Title
Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System
Document Type
Conference Presentation
Department or Administrative Unit
Computer Science
Publication Date
6-10-2017
Abstract
The use of recommenders for bug report triage decisions is especially important in the context of large software development projects, where both the frequency of reported problems and a large number of active developers can pose problems in selecting the most appropriate developer to work on a certain issue. From a machine learning perspective, the triage problem of bug report assignment in software projects may be regarded as a classification problem which can be solved by a recommender system. We describe a highly scalable SVM-based bug report assignment recommender that is able to run on massive datasets. Unlike previous desktop-based implementations of bug report triage assignment recommenders, our recommender is implemented on a cloud platform. The system uses a novel sequence of machine learning processing steps and compares favorably with other SVM-based bug report assignment recommender systems with respect to prediction performance. We validate our approach on real-world datasets from the Netbeans, Eclipse and Mozilla projects.
Recommended Citation
Florea, A.-C., Anvik, J., & Andonie, R. (2017). Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System. International Conference on Artificial Intelligence and Soft Computing 2017, 31–42. https://doi.org/10.1007/978-3-319-59060-8_4
Journal
ICAISC 2017: Artificial Intelligence and Soft Computing
Rights
© Springer International Publishing AG 2017
Comments
This article was originally published in ICAISC 2017: Artificial Intelligence and Soft Computing. 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.