Title: A hybrid approach for software cost estimation using polynomial neural networks and intuitionistic fuzzy sets
Authors: Anupama Kaushik; A.K. Soni; Rachna Soni
Addresses: Department of Information Technology, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, 110058, India ' School of Engineering and Technology, Sharda University, Greater Noida 201306, India ' Department of C.S. and Applications, DAV College, Yamunanagar 135001, India
Abstract: Software cost estimation (SCE) is an important and critical activity of any software development organisation. It helps the project managers to effectively manage their projects and prevent them from over budgeting. In this study we introduce a new design methodology for software cost estimation using polynomial neural networks (PNNs) and intuitionistic fuzzy sets which resulted in improved SCEs. The performance of the proposed model is tested through a series of experiments on three publicly available software development data, i.e., COCOMO81, NASA93, and Maxwell datasets. The proposed technique of using IFCM (intuitionistic fuzzy C Means) along with PNNs has drastically improved the cost estimations in comparison with the use of fuzzy C means (FCM) with PNN as reported in the literature.
Keywords: SCE; software cost estimation; PNN; polynomial neural networks; fuzzy clustering; intuitionistic fuzzy sets; fuzzy C means; fuzzy clustering; software development.
International Journal of Computer Applications in Technology, 2015 Vol.52 No.4, pp.292 - 304
Available online: 13 Dec 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article