Context: Bug localization, which locates suspicious snippets related to the bugs mentioned in the bug reports, is time-consuming and laborious. Many automatic bug localization methods have been proposed to speed up the process of bug fixing and reduce the burden on developers. However, these methods have not fully utilized the intra-relations and inter-relations among the bug reports and the source files (i.e., call relationships between the source files).

Objective: In this paper, we propose a novel method LaProb (a label propagation-based software bug localization method) that makes full use of the intra-relations and inter-relations among the bug reports and the source files.

Method: LaProb transforms the problem of bug localization into a multi-label distribution learning problem. LaProb first constructs a BHG (Biparty Hybrid Graph) by analyzing the structures and contents of bug reports and source files, and calculates the intra-relations between pairs of bug reports and source files, as well as the inter-relations between bug reports and source files. Based on BHG, LaProb then predicts the label distribution on source files by using the label propagation algorithm for the target bug report. Finally, LaProb finishes the bug localization task by sorting the results of label propagation.

Results: The experimental results on nine open-source software projects (i.e., SWT, AspectJ, Eclipse, ZXing, SEC, HIVE, HBASE, WFLY and ROO) show that compared with several state-of-the-art methods (including BugLocator, BRTracer, BLUiR, AmaLgam, Locus and BLIZZARD), LaProb performs the best in terms of all five metrics on average. For MAP performance measure, LaProb achieves an improvement of 30.9%, 36.6%, 28.0%, 22.2%, 20.1% and 53.5%, respectively.

Conclusion: LaProb is capable of making full use of the intra-relations and inter-relations among the bug reports and the source files and achieves better performance than seven state-of-the-art methods.

上下文：错误本地化是费时费力的工作，它定位与错误报告中提到的错误相关的可疑代码段。已经提出了许多自动的错误本地化方法，以加快错误修复过程并减轻开发人员的负担。但是，这些方法还没有充分利用错误报告与源文件之间的内部关系和内部关系（即，源文件之间的调用关系）。

目的：在本文中，我们提出了一种新颖的方法LaProb（一种基于标签传播的软件错误定位方法），该方法充分利用了错误报告和源文件之间的内部关联和内部关联。

方法： LaProb将错误本地化问题转换为多标签分发学习问题。LaProb首先通过分析错误报告和源文件的结构和内容来构造BHG（双向混合图），然后计算错误报告和源文件对之间的内部关系以及错误报告和源文件之间的相互关系。文件。然后，LaProb基于BHG，通过使用目标错误报告的标签传播算法来预测源文件上的标签分布。最后，LaProb通过对标签传播的结果进行排序来完成bug本地化任务。

结果：在9个开源软件项目（即SWT，AspectJ，Eclipse，ZXing，SEC，HIVE，HBASE，WFLY和ROO）上的实验结果表明，与几种最新方法（包括BugLocator， （BRTracer，BLUiR，AmaLgam，Locus和BLIZZARD），LaProb在所有五个指标上平均表现最好。对于MAP性能衡量，LaProb分别提高了30.9％，36.6％，28.0％，22.2％，20.1％和53.5％。

结论： LaProb能够充分利用bug报告和源文件之间的内部关系和内部关系，并且比七个最新方法具有更好的性能。