Enhancing Automated Program Repair using Patch Lightweighting and Context Information

Eunseo Jung, Abdinabiev Aslan Safarovich, Byungjeong Lee

http://doi.org/10.5626/JOK.2025.52.8.670

Large Language Models LLMs play a crucial role in the Automated Program RepairAPR field. However, their effectiveness is constrained by token limitations. When the number of tokens exceeds the model’s capacity, it struggles to fully utilize its capabilities, often failing to correctly detect and fix bugs. This study proposed an approach that could leverage patch lightweighting and context information to overcome these constraints. By incorporating the most semantically similar method as a context method and applying patch lightweighting to long methods, we ensured that the methods remained within the LLM’s token limit. Through this approach, experimental results demonstrated that effective bug fixing could be achieved with fewer tokens, improving repair efficiency.