School of Information Technology,Jiangxi University of Finance and Economics, NanChang, 330013, China; Jiangxi Key Laboratory of Data and Knowledge Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

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Abstract—The research on open source software has attracted a great deal of attention during the past decades for its wide applications in both academia and industry. Among the research topics related to open source software, the usefulness of software network metrics for fault prediction has been received much attention recently. In order to verify the importance of software network metrics in the performance of fault prediction models, this study mixes software network and Chidamber & Kemerer (CK) metrics to predict pre- and post-release fault-proneness of Eclipse version 2.0. The "small world" and "scale-free" characteristics and Spearman correlation have been analyzed in detail. In addition, we employed10 classifiers, 2 weak classifiers, and 5 performance evaluation criteria to conduct three sets of experiments. Experimental results have demonstrated that the software network metrics can only provide little overall performance improvement. Based on the comprehensive study, the effect of software network metrics can be ignored.

Index Terms—software network metrics, Chidamber & Kemerer (CK) metrics, fault prediction, classification, scalefree characteristic

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Cite: Fangjun Wu, "Comparative Analysis of Software Network and CK Metrics: Implications for Pre- and Postrelease Faults," Journal of Software vol. 9, no. 3, pp. 541-552, 2014.