An Extensive Study on Smell-Aware Bug Localization

TL;DR

This study extends previous research on smell-aware bug localization by using a larger dataset and multiple configurations, demonstrating improved accuracy of IR-based techniques at the class level across various bug localization methods.

Contribution

It generalizes smell-aware bug localization to multiple configurations and validates its effectiveness on the largest benchmark dataset, enhancing existing bug localization techniques.

Findings

Improves IR-based bug localization accuracy at class level.

Enhances performance of state-of-the-art bug localization techniques.

Effective even with large datasets and diverse configurations.

Abstract

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.