一句话：提出结合已有补丁与相似代码来塑造自动程序修复搜索空间的方法。
本文关注自动程序修复（APR）常被建模为在全体候选补丁构成的搜索空间中寻找正确补丁的问题。已有工作采用数据驱动方法，利用已有补丁或相似源代码等数据源缩小或引导搜索，从而提升找到正确补丁的概率。然而，已有补丁与相似代码提供的信息具有互补性，现有技术通常只分析其中单一数据源，且难以直接扩展到同时分析两类数据。本文提出用已有补丁与相似代码共同塑造修复空间的方法，旨在整合这两类互补信息来约束和改进补丁搜索。摘要未给出具体技术细节、实验设置或结果。
Automated program repair (APR) has great potential to reduce bug-fixing effort and many approaches have been proposed in recent years. APRs are often treated as a search problem where the search space consists of all the possible patches and the goal is to identify the correct patch in the space. Many techniques take a data-driven approach and analyze data sources such as existing patches and similar source code to help identify the correct patch. However, while existing patches and similar code provide complementary information, existing techniques analyze only a single source and cannot be easily extended to analyze both.
DOI 原文 ·
@inproceedings{paperbot4836,
title = {Shaping program repair space with existing patches and similar code},
author = {Jiajun Jiang and Yingfei Xiong and Hongyu Zhang and Qing Gao and Xiangqun Chen},
booktitle = {ISSTA},
year = {2018},
doi = {10.1145/3213846.3213871}
}