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2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) (2016)
Suita, Osaka, Japan
March 14, 2016 to March 18, 2016
ISBN: 978-1-5090-1855-0
pp: 46-56
ABSTRACT
The work to be performed on open source systems, whether feature developments or defects, is typically described as an issue (or bug). Developers self-select bugs from the many open bugs in a repository when they wish to perform work on the system. This paper evaluates a recommender, called NextBug, that considers the textual similarity of bug descriptions to predict bugs that require handling of similar code fragments. First, we evaluate this recommender using 69 projects in the Mozilla ecosystem. We show that for detecting similar bugs, a technique that considers just the bug components and short descriptions perform just as well as a more complex technique that considers other features. Second, we report a field study where we monitored the bugs fixed for Mozilla during a week. We sent mails to the developers who fixed these bugs, asking whether they would consider working on the recommendations provided by NextBug, 39 developers (59%) stated that they would consider working on these recommendations, 44 developers (67%) also expressed interest in seeing the recommendations in their bug tracking system.
INDEX TERMS
Duplicated Bug Detectors, Bug reports, Recommendation Systems, Similar Bug Detectors,
CITATION
Henrique Rocha, Marco Tulio Valente, Humberto Marques-Neto, Gail C. Murphy, "An Empirical Study on Recommendations of Similar Bugs", 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), vol. 01, no. , pp. 46-56, 2016, doi:10.1109/SANER.2016.87
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