一句话：在阿里巴巴工业环境中实证比较工作量感知的即时缺陷识别方法，填补工业评估空白。
工作量感知的即时（JIT）缺陷识别旨在以有限的代码审查工作量，及时识别引入缺陷的变更，相比传统模块级缺陷识别更具成本效益和效率。研究者已提出多种有监督（如 CBS+、OneWay）和无监督（如 LT、Code Churn）方法，并对其有效性比较产生了大量研究兴趣。然而，这些近期方法在工业环境中的有效性及相互比较尚未被研究。本文在阿里巴巴开展案例研究，以填补这一空白，为工作量感知 JIT 缺陷识别方法在实际工业场景中的表现评估提供实证依据。
Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting.
DOI 原文 ·
@inproceedings{paperbot5782,
title = {Effort-aware just-in-time defect identification in practice: a case study at Alibaba},
author = {Meng Yan and Xin Xia and Yuanrui Fan and David Lo and Ahmed E. Hassan and Xindong Zhang},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3417048}
}