Software Cost Estimation Models Using Radial Basis Function Neural Networks

Authors:
Ali Idri;Azeddine Zahi;Emilia Mendes;Abdelali Zakrani
Affiliations:
Department of Software Engineering, ENSIAS, Mohamed V University, Rabat, Morocco;Department of Computer Science FST, Sidi Mohamed Ben Abdellah Universit, Fez, Morocco;Computer Science Department, The University of Auckland, Auckland, New Zealand Private Bag 92019;Department of Software Engineering, ENSIAS, Mohamed V University, Rabat, Morocco
Venue:
Software Process and Product Measurement
Year:
2007

Citing 11
Cited 4

Approximation and radial-basis-function networks

Neural Computation
Machine Learning Approaches to Estimating Software Development Effort

IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies

IEEE Transactions on Software Engineering
An efficient method to construct a radial basis function neural network classifier

Neural Networks
Software Engineering Economics

Software Engineering Economics
Data Mining: Introductory and Advanced Topics

Data Mining: Introductory and Advanced Topics
A Comparison of Development Effort Estimation Techniques for Web Hypermedia Applications

METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Estimating Software Project Effort by Analogy Based on Linguistic Values

METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
General C-Means Clustering Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast learning in networks of locally-tuned processing units

Neural Computation
Conditional fuzzy clustering in the design of radial basis function neural networks

IEEE Transactions on Neural Networks

Systematic literature review of machine learning based software development effort estimation models

Information and Software Technology
Towards an early software estimation using log-linear regression and a multilayer perceptron model

Journal of Systems and Software
Functional Link Artificial Neural Networks for Software Cost Estimation

International Journal of Applied Evolutionary Computation
Radial basis function network using intuitionistic fuzzy C means for software cost estimation

International Journal of Computer Applications in Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Radial Basis Function Neural Networks (RBFN) have been recently studied due to their qualification as an universal function approximation. This paper investigates the use of RBF neural networks for software cost estimation. The focus of this study is on the design of these networks, especially their middle layer composed of receptive fields, using two clustering techniques: the C-means and the APC-III algorithms. A comparison between a RBFN using C-means and a RBFN using APC-III, in terms of estimates accuracy, is hence presented. This study uses the COCOMO'81 dataset and data on Web applications from the Tukutuku database.