一句话：提出 AccPR，用代码表示学习度量相似度并自适应过滤补丁，加速基于冗余的自动程序修复。
自动程序修复（APR）中，基于冗余的技术虽取得较好效果，但因代码相似度度量不准确而效率低下：会产生大量无意义补丁，阻碍正确补丁的生成与验证。本文提出 AccPR，利用代码表示学习来更准确地度量代码相似度，并采用自适应补丁过滤策略加速基于冗余的 APR。作者实现了 AccPR 原型，并将其集成到当前最优 APR 工具 SimFix 中。评估表明，平均效率提升 47.85%，验证了该方法的有效性。
Automated program repair (APR) has attracted extensive attention and many APR techniques have been proposed recently, in which redundancy-based techniques have achieved great success. However, they still suffer from the efficiency issue mainly caused by the inaccuracy of measuring code similarity, which may produce meaningless patches that hinder the generation and validation of correct patches. To solve this issue, we propose a novel method AccPR, which leverages code representation to measure code similarity and employs adaptive patch filtering to accelerate redundancy-based APR. We have implemented a prototype of AccPR and integrated it with a SOTA APR tool, SimFix, where the average improvement of efficiency is 47.85%, indicating AccPR is promising.
DOI 原文 ·
@inproceedings{paperbot6438,
title = {Accelerating redundancy-based program repair via code representation learning and adaptive patch filtering},
author = {Chen Yang},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473496}
}