Effective Bug Triage Based on Historical Bug-Fix Information

• Published in: 2014 IEEE 25th International Symposium on Software Reliability Engineering pp. 122 - 132
• Main Authors: Hao Hu, Hongyu Zhang, Jifeng Xuan, Weigang Sun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2014
• Subjects: bug report assignment; bug repository; bug triage; Companies; Computational modeling; Computer bugs; developer recommendation; Software; Support vector machines; Text categorization; Vectors
• ISSN: 1071-9458; 2332-6549
• DOI: 10.1109/ISSRE.2014.17

For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called Bug Fixer, which recommends developers for a new bug report based on historical bug-fix information. Bug Fixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship between the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, Bug Fixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate Bug Fixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.