  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.
