Adopting Curvilinear Component Analysis to Improve Software Cost Estimation Accuracy

Cost estimation is a critical issue for software organizations. Good estimates can help us make more informed decisions (controlling and planning software risks), if they are reliable (correct) and valid (stable). In this study, we apply a variable reduction technique (based on auto-associative feed--forward neural networks - called Curvilinear component analysis) to log-linear regression functions calibrated with ordinary least squares. Based on a COCOMO 81 data set, we show that Curvilinear component analysis can improve the estimation model accuracy by turning the initial input variables into an equivalent and more compact representation. We show that, the models obtained by applying Curvilinear component analysis are more parsimonious, correct, and reliable.