Reducing the effort of bug report triage: Recommenders for development-oriented decisions

• Published in: ACM transactions on software engineering and methodology 2011-08, Vol.20 (3), p.1-35
• Main Authors: Anvik, John, Murphy, Gail C.
• Format: Article
• Language: English
• Subjects: Decisions; Methodology; Project development; Repositories; Software development; Software engineering
• ISSN: 1049-331X; 1557-7392
• DOI: 10.1145/2000791.2000794

A key collaborative hub for many software development projects is the bug report repository. Although its use can improve the software development process in a number of ways, reports added to the repository need to be triaged. A triager determines if a report is meaningful. Meaningful reports are then organized for integration into the project's development process. To assist triagers with their work, this article presents a machine learning approach to create recommenders that assist with a variety of decisions aimed at streamlining the development process. The recommenders created with this approach are accurate; for instance, recommenders for which developer to assign a report that we have created using this approach have a precision between 70% and 98% over five open source projects. As the configuration of a recommender for a particular project can require substantial effort and be time consuming, we also present an approach to assist the configuration of such recommenders that significantly lowers the cost of putting a recommender in place for a project. We show that recommenders for which developer should fix a bug can be quickly configured with this approach and that the configured recommenders are within 15% precision of hand-tuned developer recommenders.