Recently, multiple Automated Program Repair (APR) techniques based on Large Language Models (LLMs) have been proposed to enhance the repair performance. While these techniques mainly focus on the single-line or hunk-level repair, they face significant challenges in real-world application due to the limited repair task scope and costly statement-level fault localization. However, the more practical function-level APR, which broadens the scope of APR task to fix entire buggy functions and requires only cost-efficient function-level fault localization, remains underexplored. In this paper, we conduct the first comprehensive study of LLM-based function-level APR including investigating the effect of the few-shot learning mechanism and the auxiliary repair-relevant information. Specifically, we adopt six widely-studied LLMs and construct a benchmark in both the Defects4J 1.2 and 2.0 datasets. Our study demonstrates that LLMs with zero-shot learning are already powerful function-level APR techniques, while applying the few-shot learning mechanism leads to disparate repair performance. Moreover, we find that directly applying the auxiliary repair-relevant information to LLMs significantly increases function-level repair performance. Inspired by our findings, we propose an LLM-based function-level APR technique, namely SRepair, which adopts a dual-LLM framework to leverage the power of the auxiliary repair-relevant information for advancing the repair performance. The evaluation results demonstrate that SRepair can correctly fix 300 single-function bugs in the Defects4J dataset, largely surpassing all previous APR techniques by at least 85%, without the need for the costly statement-level fault location information. Furthermore, SRepair successfully fixes 32 multi-function bugs in the Defects4J dataset, which is the first time achieved by any APR technique ever to our best knowledge.

翻译：近年来，多种基于大语言模型（LLM）的自动程序修复（APR）技术被提出以提升修复性能。尽管这些技术主要关注单行或代码块级别的修复，但由于修复任务范围有限且语句级故障定位成本高昂，它们在现实应用中面临重大挑战。然而，更具实用性的函数级APR——将APR任务范围扩展至修复整个缺陷函数，且仅需成本低廉的函数级故障定位——仍未得到充分探索。本文首次对基于LLM的函数级APR进行了全面研究，包括探究少样本学习机制与辅助修复相关信息的影响。具体而言，我们采用六个广泛研究的LLM，并在Defects4J 1.2和2.0数据集中构建了基准测试。研究表明，采用零样本学习的LLM本身已是强大的函数级APR技术，而应用少样本学习机制则会导致修复性能出现显著差异。此外，我们发现直接将辅助修复相关信息输入LLM能显著提升函数级修复性能。基于这些发现，我们提出了一种基于LLM的函数级APR技术——SRepair，该技术采用双LLM框架以利用辅助修复相关信息提升修复性能。评估结果表明，SRepair能在Defects4J数据集中正确修复300个单函数缺陷，较以往所有APR技术至少提升85%，且无需昂贵的语句级故障定位信息。此外，SRepair成功修复了Defects4J数据集中的32个多函数缺陷，据我们所知，这是所有APR技术首次实现该突破。