An Empirical Study on Interaction Factors Influencing Bug Reopenings

Bugs can be reopened after they have been closed due to identification of the actual cause, previous incorrect fixing, or better reproducing, etc. Reopened bugs may increase the cost in maintenance, degrade the overall quality of the software product, reduce the trust of users, and bring unnecessary work to the already-busy developers. To minimize the occurrence of bug reopenings, the potential causes and factors should be analyzed. In this paper, we explore 24 interaction factors to study their influence on bug reopenings. The data are extracted from Mylyn logs of four open-source projects. We first verify the negative impacts of bug reopenings. Then, we identify 17 factors that significantly influence the likelihood of bug reopenings using statistic tests. In addition, we build decision trees using interaction factors to predict bug reopenings and achieve good performance.

[1]  Bora Caglayan,et al.  Factors characterizing reopened issues: a case study , 2012, PROMISE '12.

[2]  Foutse Khomh,et al.  An Empirical Study of the Effect of File Editing Patterns on Software Quality , 2012, 2012 19th Working Conference on Reverse Engineering.

[3]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[4]  Philip J. Guo,et al.  Characterizing and predicting which bugs get reopened , 2012, 2012 34th International Conference on Software Engineering (ICSE).

[5]  Martin P. Robillard,et al.  The Influence of the Task on Programmer Behaviour , 2011, 2011 IEEE 19th International Conference on Program Comprehension.

[6]  Hoh Peter In,et al.  Micro interaction metrics for defect prediction , 2011, ESEC/FSE '11.

[7]  Mik Kersten,et al.  Mylar: a degree-of-interest model for IDEs , 2005, AOSD '05.

[8]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[9]  Ken-ichi Matsumoto,et al.  Studying re-opened bugs in open source software , 2012, Empirical Software Engineering.

[10]  Ken-ichi Matsumoto,et al.  Predicting Re-opened Bugs: A Case Study on the Eclipse Project , 2010, 2010 17th Working Conference on Reverse Engineering.

[11]  Foutse Khomh,et al.  An Empirical Study on Factors Impacting Bug Fixing Time , 2012, 2012 19th Working Conference on Reverse Engineering.