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Software Cost Estimation using Fuzzy Logic

Published on April 2012 by Ravishankar. S, P. Latha
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International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 7
April 2012
Authors: Ravishankar. S, P. Latha
d4ba5525-67e5-4404-9089-4dd7da541ce0

Ravishankar. S, P. Latha . Software Cost Estimation using Fuzzy Logic. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 7 (April 2012), 38-42.

@article{
author = { Ravishankar. S, P. Latha },
title = { Software Cost Estimation using Fuzzy Logic },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 7 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 38-42 },
numpages = 5,
url = { /proceedings/icon3c/number7/6055-1056/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A Ravishankar. S
%A P. Latha
%T Software Cost Estimation using Fuzzy Logic
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 7
%P 38-42
%D 2012
%I International Journal of Computer Applications
Abstract

The process of estimating time and cost required for developing software is called software cost estimation. It is one of the steps to be carried out in project planning. Early software estimation models are based on regression analysis or mathematical derivations. Today's models are based on simulation, neural network, genetic algorithm, soft computing, fuzzy logic modeling etc. This paper aims to utilise an adaptive fuzzy logic model to improve the accuracy of software time and cost estimation. Using advantages of fuzzy set and fuzzy logic can produce accurate software attributes which result in precise software estimates. 63 Historic projects of NASA dataset having COCOMO format is used in the evaluation of the proposed Fuzzy Logic COCOMO II. Eight membership functions available in fuzzy logic are used and a comparison is made to find out which membership function yields better result in terms of Mean Magnitude of Relative Error (MMRE) and PRED (25%).

References
  1. Iman Attarzadeh and Siew Hock Ow," Improving Estimation Accuracy of the COCOMO II Using an Adaptive Fuzzy Logic Model" IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, June 27-30, 2011.
  2. Mohd. Sadiq, Farhana Mariyam, Aleem Ali, Shadab Khan, Pradeep Tripath, "Prediction of Software Project Effort Using Fuzzy Logic" IEEE International Conference on Fuzzy Systems, March 2011.
  3. Mohd. Sadiq, Abdul Rahman, Shabbir Ahmad, Mohammad Asim, Javed Ahmad", esrcTool: A Tool to Estimate the Software Risk and Cost", IEEE Second International Conference on Computer Research and Development, pp. 886-890, July 2010.
  4. Prasad Reddy P. V. G. D, Sudha K. R , Rama Sree P & Ramesh S. N. S. V. S. C,"Fuzzy Based Approach for Predicting Software Development", International Journal of Software Engineering (IJSE), Volume (1): Issue (1).
  5. L. H. Putnam, "A general empirical solution to the macro software sizing and estimating problem," IEEE Transactions on Software Engineering, 4(4), pp. 345 – 361, 1978. problem", IEEE Transactions on Software Engineering,4(4),pp. 345-361,1978.
Index Terms

Computer Science
Information Sciences

Keywords

Software Cost Estimation Models Cocomo Ii Soft Computation Techniques Fuzzy Logic Membership Function Mean Relative Error Pred (25%).