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Issue No.04 - July/August (2008 vol.34)
pp: 485-496
Bart Baesens , K.U.Leuven, Leuven
Christophe Mues , University of Southampton, Southampton
Stefan Lessmann , University of Hamburg, Hamburg
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.
INDEX TERMS
Complexity measures, Data mining, Formal methods, Statistical methods
CITATION
Bart Baesens, Christophe Mues, Stefan Lessmann, "Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings", IEEE Transactions on Software Engineering, vol.34, no. 4, pp. 485-496, July/August 2008, doi:10.1109/TSE.2008.35
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