ABSTRACT This paper is concerned with constructing software effort estimation model based on artificial neural networks. The model is designed accordingly to improve the performance of the network that suits to the COCOMO Model. It is proposed to use single layer feed forward neural network to accommodate the model and its parameters to estimate software development effort. The network is trained with back propagation learning algorithm and Resilient Back propagation algorithm (RPROP) by iteratively processing a set of training samples and comparing the network’s prediction with the actual effort. COCOMO dataset is used to train and to test the network and it was observed that proposed neural network model improves the estimation accuracy of the model. The test results from the trained neural network are compared with that of the COCOMO model. By comparing the results of these two models, it is proven that both models (SLANN with BP and SLANN with RPROP) works better than COCOMO and SLANN with RPROP is an optimal neural network model for software effort estimation. SLANN with BP works well only for projects with small size, where as SLANN with RPROP works well for all kinds of projects as the convergence rate of RPROP algorithm is very high. The preliminary results obtained suggest that the proposed architecture can be replicated for accurately forecasting the software development effort.
