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Automated Bug Assignment: Ensemble-based Machine Learning in Large Scale Industrial Contexts

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Author

Summary, in English

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.

Publishing year

2015

Language

English

Publication/Series

Empirical Software Engineering

Volume

21

Issue

4

Document type

Journal article

Publisher

Springer

Topic

  • Software Engineering

Keywords

  • Large scale
  • Industrial scale
  • Bug assignment
  • Bug reports
  • Classification
  • Ensemble learning
  • Machine learning

Status

Published

Project

  • Embedded Applications Software Engineering
  • Embedded Applications Software Engineering

ISBN/ISSN/Other

  • ISSN: 1573-7616