IncBL：增量错误定位
IncBL: Incremental Bug Localization
摘要：
大量的努力已经投入到提高缺陷定位技术的有效性，而很少有人注意到，使这些工具在不断发展的软件库中更有效地运行。本文首先分析了一个经典的缺陷定位工具Buggram背后的信息检索模型，并建立了一个数学基础，说明该模型可以随着代码库或缺陷报告的发展而增量式更新。然后，我们提出了IncBL，一个工具，增量错误定位在不断发展的软件仓库。在Bugzbook数据集上对IncBL进行了评估，结果表明，与重新计算模型相比，IncBL可以显著减少平均77.79%的运行时间，同时保持相同的精度水平。我们还将IncBL实现为Github应用程序，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上查看，源代码可以在https://github.com/soarsmu/IncBL上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
