In recent years, large language models (LLMs) have shown potential in automatic program repair (APR) tasks. The study focuses on repository-level context for bug fixes. RepoBugs benchmark reveals the importance of precise context for LLMs. RLCE method enhances LLMs' ability to handle repository-level bugs by 160%. Different prompt strategies impact model performance. Explanation accuracy is crucial for CoT strategy.
The study compares different models and methods using a variety of evaluation metrics. RLCE outperforms baselines like preliminary and slice-similarity methods. Context sources like definitions of EIF play a vital role in repair accuracy. Error types like ORV and CRV pose challenges for LLMs. Prompt length affects repair accuracy, with longer prompts not always leading to better results.
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by Yuxiao Chen,... às arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00448.pdfPerguntas Mais Profundas