Comparison of artificial neural network and regression models for estimating software development effort

Abstract Estimating the amount of effort required for developing an information system is an important project management concern. In recent years, a number of studies have used neural networks in various stages of software development. This study compares the prediction performance of multilayer perceptron and radial basis function neural networks to that of regression analysis. The results of the study indicate that when a combined third generation and fourth generation languages data set were used, the neural network produced improved performance over conventional regression analysis in terms of mean absolute percentage error.

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