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Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System

2017; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-59060-8_4

1611-3349

Adrian-Cătălin Florea, John Anvik, Răzvan Andonie,

Software System Performance and Reliability

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.