一句话：提出增量式缺陷定位工具 IncBL，通过增量更新检索模型，在保持精度的同时平均减少 77.79% 的运行时间。
论文关注持续演化的软件仓库中缺陷定位工具的效率问题：当代码库或缺陷报告更新时，传统工具往往需要重新计算整个检索模型，开销较大。作者首先剖析了经典缺陷定位工具 BugLocator 背后的信息检索模型，建立数学基础，证明该模型在代码库或缺陷报告演化时可以增量式更新。基于此实现了增量缺陷定位工具 IncBL，并在 Bugzbook 数据集上评估。结果显示，与重新计算模型相比，IncBL 平均减少 77.79% 的运行时间，同时保持相同的准确率。作者还将 IncBL 实现为可集成到 GitHub 开源项目的 GitHub App，并支持本地部署。
Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on average compared with the re-computing the model, while maintaining the same level of accuracy. We also implement IncBL as a Github App that can be easily integrated into open-source projects on GitHub. Users can deploy and use IncBL locally as well. The demo video for IncBL can be viewed at https://youtu.be/G4gMuvlJSb0, and the source code can be found at https://github.com/soarsmu/IncBL.
DOI 原文 ·
@inproceedings{paperbot6633,
title = {IncBL: Incremental Bug Localization},
author = {Zhou Yang and Jieke Shi and Shaowei Wang and David Lo},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678546}
}