Learning to rank for bug report assignee recommendation
Conference Proceeding Article
Projects receive a large number of bug reports, and resolving these reports take considerable time and human resources. To aid developers in the resolution of bug reports, various automated techniques have been proposed to identify and recommend developers to address newly reported bugs. Two families of bug assignee recommendation techniques include those that recommend developers who have fixed similar bugs before (a.k.a. activity-based techniques) and those recommend suitable developers based on the location of the bug (a.k.a. location-based techniques). Previously, each of these techniques has been investigated separately. In this work, we propose a unified model that combines information from both developers' previous activities and suspicious program locations associated with a bug report in the form of similarity features. We have evaluated our proposed approach on more than 11,000 bug reports from Eclipse JDT, Eclipse SWT and ArgoUML projects. Our experiments show that our unified model can outperform a location-based baseline by Anvik et al. and an activity-based baseline by Shokripour et al. In terms of correct recommendations at top-1 position, our unified model outperforms the activity-based baseline 50.0%-100.0%, and the location-based baseline by 11.1%-27.0%
Bug Assignee Recommendation, Bug Reports
Computer Sciences | Programming Languages and Compilers
Software and Cyber-Physical Systems
24th IEEE International Conference on Program Comprehension: ICPC 2016: Proceedings: May 16-17, Austin, Texas
City or Country
TIAN YUAN, ; WIJEDASA, Withthige Dinusha Ruchira; David LO; and LE GOUES, Claire.
Learning to rank for bug report assignee recommendation. (2016). 24th IEEE International Conference on Program Comprehension: ICPC 2016: Proceedings: May 16-17, Austin, Texas. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3561