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ICSE 2013

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

Hongyu Zhang, Liang Gong, Steven Versteeg

DOI 10.1109/icse.2013.6606654dblp

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.

BibTeX
@inproceedings{Zhang-al:ICSE13,
  author    = {Hongyu Zhang and
               Liang Gong and
               Steven Versteeg},
  title     = {Predicting bug-fixing time: an empirical study of commercial software projects},
  booktitle = {ICSE},
  pages     = {1042--1051},
  publisher = {{IEEE} Computer Society},
  year      = {2013},
}

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