An approach to detecting duplicate bug reports using natural language and execution information

An open source project typically maintains an open bug repository so that bug reports from all over the world can be gathered. When a new bug report is submitted to the repository, a person, called a triager, examines whether it is a duplicate of an existing bug report. If it is, the triager marks i...

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Bibliographic Details
Main Authors: Wang, Xiaoyin, Zhang, Lu, Xie, Tao, Anvik, John, Sun, Jiasu
Format: Conference Proceeding
Language:English
Subjects:
Computer bugs
Computer science education
Costs
duplicate bug report
Educational technology
execution information
information retrieval
Laboratories
Natural languages
Open source software
Social and professional topics > Professional topics > Management of computing and information systems > Software management > Software maintenance
Software and its engineering > Software creation and management > Software post-development issues
Software maintenance
Software quality
Testing
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Description
Summary:An open source project typically maintains an open bug repository so that bug reports from all over the world can be gathered. When a new bug report is submitted to the repository, a person, called a triager, examines whether it is a duplicate of an existing bug report. If it is, the triager marks it as DUPLICATE and the bug report is removed from consideration for further work. In the literature, there are approaches exploiting only natural language information to detect duplicate bug reports. In this paper we present a new approach that further involves execution information. In our approach, when a new bug report arrives, its natural language information and execution information are compared with those of the existing bug reports. Then, a small number of existing bug reports are suggested to the triager as the most similar bug reports to the new bug report. Finally, the triager examines the suggested bug reports to determine whether the new bug report duplicates an existing bug report. We calibrated our approach on a subset of the Eclipse bug repository and evaluated our approach on a subset of the Firefox bug repository. The experimental results show that our approach can detect 67%-93% of duplicate bug reports in the Firefox bug repository, compared to 43%-72% using natural language information alone.
ISSN:0270-5257
1558-1225
DOI:10.1145/1368088.1368151