一句话：通过复现性基准研究评估基于信息检索的缺陷定位方法的真实性能，揭示现有评估的不足。
近年来，利用信息检索技术根据缺陷报告自动定位缺陷文件已取得有希望的结果。然而文献中方法众多，却与开发者乃至研究社区实际采用 IR-based bug localization 的现实形成反差。论文认为，造成这一反差的原因之一在于缺乏对最先进方法的全面评估，无法提供关于这些技术实际性能的洞见。为此，Bench4BL 开展复现性研究，围绕基于 IR 的缺陷定位方法的性能进行基准化评估，系统考察其真实表现，以弥补现有评估覆盖不足的问题。
In recent years, the use of Information Retrieval (IR) techniques to automate the localization of buggy files, given a bug report, has shown promising results. The abundance of approaches in the literature, however, contrasts with the reality of IR-based bug localization (IRBL) adoption by developers (or even by the research community to complement other research approaches). Presumably, this situation is due to the lack of comprehensive evaluations for state-of-the-art approaches which offer insights into the actual performance of the techniques.
DOI 原文 ·
@inproceedings{paperbot4839,
title = {Bench4BL: reproducibility study on the performance of IR-based bug localization},
author = {Jaekwon Lee and Dongsun Kim and Tegawendé F. Bissyandé and Woosung Jung and Yves Le Traon},
booktitle = {ISSTA},
year = {2018},
doi = {10.1145/3213846.3213856}
}