Comparing learning to rank techniques in hybrid bug localization

Abstract

Bug localization is a software development and maintenance activity that aims to find relevant source code entities to be modified so that a specific bug can be fixed on the basis of the given bug report. Information retrieval (IR) techniques have been widely used to locate bugs in recent decades. These techniques mainly use the IR similarity between the bug report and source code entities. In addition to IR similarity, features that are extracted from version history, source code structure, dynamic analysis, and other resources are found to be beneficial for bug localization. The approaches utilizing extra features in IR-based bug localization are called hybrid bug localization. We conduct a short survey of the hybrid bug localization methods that use additional features in addition to IR similarity. We also use Learning to Rank (LtR) techniques to combine the beneficial features to improve bug localization. Learning to Rank is the application of machine learning in the ranking models for information retrieval. We compared eight LtR techniques in bug localization, and the experimental results show that coordinate ascent algorithms without normalization is a suitable LtR technique in bug localization for selected attributes, and it outperforms two state-of-the-art localization approaches for two large projects, Eclipse and SWT.