A Neural Network Approach for Web Cost Estimation

C.S. Reddy, K.V.S.V.N. Raju, T. Srinivas, and G.L. Devi (India)


Cost estimation, Neural network, Web application, WebMo, Web object.


Accurate cost estimates are an essential element to remain successful in the market, so cost estimation initiatives have been in the center of attention for many firms. Web development projects are certainly different from traditional software development projects and hence, require differently tailored measures for accurate estimation. The use of neural network in estimating software cost by Nasser Tadayon [1] produced accurate results, but it can’t be applied to web applications, because they do not take all of the web objects into consideration. In this paper, author explores the use of expert judgment and machine learning techniques using neural network as well as referencing WebMo Estimation model to predict the cost of software. The proposed network improves the accuracy of the estimation as the number of dataset increases with input from expert judgment that affects the learning procedure.

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