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This paper presents an empirical analysis of significance of different process and product metrics in defect prediction models. 48 releases of 15 open-source and 38 releases of 7 proprietary projects were investigated. Pearson correlation coefficients with the number of defects were calculated for each of the metrics respectively. Subsequently defect prediction models were built using linear stepwise regression and a discriminant analysis was conducted. Since the stepwise regression was used, the obtained models always consisted of a subset of the investigated metrics. Therefore, it was possible to check whether the selection of metrics corresponds with the correlations with number of defects and the discriminant power of the metrics. Moreover, according to the obtained results some of the metrics were recommended with regard to defect prediction.
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