On the Role of Context Granularity in LLM-Driven Program Repair

Recent advances in Large Language Models (LLMs) have created new opportunities for scalable Automated Program Repair (APR). However, an underexplored aspect of APR is the impact of the surrounding code on patch correctness. We propose a novel context granularity based on backward static slicing, capturing lines on which the buggy line is data- and control-dependent. We then evaluate its performance against five commonly used APR context granularities as well as state-of-the-art APR systems. Using GPT-4, we assess all six context granularities on 109 single-line bugs from the Defects4J dataset. Our results show that the sliced context achieves the highest Correct/Plausible ratio (79%) in the dataset, suggesting that a more focused context improves the generation of semantically accurate patches per passed test case. In contrast, larger contexts, such as entire files, produce more correct patches overall but at a lower ratio. We propose that future work explore combining smaller, focused contexts like slicing with larger ones to enhance both semantic accuracy and the total number of correct patches, as well as investigating context granularity strategies tailored to specific bug types.