Automatic program repair (APR) is crucial to reduce manual debugging efforts
for developers and improve software reliability. While conventional
search-based techniques typically rely on heuristic rules or a redundancy
assumption to mine fix patterns, recent years have witnessed the surge of deep
learning (DL) based approaches to automate the program repair process in a
data-driven manner. However, their performance is often limited by a fixed set
of parameters to model the highly complex search space of APR. To ease such
burden on the parametric models, in this work, we propose a novel
Retrieval-Augmented Patch Generation framework (RAP-Gen) by explicitly
leveraging relevant fix patterns retrieved from a codebase of previous bug-fix
pairs. Specifically, we build a hybrid patch retriever to account for both
lexical and semantic matching based on the raw source code in a
language-agnostic manner, which does not rely on any code-specific features. In
addition, we adapt a code-aware language model CodeT5 as our foundation model
to facilitate both patch retrieval and generation tasks in a unified manner. We
adopt a stage-wise approach where the patch retriever first retrieves a
relevant external bug-fix pair to augment the buggy input for the CodeT5 patch
generator, which synthesizes a ranked list of repair patch candidates. Notably,
RAP-Gen is a generic APR framework that can flexibly integrate different patch
retrievers and generators to repair various types of bugs. We thoroughly
evaluate RAP-Gen on three benchmarks in two programming languages, including
the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks
in Java, where the bug localization information may or may not be provided.
Experimental results show that RAP-Gen significantly outperforms previous
state-of-the-art approaches on all benchmarks, e.g., repairing 15 more bugs on
818 Defects4J bugs.

自动程序修复是减少开发人员手动调试努力并提高软件可靠性的关键。本文提出了一种新颖的检索增强型修补生成框架（RAP-Gen），通过明确利用从以前的错误修复对的代码库中检索出的相关修复模式来简化参数模型的负担，进而自动化程序修复过程。实验结果表明，RAP-Gen 在所有基准测试中显著优于先前的最先进方法，例如在 818 个 Defects4J 错误上修复了 15 个更多的错误。