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TIPO
Artículo
TÍTULO
Neural networks for predicting the duration of new software projects
AUTOR
Lopez-Martin, C.. Abran, A..
ASESORES
L�pez-Mart�n, C., Information Systems Department, CUCEA, Universidad de Guadalajara, Perif�rico Norte #799, P.O. Box 45100Zapopan, Jalisco, Mexico; Abran, A., Department of Software Engineering and Information Technology, �cole de Technologie Sup�rieure, Universit� du Qu�bec, 1100 Rue Notre-Dame OuestMontr�al, Quebec, QC, Canada.
The duration of software development projects has become a competitive issue: only 39% of them are finished on time relative to the duration planned originally. The techniques for predicting project duration are most often based on expert judgment and mathematical models, such as statistical regression or machine learning. The contribution of this study is to investigate whether or not the duration prediction accuracy obtained with a multilayer feedforward neural network model, also called a multilayer perceptron (MLP), and with a radial basis function neural network (RBFNN) model is statistically better than that obtained by a multiple linear regression (MLR) model when functional size and the maximum size of the team of developers are used as the independent variables. The three models mentioned above are trained and tested by predicting the duration of new software development projects with a set of projects from the International Software Benchmarking Standards Group (ISBSG) release 11. Results based on absolute residuals, Pred(l) and a Friedman statistical test show that prediction accuracy with the MLP and the RBFNN is statistically better than with the MLR model. � 2015, Elsevier Inc. All rights reserved..