- Title
- Predicting defect numbers based on defect state transition models
- Creator
- Wang, Jue; Zhang, Hongyu
- Relation
- ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM'12). ESEM’12: Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (Lund, Sweden 19-20 September, 2012) p. 191-200
- Publisher Link
- http://dx.doi.org/10.1145/2372251.2372287
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- conference paper
- Date
- 2012
- Description
- During software maintenance, a large number of defects could be discovered and reported. A defect can enter many states during its lifecycle, such as NEW, ASSIGNED, and RESOLVED. The ability to predict the number of defects at each state can help project teams better evaluate and plan maintenance activities. In this paper, we present BugStates, a method for predicting defect numbers at each state based on defect state transition models. In our method, we first construct defect state transition models using historical data. We then derive a stability metric from the transition models to measure a project's defect-fixing performance. For projects with stable defect-fixing performance, we show that we can apply Markovian method to predict the number of defects at each state in future based on the state transition model. We evaluate the effectiveness of BugStates using six open source projects and the results are promising. For example, when predicting defect numbers at each state in December 2010 using data from July 2009 to June 2010, the absolute errors for all projects are less than 28. In general, BugStates also outperforms other related methods.
- Subject
- defect state transitions; defect prediction; defect numbers; defect-fixing performance; Markov models
- Identifier
- http://hdl.handle.net/1959.13/1357004
- Identifier
- uon:31847
- Identifier
- ISBN:9781450310567
- Language
- eng
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