A New Approach For Estimating Software Effort Using RBFN Network

The prediction of software development effort has been focused mostly on the accuracy comparison of algorithmic models rather than on the suitability of the approach for building software effort prediction systems. Several estimation techniques have been developed to predict the Effort estimation. In this paper the main focus is on investigating the accuracy of the prediction of effort using RBFN network which can be used for functional approximation. The use of RBFN to estimate software development effort requires the determination of its architecture parameters according to the characteristics of COCOMO, especially the number of input neurons, no of hidden neurons, centers ci, width σ1 and weight wi. In the aspect of learning, the RBFN network is much faster than other network because the learning process in this network has two stages and both stages can be made efficient by appropriate learning algorithms. The proposed network is empirically validated using COCOMO’81 dataset which is used to train and test the designed RBFN network and found that the RBFN designed with the K-means clustering algorithm performs better, in terms cost estimation accuracy.