Software effort prediction using unsupervised learning (clustering) and functional link artificial neural networks

• Published in: 2012 World Congress on Information and Communication Technologies pp. 115 - 120
• Main Authors: Benala, T. R., Dehuri, S., Mall, R., ChinnaBabu, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2012
• Subjects: Algorithm design and analysis; Approximation algorithms; Clustering algorithms; Density-Based Spatial Clustering of Application with Noise (DBSCAN); Estimation; Functional Link Artificial Neural Network (FLANN); Software; Software Cost Estimation (SCE); Training; Unsupervised K-Window Clustering (UKW); Unsupervised learning
• ISBN: 1467348066; 9781467348065
• DOI: 10.1109/WICT.2012.6409060

Software cost estimation continues to be an area of concern for managing of software development industry. We use unsupervised learning (e.g., clustering algorithms) combined with functional link artificial neural networks for software effort prediction. The unsupervised learning (clustering) indigenously divide the input space into the required number of partitions thus eliminating the need of ad-hoc selection of number of clusters. Functional link artificial neural networks (FLANNs), on the other hand is a powerful computational model. Chebyshev polynomial has been used in the FLANN as a choice for functional expansion to exhaustively study the performance. Three real life datasets related to software cost estimation have been considered for empirical evaluation of this proposed method. The experimental results show that our method could significantly improve prediction accuracy of conventional FLANN and has the potential to become an effective method for software cost estimation.