Journal of Pattern Recognition Research

The automated prediction of qualitative attributes such as software complexity is a desirable software engineering goal. A potential technique is to use software metrics as quantitative predictors for these kinds of attributes. We describe a pattern classification method where a large collection of classifiers is presented with randomly selected subsets of software metrics describing modules from a sophisticated biomedical data analysis system. The method identifies the software metric subset that has the highest discriminatory power vis-à-vis software complexity. That is, we identify the metric subset that is most effective at predicting this qualitative attribute. This pattern classification method is empirically evaluated and carefully validated against two benchmark approaches. We demonstrate that this method has utility in the automated prediction of software complexity using quantitative software measures.