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Reseach Article

Comparative Analysis of COCOMO81 using Various Fuzzy Membership Functions

by Pooja Jha, K. S. Patnaik
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 14
Year of Publication: 2012
Authors: Pooja Jha, K. S. Patnaik
10.5120/9350-3676

Pooja Jha, K. S. Patnaik . Comparative Analysis of COCOMO81 using Various Fuzzy Membership Functions. International Journal of Computer Applications. 58, 14 ( November 2012), 20-27. DOI=10.5120/9350-3676

@article{ 10.5120/9350-3676,
author = { Pooja Jha, K. S. Patnaik },
title = { Comparative Analysis of COCOMO81 using Various Fuzzy Membership Functions },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 14 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number14/9350-3676/ },
doi = { 10.5120/9350-3676 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:30.461229+05:30
%A Pooja Jha
%A K. S. Patnaik
%T Comparative Analysis of COCOMO81 using Various Fuzzy Membership Functions
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 14
%P 20-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software Estimation has always been one of the prompting challenges for the software engineers. Software cost estimation techniques helps in forecasting the amount of effort required to develop software. Constructive Cost Model (COCOMO) is considered to be the most widely used model for effort estimation. Cost drivers have great influence on the COCOMO and this paper investigates the role of cost drivers in improving the precision of effort estimation using different membership functions. Fuzzy logic-based estimation models are more suitable when formless and inaccurate information is to be used. The proposed fuzzy COCOMO model consists of a collection of linear sub-models joined together smoothly using fuzzy membership functions. This paper focus on the comparative analysis of COCOMO81 using various fuzzy membership functions. The present work is based on COCOMO81 dataset and the experimental part of the study illustrates the approach and compares it with the standard version of the COCOMO81. It has been found that Fuzzy based COCOMO model gives better performance when compared to the ¬COCOMO81, demonstrating a smoother transition in its intervals, and the achieved results were closer to the actual effort.

References
  1. Satyananda Reddy, KVSVN Raju, " An Improved Fuzzy Approach for COCOMO's Effort Estimation using Gaussian Membership Function", Vol. (4), No. (5), July 2009.
  2. Boehm, B. W. , Software Engineering Economics, Prentice-Hall, Englewood Cliffs, NJ (1981).
  3. Boehm B. W. , Royce, W. W. , Le COCOMO Ada, Genielogiciel & Systemes experts, 1989.
  4. Boehm, B, W. , et al, "Cost models foe future software life cycle processes: COCOMO2. 0", Annals of Software Engineering on Software process and Product Measurement, Amsterdam, 1995.
  5. Walston, C. E. , Felix, A. P, "A method of programming measurement and estimation", IBM Systems Journal, Vol. 16, No. 1, 1997.
  6. Putnam, L. H. , "A general empirical solution to the macro software sizing and estimation problem", IEEE Transaction on Software Engineering, No. 4, July 1978.
  7. Jones, C. , Programming Productivity, McGraw-Hill, New York, 1986.
  8. Alberta's. , Gaffney , J. E. , "Software function, Source lines of code, and development effort prediction: A Software science validation", IEEE transactions on Software Engineering, Vol. SE-9, No. 6, Nov, 1983, pp 639-647.
  9. Matson, J. E. , Barrett, B. E. , Mellichamp, J. M. , "Software development cost estimation using function points", IEEE Transactions on Software Engineering, Vol. 20, No. 4, Apr. , 1994, pp. 275-287.
  10. Leonard J. Jowers A, james j. Buckley B, Kevin D. Reilly A, "Estimation of f-COCOMO model parameters using optimization techniques", http://sunset. usc. edu/events/2006/CIIForum/pages/presentations/2006SEWorld Jowers-Buckley-Reilly-c. pdf.
  11. J. Ryder, "Fuzzy modeling of software effort prediction", Proceedings of IEEE Information Technology Conference, Syracuse, NY, 1998.
  12. Z. Fei, and X. Liu, "f-COCOMO: fuzzy constructive cost model in software engineering", Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE Press, New York, 1992 pp. 331–337.
  13. K. Srinivasan, and D. Fisher, "Machine learning approaches to estimating software development effort", IEEE Transactions on Software Engineering, 21(2) 1995.
  14. B. Boehm, C. Abts, and S. Chulani, "Software development cost estimation approaches—a survey", Technical Reports, USC-CSE-2000505, University of Southern California Center for Software Engineering, 2000.
  15. A. C. Hodgkinson, and P. W. Garratt, "A neurofuzzy cost estimator", in: Proceedings of the Third International Conference on Software Engineering and Applications—SAE 1999, pp. 401–406.
  16. C. Schofield, "Non-algorithmic effort estimation techniques", Technical Reports, Department of Computing, Bournemouth University, England,TR98-01, March 1998.
  17. C. Kirsopp, and M. J. Shepperd, "Making inferences with small numbers of training sets", Sixth International Conference on Empirical Assessment & Evaluation in Software Engineering, Keele University, Staffordshire, UK, 2002.
  18. A. Idri, and A. Abran, "COCOMO cost model using fuzzy logic", Seventh International Conference on Fuzzy Theory and Technology, Atlantic City, NJ, 2000.
  19. P. Musilek, W. Pedrycz, G. Succi, and M. Reformat, "Software cost estimation with fuzzy models", Applied Computing Review, 8(2) 2000 24–29.
  20. G. D. Boetticher, "An assessment of metric contribution in the construction of a neural network-based effort estimator", Proceedings of Second International Workshop on Soft Computing Applied to Software Engineering, 2001.
  21. C. J. Burgess, and M. Lefley, "Can genetic programming improve software effort estimation? A comparative evaluation", Information and Software Technology, 43 2001, pp. 863–873.
  22. M. Shepperd, and G. Kadoda, "Comparing software prediction techniques using simulation", IEEE Transactions on Software Engineering, 27(11) 2001, pp. 1014–1022.
  23. B. K. Clark, "The Effects of Software Process Maturity on Software Development Effort", PhD Dissertation, Faculty of Graduate School, University of Southern California, 1997.
  24. Harsh Kumar Verma, Vishal Sharma , "Handling Imprecision in Inputs using Fuzzy Logic to Predict Effort in Software Development", IEEE 2nd International Advance Computing Conference, 2010, pp. 436-442.
  25. Ch. Satyananda Reddy, KVSVN Raju, "Improving the accuracy of effort estimation through Fuzzy set combination of size and cost drivers", WSEAS TRANSACTIONS on COMPUTERS, Vol(8),Issue (6), pp. 926-936, June 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Software cost estimation COCOMO81 EAF Fuzzy logic Membership Functions