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<p><b>Abstract</b>—An enhanced technique for risk categorization is presented. This technique, PCA-ANN, provides an improved capability to discriminate high-risk software. The approach draws on the combined strengths of pattern recognition, multivariate statistics and neural networks. Principal component analysis is utilized to provide a means of normalizing and orthogonalizing the input data, thus eliminating the ill effects of multicollinearity. A neural network is used for risk determination/classification. A significant feature of this approach is a procedure, herein termed cross-normalization. This procedure provides the technique with capability to discriminate data sets that include disproportionately large numbers of high-risk software modules.</p>
Software risk analysis and defect prediction, decision making, mathematical models, system process models.

D. E. Neumann, "An Enhanced Neural Network Technique for Software Risk Analysis," in IEEE Transactions on Software Engineering, vol. 28, no. , pp. 904-912, 2002.
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