SOFTWARE-DEVELOPMENT COST ESTIMATION - INTEGRATING NEURAL-NETWORK WITH CLUSTER-ANALYSIS

For software project planning control and management, an accurate esti mate of software development cost is important. Past research has focu sed on using parametric models to predict development cost based on at tributes such as lines of code or function points. This requires resea rchers to identify the set of factors that influence cost estimation b efore the system is constructed. We propose a non-parametric approach that integrates a neural network method with cluster analysis to estim ate development cost. The integration of the two techniques not only a llows for a more accurate cost estimate but also leads to an increase in the training efficacy of the network. (C) 1998 Elsevier Science B.V . All rights reserved.