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May 10, 2008545 citations

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

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XWXiaoyin WangLZLu ZhangTXTao Xie

Key Points

  • The research aims to enhance the detection of duplicate bug reports by incorporating execution information alongside natural language data.
  • Developed a dual approach that analyzes natural language and execution information when a new bug report is submitted.
  • Calibrated the method on a subset of the Eclipse bug repository and tested it on a subset of the Firefox bug repository.
  • Compared the proposed method's effectiveness to traditional methods using only natural language processing.
  • Detected 67%-93% of duplicate bug reports in the Firefox bug repository with the new method.
  • Achieved improved detection compared to 43%-72% with natural language information alone.

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.

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Cite This Study

Wang et al. (2008) studied this question.

synapsesocial.com/papers/6a09aaba36c3abab5045ead1https://doi.org/10.1145/1368088.1368151
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