Reducing the effort of bug report triage: Recommenders for development-oriented decisions
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
8-2011
Abstract
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.
Recommended Citation
Anvik, J., & Murphy, G. C. (2011). Reducing the effort of bug report triage. ACM Transactions on Software Engineering and Methodology, 20(3), 1–35. https://doi.org/10.1145/2000791.2000794
Journal
ACM Transactions on Software Engineering and Methodology
Rights
© 2011 ACM
Comments
This article was originally published in ACM Transactions on Software Engineering and Methodology. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.