During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Models (LLMs) have been shown to be helpful "copilots" in assisting developers with various coding tasks, and have also been directly applied for patch synthesis. However, most LLMs treat programs as sequences of tokens, meaning that they are ignorant of the underlying semantics constraints of the target programming language. This results in plenty of statically invalid generated patches, impeding the practicality of the technique. Therefore, we propose Repilot, a framework to further copilot the AI "copilots" (i.e., LLMs) by synthesizing more valid patches during the repair process. Our key insight is that many LLMs produce outputs autoregressively (i.e., token by token), resembling human writing programs, which can be significantly boosted and guided through a Completion Engine. Repilot synergistically synthesizes a candidate patch through the interaction between an LLM and a Completion Engine, which 1) prunes away infeasible tokens suggested by the LLM and 2) proactively completes the token based on the suggestions provided by the Completion Engine. Our evaluation on a subset of the widely-used Defects4j 1.2 and 2.0 datasets shows that Repilot fixes 66 and 50 bugs, respectively, surpassing the best-performing baseline by 14 and 16 bugs fixed. More importantly, Repilot is capable of producing more valid and correct patches than the base LLM when given the same generation budget.

翻译：在自动程序修复（APR）过程中，为通用编程语言中的真实系统合成正确补丁可能极具挑战性。近年来，大型语言模型（LLM）已被证明是协助开发者完成各类编码任务的有效“副驾驶”，并已直接应用于补丁合成。然而，大多数LLM将程序视为令牌序列，这意味着它们忽视了目标编程语言的基础语义约束。这导致了大量生成的补丁在静态上无效，阻碍了该技术的实用性。因此，我们提出Repilot框架，通过修复过程中合成更多有效补丁，进一步为AI“副驾驶”（即LLM）提供辅助。我们的核心洞见在于，许多LLM以自回归方式（即逐令牌）生成输出，这与人类编写程序类似，而通过补全引擎可以显著增强和引导这一过程。Repilot通过LLM与补全引擎的协同交互来合成候选补丁：1）剪除LLM建议的不可行令牌；2）基于补全引擎提供的建议主动完成令牌。我们在广泛使用的Defects4j 1.2和2.0数据集的子集上进行的评估表明，Repilot分别修复了66个和50个缺陷，超越了性能最佳的基线方法14个和16个缺陷。更重要的是，在给定相同生成预算的情况下，Repilot相比基础LLM能生成更多有效且正确的补丁。