一句话：通过 11 个 Java 测试套件驱动修复工具在 5 个基准、2,141 个缺陷上的大规模实验，揭示修复能力难以跨基准泛化。
过去十年，基于测试套件的自动程序修复研究增长迅速，但多数方法仅在单一基准上评估，结果也鲜有他人复现。本文开展大规模实证实验，使用 11 个 Java 测试套件驱动的修复工具，覆盖 5 个基准的 2,141 个缺陷，共执行 23,551 次修复尝试，以检验“修复工具的可修复性未必能跨基准泛化”这一假设。结果表明：11 个工具仅能为 21% 的缺陷生成补丁；且在 Defects4J 上表现明显更好，可修复 47% 的缺陷，而在其他基准上仅为 10%–30%。论文还分析了未能生成补丁的原因，可为修复工具设计者改进方法与工具提供依据。
In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 2,141 bugs from 5 benchmarks. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts, which we used to find causes of non-patch generation. These causes are reported in this paper, which can help repair tool designers to improve their approaches and tools.
DOI 原文 ·
@inproceedings{paperbot5060,
title = {Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts},
author = {Thomas Durieux and Fernanda Madeiral and Matias Martinez and Rui Abreu},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338911}
}