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.
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Notes
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Data extracted from a Mozilla’s Bugzilla database dump as of March 4, 2016.
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The profiles table contains personal information, such as names and email addresses of the project members.
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In cases where a bug report triager is also an active developer, the person will have been assigned to issues that are marked as FIXED.
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Those terms tagged as either Noun {NN}, Noun Plural {NNS}, Proper Noun {NNP}, or Proper Noun Plural {NNPS}.
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Without this reduction, the dataset was found to be too large for WEKA to process.
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The authors are grateful to the Mozilla Foundation for providing a dump of their Bugzilla database.
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Florea, AC., Anvik, J., Andonie, R. (2017). Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_4
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