An Enhanced Patch Optimization Technique for Multi-Chunk Bugs in Automated Program Repair

Automated program repair techniques leveraging deep learning have shown remarkable performances in bug repair. These techniques commonly employ pre-trained neural machine translation (NMT) models to generate patches for a buggy part of the source code. However, when dealing with multiple buggy code chunks in various locations, current methods face challenges in effectively selecting and combining these patches for optimal repair. This paper identifies limitations within one of the existing methods used for optimizing patches related to multiple buggy code chunks and proposes an enhanced patch optimization technique to address these shortcomings. The primary aim of this study is to improve the process of selecting and combining patches generated for groups of buggy chunks. Through experiments conducted on a dataset, this paper demonstrates the efficacy of the proposed patch optimization technique, showcasing its potential to enhance the overall bug repair process. This study highlights the importance of patch optimization in bug repair by addressing limitations and enhancing the repair process.