Software quality prediction using median-adjusted class labels

Software metrics aid project managers in predicting the quality of software systems. A method is proposed using a neural network classifier with metric inputs and subjective quality assessments as class labels. The labels are adjusted using fuzzy measures of the distances from each class center computed using robust multivariate medians.