This paper presents a novel end-to-end approach to program repair based on
sequence-to-sequence learning. We devise, implement, and evaluate a system,
called SequenceR, for fixing bugs based on sequence-to-sequence learning on
source code. This approach uses the copy mechanism to overcome the unlimited
vocabulary problem that occurs with big code. Our system is data-driven; we
train it on 35,578 samples, carefully curated from commits to open-source
repositories. We evaluate it on 4,711 independent real bug fixes, as well on
the Defects4J benchmark used in program repair research. SequenceR is able to
perfectly predict the fixed line for 950/4711 testing samples, and find correct
patches for 14 bugs in Defects4J. It captures a wide range of repair operators
without any domain-specific top-down design.

本文以序列到序列学习为基础，提出了一种全新的端到端程序修复方法。通过利用复制机制克服大型代码中出现的无限词汇问题，使用数据驱动的 SequenceR 系统在开放源代码上进行了评估和评测，有效地从 Defects4J 基准中找到正确的补丁。