Design of Radial Basis Function Neural Networks for Software Effort Estimation
In spite of the several software effort estimation models
developed over the last 30 years, providing accurate estimates of
the software project under development is still unachievable goal.
Therefore, many researchers are working on the development of
new models and the improvement of the existing ones using
artificial intelligence techniques such as: case-based reasoning,
decision trees, genetic algorithms and neural networks. This
paper is devoted to the design of Radial Basis Function Networks
for software cost estimation. It shows the impact of the RBFN
network structure, especially the number of neurons in the hidden
layer and the widths of the basis function, on the accuracy of the
produced estimates measured by means of MMRE and Pred
indicators. The empirical study uses two different software
project datasets namely, artificial COCOMO'81 and Tukutuku
datasets.
Keywords: software effort estimation, RBF Neural Networks,
COCOMO'81, Tukutuku dataset
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