一句话：BugPecker 通过在修订图上深度学习，利用历史修复细节来定位缺陷方法，显著优于现有方法。
缺陷定位旨在根据缺陷报告找出项目中出错的位置。已有方法在定位缺陷文件上表现良好，但定位到具体缺陷方法仍然困难，因为方法体往往很短、信息不足。本文提出 BugPecker，一种基于修订图（revision graph）深度学习的方法级缺陷定位技术。其核心思想是：(1) 构建修订图，尽可能捕获过往修复的细节信息；(2) 挖掘修订图内部的关系以扩展方法相关细节，并计算多种特征用于排序。作者实现了 BugPecker，并在三个开源项目上进行评估。结果表明，BugPecker 取得 0.263 的平均精度均值（MAP）和 0.291 的平均倒数排名（MRR），显著优于先前方法：例如相比 DNNLoc-m 和 BLIA 1.5 等两种近期方法，其在三个项目上的 MAP 值均提升约五倍。
Given a bug report of a project, the task of locating the faults of the bug report is called fault localization. To help programmers in the fault localization process, many approaches have been proposed, and have achieved promising results to locate faulty files. However, it is still challenging to locate faulty methods, because many methods are short and do not have sufficient details to determine whether they are faulty. In this paper, we present BugPecker, a novel approach to locate faulty methods based on its deep learning on revision graphs. Its key idea includes (1) building revision graphs and capturing the details of past fixes as much as possible, and (2) discovering relations inside our revision graphs to expand the details for methods and calculating various features to assist our ranking. We have implemented BugPecker, and evaluated it on three open source projects. The early results show that BugPecker achieves a mean average precision (MAP) of 0.263 and mean reciprocal rank (MRR) of 0.291, which improve the prior approaches significantly. For example, BugPecker improves the MAP values of all three projects by five times, compared with two recent approaches such as DNNLoc-m and BLIA 1.5.
DOI 原文 ·
@inproceedings{paperbot5979,
title = {BugPecker},
author = {Junming Cao and Shouliang Yang and Wenhui Jiang and Hushuang Zeng and Beijun Shen and Hao Zhong},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3418934}
}