Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from existing patches and code corpora. While promising, DL-based APR techniques suffer from the abundant syntactically or semantically incorrect patches in the patch space. These patches often disobey the syntactic and semantic domain knowledge of source code and thus cannot be the correct patches to fix a bug. We propose a DL-based APR approach KNOD, which incorporates domain knowledge to guide patch generation in a direct and comprehensive way. KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates Abstract Syntax Trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases. We evaluate KNOD on three widely-used benchmarks. KNOD fixes 72 bugs on the Defects4J v1.2, 25 bugs on the QuixBugs, and 50 bugs on the additional Defects4J v2.0 benchmarks, outperforming all existing APR tools.

翻译：摘要：自动化程序修复（APR）通过自动为缺陷程序生成补丁来提升软件可靠性。近期APR技术利用深度学习（DL）构建模型，从现有补丁和代码语料库中学习补丁生成。尽管前景可观，但基于DL的APR技术在补丁空间中面临大量语法或语义错误补丁的难题。这些补丁通常违反源代码的语法与语义领域知识，因而无法作为修正缺陷的正确补丁。我们提出基于DL的APR方法KNOD，以直接且全面的方式融合领域知识以指导补丁生成。KNOD包含两大创新点：（1）一种新颖的三阶段树解码器，能依据固有树结构直接生成修补代码的抽象语法树；（2）一种新颖的领域规则蒸馏方法，利用语法与语义规则以及师生分布，在训练与推理阶段将领域知识显式注入解码过程。我们在三个广泛使用的基准集上评估KNOD。KNOD在Defects4J v1.2修复72个缺陷、在QuixBugs修复25个缺陷、在附加Defects4J v2.0基准集修复50个缺陷，性能超越所有现有APR工具。