Predicting bug-fixing time: an empirical study of commercial software projects

Hongyu Zhang, Liang Gong, Steven Versteeg

Abstract: For a large and evolving software system, the project team could receive many bug reports over a long period of time. It is important to achieve a quantitative understanding of bug-fixing time. The ability to predict bug-fixing time can help a project team better estimate software maintenance efforts and better manage software projects. In this paper, we perform an empirical study of bug-fixing time for three CA Technologies projects. We propose a Markov-based method for predicting the number of bugs that will be fixed in future. For a given number of defects, we propose a method for estimating the total amount of time required to fix them based on the empirical distribution of bug-fixing time derived from historical data. For a given bug report, we can also construct a classification model to predict slow or quick fix (e.g., below or above a time threshold). We evaluate our methods using real maintenance data from three CA Technologies projects. The results show that the proposed methods are effective.