- Title
- Learning to rank duplicate bug reports
- Creator
- Zhou, Jian; Zhang, Hongyu
- Relation
- 21st ACM International Conference on Information and Knowledge Management (CIKM '12). CIKM '12 Proceedings of the 21st ACM International Conference on Information and Knowledge Management (Maui, HI 29 October-02 November, 2012) p. 852-861
- Publisher Link
- http://dx.doi.org/10.1145/2396761.2396869
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- conference paper
- Date
- 2012
- Description
- For a large and complex software system, the project team could receive a large number of bug reports. Some bug reports could be duplicates as they essentially report the same problem. It is often tedious and costly to manually check if a newly reported bug is a duplicate of an already reported bug. In this paper, we propose BugSim, a method that can automatically retrieve duplicate bug reports given a new bug report. BugSim is based on learning to rank concepts. We identify textual and statistical features of bug reports and propose a similarity function for bug reports based on the features. We then construct a training set by assembling pairs of duplicate and non-duplicate bug reports. We train the weights of features by applying the stochastic gradient descent algorithm over the training set. For a new bug report, we retrieve candidate duplicate reports using the trained model. We evaluate BugSim using more than 45,100 real bug reports of twelve Eclipse projects. The evaluation results show that the proposed method is effective. On average, the recall rate for the top 10 retrieved reports is 76.11%. Furthermore, BugSim outperforms the previous state-of-art methods that are implemented using SVM and BM25Fext.
- Subject
- bug reports; duplicate bug retrieval; duplicate documents; software maintenance; learning to rank
- Identifier
- http://hdl.handle.net/1959.13/1357327
- Identifier
- uon:31905
- Identifier
- ISBN:9781450311564
- Language
- eng
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