Prediction of Software Development Effort Using RBNN and GRNN

Software development effort prediction is one of the most key activities in software industry. Many models have been proposed to build a relationship between software size and effort; however we still have problems for effort prediction. This is because project data, available in the primary stages of project is often inadequate, unpredictable, uncertain and unclear. The need for accurate effort estimation in software industry is an ongoing challenge. Artificial Neural Network models are more apt in such situations. The present paper is concerned with developing software effort prediction models based on artificial neural networks. The models are designed to improve the performance of the network that suits to the COCOMO Model. Artificial Neural Network models are created using Radial Basis and Generalized Regression. A case study based on the NASA 93 database compares the proposed neural network models with the Intermediate COCOMO. The results were analyzed using different criterions VAF, MMRE, MARE, VARE, Mean BRE and Prediction. It is observed that the Radial Basis Neural Network provided better results. Keywords— Intermediate COCOMO, Cost Estimation, Radial Basis Neural Networks, Generalized Regression Neural Networks.