Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

  • Authors:

  • Stefan Lessmann;Bart Baesens;Christophe Mues;Swantje Pietsch

  • Affiliations:

  • University of Hamburg, Hamburg;K.U.Leuven, Leuven;University of Southampton, Southampton;University of Hamburg, Hamburg

  • Venue:
  • IEEE Transactions on Software Engineering
  • Year:
  • 2008

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Abstract

Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary datasets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and finally, limited use of statisti-cal testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over ten public domain datasets from the NASA Metrics Data repository. Our results indicate that the importance of the particu-lar classification algorithm may have been overestimated in previous research since no significant performance differ-ences could be detected among the top-17 classifiers.