An Extensive Study on Smell-Aware Bug Localization
An Extensive Study on Smell-Aware Bug Localization
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DOI：
10.1016/j.jss.2021.110986
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Aoi Takahashi;Natthawute Sae-Lim;Shinpei Hayashi;M. Saeki
中科院分区：
文献类型：
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作者：
Aoi Takahashi;Natthawute Sae-Lim;Shinpei Hayashi;M. Saeki
错误定位是软件维护的一个重要方面，因为它可以定位应该修改以修复特定错误的模块。我们以前的研究表明，信息检索（IR）为基础的错误定位技术的准确性提高时，结合使用代码气味信息。虽然这种技术显示出了希望，但该研究显示出有限的实用性，因为：（1）数据集中的项目，（2）气味信息的类型，以及（3）用于评估的基线错误定位技术。本文介绍了我们以前的实验Bench4BL，最大的错误本地化基准数据集可用于错误本地化的扩展。此外，我们推广的气味感知的错误定位技术，允许不同的配置的气味信息，这是结合各种错误定位技术。我们的结果证实，即使在处理大型数据集时，我们的技术也可以提高基于IR的错误定位技术在类级别的性能。此外，由于气味信息的优化配置，我们的技术可以提高大多数国家的最先进的错误定位技术的性能。
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.