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An approach to detecting duplicate bug reports using natural language and execution information
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International Conference on Software Engineering archive
Proceedings of the 30th international conference on Software engineering table of contents
Leipzig, Germany
SESSION: Evolution table of contents
Pages 461-470  
Year of Publication: 2008
ISBN:978-1-60558-079-1
Authors
Xiaoyin Wang  Peking University, Beijing, China
Lu Zhang  Peking University, Beijing, China
Tao Xie  North Carolina State University, Raleigh, NC, USA
John Anvik  University of Victoria, Victoria, BC, Canada
Jiasu Sun  Peking University, Beijing, China
Sponsors
ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Xiaoyin Wang: colleagues
Lu Zhang: colleagues
Tao Xie: colleagues
John Anvik: colleagues
Jiasu Sun: colleagues