Abstract: One of the biggest challenges that software developers face is to make an accurate estimate of the
project effort. Radial basis function neural networks have been used to software effort estimation in this work
using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The
results show that radial basis function neural network have obtained less Mean Square Error than the regression
method.
Keywords: software effort estimation, software repositories, radial basis function and artificial neural networks.
ACM Classification Keywords: I.2.6 Artificial Intelligence – Connectionism and neural nets, H.2.7 Database
Administration – Data Ware house and repository.
Link:
SOFTWARE EFFORT ESTIMATION USING RADIAL BASIS FUNCTION NEURAL
NETWORKS
Ana Maria Bautista, Angel Castellanos, Tomas San Feliu
http://www.foibg.com/ijita/vol21/ijita21-04-p03.pdf
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