Benchmarking classification models for software defect prediction: a proposed framework and novel findings


Lessmann, Stefan, Baesens, Bart, Mues, Christophe and Pietsch, Swantje (2008) Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Transactions on Software Engineering, 34, (4), 485-496. (doi:10.1109/TSE.2008.35).

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Description/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.

Item Type: Article
ISSNs: 0098-5589 (print)
Keywords: complexity measures, data mining, formal methods, statistical methods
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics > QA76 Computer software
H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > School of Management
ePrint ID: 63006
Date Deposited: 14 Oct 2008
Last Modified: 27 Mar 2014 18:44
URI: http://eprints.soton.ac.uk/id/eprint/63006

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