CPN-a hybrid model for software cost estimation

One of the challenges faced by the managers in the software industry today is the ability to accurately define the requirements of the software projects early in the software development phase. The cost-benefit analysis forms the basis of the planning and decision making throughout the software development lifecycle. As such there is a need for efficient software cost estimation techniques for making any endeavor viable. Software cost estimation is the process of prognosticating the amount of effort required to build a software project. In this paper we have proposed a Particle Swarm Optimization (PSO) technique which operates on data sets clustered using the K-means clustering algorithm. PSO is employed to generate parameters of the COCOMO model for each cluster of data values. The clusters and effort parameters are then trained to a Neural Network by using Back propagation technique, for classification of data. Here we have tested the model on the COCOMO 81 dataset and also compared the obtained values with standard COCOMO model. By making use of the experience from Neural Networks and the efficient tuning of parameters by PSO operating on clusters, the proposed model is able to generate better results and it can be applied efficiently to larger data sets.