High-Dimensional Hybrid Data Reduction for Effective Bug Triage

Abstract

Owing to the ever-expanding scale of software, solving the problem of bug triage efficiently and reasonably has become one of the most important issues in software project maintenance. However, there are two challenges in bug triage: low quality of bug reports and engagement of developers. Most of the existing bug triage solutions are based on the text information and have no consideration of developer engagement, which leads to the loss of bug triage accuracy. To overcome these two challenges, we propose a high-dimensional hybrid data reduction method that combines feature selection with instance selection to build a small-scale and high-quality dataset of bug reports by removing redundant or noninformative bug reports and words. In addition, we also study the recent engagement of developers, which can effectively distinguish similar bug reports and provide a more suitable list of the recommended developers. Finally, we experiment with four bug repositories: GCC, OpenOffice, Mozilla, and NetBeans. We experimentally verify that our method can effectively improve the efficiency of bug triage.