标题： 利用大型语言模型上的双检索增强微调和补丁生成加速自动程序修复 显示英文标题

标题： Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models

摘要： 自动化程序修复（APR）对于确保软件的可靠性与质量至关重要，同时可以提高效率并减轻开发人员的工作负担。 尽管基于规则和基于学习的APR方法已展示了其有效性，但它们的性能受到修复缺陷类型、训练数据质量和模型参数大小的限制。 最近，结合检索增强生成（RAG）的大语言模型（LLMs）在APR任务中被越来越多地采用。 然而，当前的代码LLMs和RAG设计既不能充分解决代码修复任务，也未考虑代码特定特征。 为了克服这些限制，我们提出了SelRepair，这是一种新颖的APR方法，结合了微调的LLM与新设计的双RAG模块。 该方法使用错误修复对数据集进行微调，并通过RAG选择门整合语义和语法/结构相似性信息。 这种设计确保相关信息能够高效检索，从而减少标记长度和推理时间。 在Java数据集上的评估表明，SelRepair优于其他APR方法，在不同数据集上精确匹配（EM）分别达到26.29%和17.64%，同时在控制输入长度的情况下，推理时间至少减少了6.42%。 显示英文摘要

摘要： Automated Program Repair (APR) is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of repair, the quality of training data, and the size of model parameters. Recently, Large Language Models (LLMs) combined with Retrieval-Augmented-Generation (RAG) have been increasingly adopted in APR tasks. However, current code LLMs and RAG designs neither fully address code repair tasks nor consider code-specific features. To overcome these limitations, we propose SelRepair, a novel APR approach with integration of a fine-tuned LLM with a newly-designed dual RAG module. This approach uses a bug-fix pair dataset for fine-tuning and incorporates semantic and syntactic/structural similarity information through an RAG selection gate. This design ensures relevant information is retrieved efficiently, thereby reducing token length and inference time. Evaluations on Java datasets show SelRepair outperforms other APR methods, achieving 26.29% and 17.64% in terms of exact match (EM) on different datasets while reducing inference time by at least 6.42% with controlled input lengths.