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Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts
Eriksson AB and Linköping University.
Lund University.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. UC Berkeley, Berkeley, CA, United States.ORCID iD: 0000-0001-8457-4105
Linköping University.
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2016 (English)In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 21, no 4, 1533-1578 p.Article in journal (Refereed) Published
Abstract [en]

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2016. Vol. 21, no 4, 1533-1578 p.
National Category
Software Engineering
URN: urn:nbn:se:kth:diva-174595DOI: 10.1007/s10664-015-9401-9ISI: 000379060700004ScopusID: 2-s2.0-84941356343OAI: diva2:877365

QC 20160715

Available from: 2015-12-07 Created: 2015-10-07 Last updated: 2016-08-15Bibliographically approved

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