Title: Radial basis function network using intuitionistic fuzzy C means for software cost estimation
Authors: Anupama Kaushik; A.K. Soni; Rachna Soni
Addresses: Department of Information and Technology, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi 110058, India ' Department of Information and Technology, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi 110058, India ' Department of Information and Technology, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi 110058, India
Abstract: Software development has become an important activity for many modern organisations. Software engineers have become more and more concerned about accurately predicting the cost and quality of software product under development. In the last few decades many software cost estimation models have been developed but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper we propose the use of Radial Basis Function Network (RBFN) for software cost estimation using Intuitionistic Fuzzy C Means (IFCM) with Gaussian potential functions. This technique selects the most desirable cluster centres, thereby increasing the clustering accuracy which results in improved software cost estimations. A comparison of RBFN using IFCM, Fuzzy C Means (FCM) and conventional COCOMO model is presented. The datasets used in our study are the COCOMO81 dataset and NASA93 dataset. Experimental results are given to show the effectiveness of the proposed method.
Keywords: software cost estimation; radial basis function; RBF neural networks; fuzzy clustering; IFCM; intuitionistic fuzzy C means; software development; Gaussian potential functions.
International Journal of Computer Applications in Technology, 2013 Vol.47 No.1, pp.86 - 95
Published online: 02 Dec 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article