• Jul 29, 2019 News!IJMLC Had Implemented Online Submission System, Please Sumbit New Submissions thorough This System Only!   [Click]
  • Jul 16, 2019 News!Good News! All papers from Volume 9, Number 3 have been indexed by Scopus!   [Click]
  • Jul 08, 2019 News!Vol.9, No.4 has been published with online version.   [Click]
General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2012 Vol.2(5):548-551 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.186

Software Development Effort Estimation Using Soft Computing

Sandeep Kad and Vinay Chopra
Abstract—Software development effort estimation is a daunting task that is being carried out by software developers as not much of the information about the software which is to be developed is available during the early stages of development. The information that is to be gathered for various attributes of software needs to be subjective which otherwise leads to imprecision and uncertainty. Inaccurate estimation of the software effort and schedule leads to financial loses and also delays in project deadline. In this paper, we present the use of soft computing technique to build a suitable model which improves the process of effort estimation. To do so, various parameters of Constructive Cost Model (COCOMO) II are fuzzified that leads to reliable and accurate estimates of effort. The results show that the value of Magnitude of Relative Error (MRE) obtained by applying fuzzy logic is quite lower than MRE obtained from algorithmic model. By analyzing the results further it is observed that Gaussian Membership Function (gaussmf) performs better than Triangular Membership Function (trimf) and Trapezoidal Membership Function (trapmf) as the transition from one interval to another is quite smoother. Here varying number of COCOMO II inputs are fuzzified with these membership functions. The validation of the experiment is carried on COCOMO public dataset.

Index Terms—Software cost estimation, COCOMO, soft computing, fuzzy logic.

Sandeep Kad is with the Department of Information Technology, Amritsar College of Engineering and Technology, Amritsar, Punjab, India (e-mail: kadsandeep@yahoo.com).
Vinay Chopra is with the Department of Computer Science and Engineering,DAV Institute of Engineering and Technology, Jalandhar, Punjab, India (e-mail: vinaychopra222@yahoo.co.in).


Cite:Sandeep Kad and Vinay Chopra, "Software Development Effort Estimation Using Soft Computing," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 548-551, 2012.

Copyright © 2008-2019. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net