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 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 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, and users can also deploy and use IncBL locally. 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

中文翻译：

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IncBL：增量错误定位

已经投入了大量努力来提高错误定位技术的有效性，而很少关注使这些工具在不断发展的软件存储库中更有效地运行。本文首先分析了经典错误定位工具 BugLocator 背后的信息检索模型，并建立了一个数学基础，即当代码库或错误报告演变时，该模型可以增量更新。然后，我们介绍了 IncBL，这是一种在不断发展的软件存储库中进行增量错误定位的工具。IncBL 在 Bugzbook 数据集上进行评估，结果表明，与重新计算模型相比，IncBL 可以显着减少 77.79% 的平均运行时间，同时保持相同的精度水平。我们还将 IncBL 实现为 Github App，可以轻松集成到 Github 上的开源项目中，用户也可以在本地部署和使用 IncBL。IncBL的演示视频可以在https://youtu.be/G4gMuvlJSb0查看，源代码可以在https://github.com/soarsmu/IncBL找到