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Software Effort Estimation: A Fuzzy Logic Approach

by Vishal Chandra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 9
Year of Publication: 2014
Authors: Vishal Chandra
10.5120/18106-9210

Vishal Chandra . Software Effort Estimation: A Fuzzy Logic Approach. International Journal of Computer Applications. 103, 9 ( October 2014), 39-43. DOI=10.5120/18106-9210

@article{ 10.5120/18106-9210,
author = { Vishal Chandra },
title = { Software Effort Estimation: A Fuzzy Logic Approach },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 9 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number9/18106-9210/ },
doi = { 10.5120/18106-9210 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:08.459085+05:30
%A Vishal Chandra
%T Software Effort Estimation: A Fuzzy Logic Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 9
%P 39-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are many equation based effort estimation models like Bailey-Basil Model, Halstead Model, and Walston-Felix Model. Effort and Cost estimation are the major concern of any sort of software industry. They are calculated with the help of Kilo Line Code (KLOC) which are the number of, line of code in software. For example if a software contain 1000 lines then it has 1 kilo line of codes. There are several ways to estimate it with various advantages and disadvantages. We can distinguish them in two parts, one is equation based and another is model based estimation techniques. In this paper a KLOC formula is proposed that calculate effort which is integrated with fuzzy logic. These components are vague and fuzzy easily handles them as well. Various fuzzy triangular membership functions are used in this paper. One of the most popular model is COCOMO [5] and its variants, which is mostly used in the industry, along with the other variants such as fuzzy approach, neuro fuzzy approach and cost driver based estimations. There are differences between Equation Based and Model Based effort estimation techniques, model based built on specific model, like architecture & available resources whereas equation based techniques follows some back- ground equations.

References
  1. Baik, J. (2000) 'The effect of case tools on software development effort', PhD thesis, University of Southern California, CA, USA.
  2. Bailey, J. W. and Basili, V. R. (1981) 'A meta model for software Development resource expenditure', in Proceedings of The International Conference on Software Engineering Pp. 107–115.
  3. Benediktsson, O. , Dalcher, D. , Reed, K. and Woodman, M. (2003) 'COCOMO based effort estimation for iterative and Incremental software development', Software Quality Journal, Vol. 11, pp. 265–281. Birge, B. (2005) PSOt, Particle Swarm Optimization Toolbox for Matlab, web site www. matlworks. com.
  4. Boehm, B. (1981) Software Engineering Economics, Prentice-Hall, Englewood Cliffs, NJ. Boehm, B. (1995) 'Cost models for future software life cycle process: COCOMO2', Annals of Software Engineering.
  5. Boehm, B. , et al. (2000) Software Cost Estimation with COCOMOII, Prentice Hall PTR. Boraso, M. , Montangero, C. and Sedehi, H. (1996) Software Cost Estimation: An Experimental Study of Model Performances, Technical report.
  6. Briand, L. C. , Emam, K. E. and Wieczorek, I. (1999) 'Explaining The cost of European space and military projects', in ICSE 99: Proceedings of the 21st International Conference on Software Engineering, pp. 303–312, IEEE Computer Society Press, Los Alamitos, CA, USA.
  7. Chulani, S. and Boehm, B. (1999) Modeling Software Defect Introduction and Removal: Coqualmo (Constructive QualitybModel), Technical report, Technical Report USC-CSE-99-b510, University of Southern California, Center for Software Engineering.
  8. Chulani, S. , Boehm, B. and Steece, B. (1999) 'Calibrating software cost models using Bayesian analysis', IEEE Trans. Software Engr. , July–August 1999, pp. 573–583.
  9. Clark, B. , Devnani-Chulani, S. and Boehm, B. (1998) 'Calibrating the COCOMO II post-architecture model', in Proceedings of the 20th International Conference on Software Engineering (ICSE '98), pp. 477–480, IEEE Computer Society, Washington, DC, USA.
  10. Dolado, C. J. and Lefley, M. (2001) 'Can genetic programming improve software effort estimation? A comparative Evaluation', Information and Software Technology, Vol. 43, Pp. 863–873.
  11. Dubois, D. and Prade, H. (1992) 'Fuzzy sets in approximate Reasoning: part 1', Fuzzy Sets and Systems, Vol. 40, Pp. 143–202. Group, T. S. (1995) 'CHAOS chronicles', PhD thesis, Standish Group Internet Report.
  12. Gustafson, D. E. and Kessel, W. C. (1979) 'Fuzzy clustering with a fuzzy covariance matrix', in Proceedings of the IEEE CDC, San Diego, CA, USA, pp. 761–766.
  13. Hodgkinson, A. C. and Garratt, P. W. (1999) 'A neurofuzzy cost Estimator', in Proceedings of the Third Conference ond Software Engineering and Applications, pp. 401–406. Kemere, C. F. (1987) 'An empirical validation of software Cost estimation models', Communication ACM, Vol. 30, Pp. 416–429.
  14. Kosko, B. (1998) 'Fuzzy systems as universal approximators', in Proceedings of Int. Conf. Fuzzy Syst. , Pp. 1153.
Index Terms

Computer Science
Information Sciences

Keywords

Empirical Equation fuzzy logic effort estimation membership functions KLOC.

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