Locating relevant source files for a given bug report is an important task in software development and maintenance. To make the locating process easier, information retrieval methods have been widely used to compute the content similarities between bug reports and source files. In addition to content similarities, various other sources of information such as the metadata and the stack-trace in the bug report can be used to enhance the localization accuracy. In this paper, we propose a supervised topic modeling approach for automatically locating the relevant source files of a bug report. In our approach, we take into account the following five key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. Fourth, metainformation brings additional guidance on the search space. Fifth, buggy source files could be already contained in the stack-trace. By integrating the above five observations, we experimentally show that the proposed method can achieve up to 67.1% improvement in terms of prediction accuracy over its best competitors and scales linearly with the size of the data.

中文翻译：

---

通过元数据和堆栈跟踪来增强监督性错误的本地化

找到给定错误报告的相关源文件是软件开发和维护中的重要任务。为了使查找过程更容易，信息检索方法已被广泛用于计算错误报告和源文件之间的内容相似度。除了内容相似之外，还可以使用其他各种信息源（例如错误报告中的元数据和堆栈跟踪）来提高定位精度。在本文中，我们提出了一种监督主题建模方法，用于自动定位错误报告的相关源文件。在我们的方法中，我们考虑了以下五个主要观点。首先，监督建模可以有效利用现有的修复历史。其次，错误报告中的某些单词倾向于在其相关的源文件中多次出现。第三，更长的源文件往往会有更多的错误。第四，元信息为搜索空间带来了额外的指导。第五，有问题的源文件可能已经包含在堆栈跟踪中。通过整合以上五个观察结果，我们实验证明了该方法在预测准确度方面可以比其最佳竞争对手提高67.1％，并且与数据大小呈线性比例。