Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization technique improved when used in combination with code smell information. Although this technique showed promise, the study showed limited usefulness because of the small number of: (1) projects in the dataset, (2) types of smell information, and (3) baseline bug localization techniques used for assessment. This paper presents an extension of our previous experiments on Bench4BL, the largest bug localization benchmark dataset available for bug localization. In addition, we generalized the smell-aware bug localization technique to allow different configurations of smell information, which were combined with various bug localization techniques. Our results confirmed that our technique can improve the performance of IR-based bug localization techniques for the class level even when large datasets are processed. Furthermore, because of the optimized configuration of the smell information, our technique can enhance the performance of most state-of-the-art bug localization techniques.

错误本地化是软件维护的重要方面，因为它可以找到应更改以修复特定错误的模块。我们之前的研究表明，与代码气味信息结合使用时，基于信息检索（IR）的错误定位技术的准确性得到了提高。尽管这项技术显示出了希望，但由于以下几个方面的原因，该研究的用途有限：（1）数据集中的项目，（2）气味信息的类型以及（3）用于评估的基准错误定位技术。本文介绍了我们先前对Bench4BL进行的实验的扩展，Bench4BL是可用于错误本地化的最大错误本地化基准数据集。此外，我们对气味感知错误的定位技术进行了通用化，以允许对气味信息进行不同的配置，结合了各种错误本地化技术。我们的结果证实，即使处理大型数据集，我们的技术也可以针对类级别提高基于IR的错误定位技术的性能。此外，由于气味信息的优化配置，我们的技术可以增强大多数最新的bug定位技术的性能。