique is used to accelerate the patch validation process and generate correct patches. MGPRepair was experimentally evaluated on 395 real Java software defects from the Defects4J dataset. The experimental results show that MGPRepair can generate test case-passing patches for 51 defects, of which 35 defect patches are equivalent to manually generated patches. Its repair the efficiency and success rate are higher to other excellent automatic software defect repair methods such as jGenProg, RSRepair, ARJA, Nopol, Capgen, and SequenceR.",
• notes = "also known as \cite{app14188550}",

}

Genetic Programming entries for Tiantian Han Yonghe Chu Fangzheng Liu

Citations