A more accurate bug localization technique for bugs with multiple buggy code files

Abstract

Context:

Bug localization is a key step in bug fixing. Despite considerable progress, existing bug localization techniques still perform unsatisfactorily in situations where the complete fix to a bug involves touching multiple buggy code files. That is, for such bugs, those techniques tend to locate correctly only one or at least not all buggy code files, leaving other buggy code files undetected.

Objective:

This study aims to improve bug localization in cases where resolving a bug requires modifications to multiple buggy code files by proposing HitMore to rank more truly buggy files higher in the recommendation list.

Method:

The basic idea of HitMore is to attempt to retrieve a subset of truly buggy code files first, then use these files to retrieve other buggy code files based on code relation analysis. For the first part, we designed three kinds of domain-specific features to build a machine-learning model to identify the truly buggy code file subset. For the second part, we make use of three types of code relations between the code base and the buggy file subset to better retrieve the remaining truly buggy code files.

Results:

The experiments on six widely open-source projects show that: Our technique is effective in identifying the subset of truly buggy code files, with a weighted prediction F1-Score of 86.1%–92.1%. By leveraging the code relations to the retrieved subset and the code base, our HitMore could retrieve all truly buggy code files for 29.31%–69.56% of bugs across six projects. For multiple-buggy-code-file bugs, HitMore could completely localize such bugs by up to 15.38%, 19.36%, and 11.86% more than three representative IRBL baselines across six projects.

Conclusion:

The experimental results demonstrate the potential of HitMore in reducing developers’ burden of locating and further fixing relatively complex bugs such as those with multiple buggy code files in practice.