Automating bug report assignment

ABSTRACT

Open-source development projects typically support an open bug repository to which both developers and users can report bugs. A report that appears in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the responsibility of resolving the report. Large open-source developments are burdened by the rate at which new bug reports appear in the bug repository. The thesis of this work is that the task of triage can be eased by using a semi-automated approach to assign bug reports to developers. The approach consists of constructing a recommender for bug assignments; examined are both a range of algorithms that can be used and the various kinds of information provided to the algorithms. The proposed work seeks to determine through human experimentation a sufficient level of precision for the recommendations, and to analytically determine the trade-offs of the various algorithmic and information choices.

REFERENCES

• 1: John Anvik , Lyndon Hiew , Gail C. Murphy, Coping with an open bug repository, Proceedings of the 2005 OOPSLA workshop on Eclipse technology eXchange, p.35-39, October 16-17, 2005, San Diego, California [doi>10.1145/1117696.1117704]
• 2: John Anvik , Lyndon Hiew , Gail C. Murphy, Who should fix this bug?, Proceeding of the 28th international conference on Software engineering, May 20-28, 2006, Shanghai, China [doi>10.1145/1134285.1134336]
• 3: Ivan T. Bowman , Richard C. Holt, Reconstructing Ownership Architectures To Help Understand Software Systems, Proceedings of the 7th International Workshop on Program Comprehension, p.28, May 05-07, 1999
• 4: G. Canfora and L. Cerulo. How software repositories can help in resolving a new change request. In Workshop on Empirical Studies in Reverse Engineering, 2005.
• 5: D. Čubranić and G. C. Murphy. Automatic bug triage using text classification. In Proceedings of Software Engineering and Knowledge Engineering, pages 92--97, 2004.
• 6: S. R. Gunn. Support Vector Machines for classification and regression. Technical report, University of Southampton, 1998.
• 7: Isabelle Guyon , André Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, 3, 3/1/2003
• 8: Thorsten Joachims, Text Categorization with Suport Vector Machines: Learning with Many Relevant Features, Proceedings of the 10th European Conference on Machine Learning, p.137-142, April 21-23, 1998
• 9: Audris Mockus , James D. Herbsleb, Expertise browser: a quantitative approach to identifying expertise, Proceedings of the 24th International Conference on Software Engineering, May 19-25, 2002, Orlando, Florida [doi>10.1145/581339.581401]
• 10: Andy Podgurski , David Leon , Patrick Francis , Wes Masri , Melinda Minch , Jiayang Sun , Bin Wang, Automated support for classifying software failure reports, Proceedings of the 25th International Conference on Software Engineering, May 03-10, 2003, Portland, Oregon
• 11: Eric S. Raymond , Tim O'Reilly, The Cathedral and the Bazaar, O'Reilly & Associates, Inc., Sebastopol, CA, 1999
• 12: C. R. Reis and R. P. de Mattos Fortes. An overview of the software engineering process and tools in the Mozilla project. In Proceedings of the Open Source Software Development Workshop, pages 155--175, 2002.
• 13: Ian H. Witten , Eibe Frank, Data mining: practical machine learning tools and techniques with Java implementations, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING

General Terms:
Management

Keywords:
bug report assignment, triage