A NEW HIGH PERFORMANCE NEURAL NETWORK MODEL FOR SOFTWARE EFFORT ESTIMATION
Omprakash Tailor, Amit Kumar · 2014
In this research, it is concerned with concerned with constructing software effort estimation models based on artificial neural network. The model is designed accordingly to improve the performance of the network that suits to the COCOMO model. Recent year the software industry is growing rapidly and people pay more attention on how to keep high efficiency in the process of software development and management. In the process of software development , time, cost, manpower are all critical factor. At the stage of software project planning, project manager will evaluate these parameter to get an efficient software develop process. Software effort evaluate is an important aspect which includes amount of cost, schedule, and manpower requirement. In this paper, it is proposed to use multilayer feed forward neural network to accommodate the model and its parameter to estimate software development effort. The network is trained with back propagation learning algorithm by iteratively processing a set of training samples and comparing the network's prediction with actual effort. COCOMO dataset is used to train and to test the network and it was observed that proposed neural network model improve the estimation accuracy of the model. The compared with that of the OCOCMO model. The aim of this study is to enhance the estimation accuracy of COCOMO model , so that the estimated effort is more close to the actual effort.