D&C：一种基于IR的缺陷定位方法
D&C: A Divide-and-Conquer Approach to IR-based Bug Localization
摘要：
软件维护中的许多自动化任务依赖于信息检索技术来识别非结构化数据中的特定信息。Bug本地化就是这样一个典型的任务，其中分析bug报告中的文本以识别源代码中与所报告的bug相关联的文件位置。尽管结果令人鼓舞，但基于IR的错误定位工具提供的性能对于大规模采用仍然不显著。我们认为，一个原因可能是试图建立一个一刀切的方法。在本文中，我们广泛研究了最先进的错误定位工具的性能，重点是查询制定和它的重要性方面的本地化性能。基于这项研究的见解，我们提出了一种新的学习方法，其中多个分类器模型在明确的错误位置对集合上进行训练。具体地说，我们应用梯度提升监督学习的方法，以各种错误报告的本地化似乎是成功的特定类型的功能。培训场景建立在我们的发现基础之上，即各种最先进的本地化工具对于特定的bug报告集可以具有很高的性能。我们实现D&C，它计算适当的权重，应该分配给信息令牌类型对之间的相似性测量。在大型和最新数据集上的实验结果表明，D&C的性能优于最先进的工具。平均而言，实验产生的MAP得分为0.52，MRR得分为0.63，这提供了所有工具的实质性性能改进：MAP提高了4到10个百分点，而MRR提高了1到12个百分点。最后，我们注意到D&C的本地化性能是稳定的：大约50%的bug可以定位在Top1，77%在Top5，85%在Top10。
英文摘要：
Many automated tasks in software maintenance rely on information retrieval techniques to identify specific information within unstructured data. Bug localization is such a typical task, where text in a bug report is analyzed to identify file locations in the source code that can be associated to the reported bug. Despite the promising results, the performance offered by IR-based bug localization tools is still not significant for large adoption. We argue that one reason could be the attempt to build a one-size-fits-all approach. In this paper, we extensively study the performance of state-of-the-art bug localization tools, focusing on query formulation and its importance with respect to the localization performance. Building on insights from this study, we propose a new learning approach where multiple classifier models are trained on clear-cut sets of bug-location pairs. Concretely, we apply a gradient boosting supervised learning approach to various sets of bug reports whose localizations appear to be successful with specific types of features. The training scenario builds on our findings that the various state-of-the-art localization tools can be highly performant for specific sets of bug reports. We implement D&C, which computes appropriate weights that should be assigned to the similarity measurements between pairs of information token types. Experimental results on large and up-to-date datasets reveal that D&C outperforms state-of-the-art tools. On average, the experiments yield an MAP score of 0.52, and an MRR score of 0.63 with a curated dataset, which provides a substantial performance improvement over all tools: MAP is improved by between 4 and up to 10 percentage points, while MRR is improved by between 1 and up to 12. Finally, we note that D&C is stable in its localization performance: around 50% of bugs can be located at Top1, 77% at Top5 and 85% at Top10.
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。
