International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.5, No.5, Apr. 2013
An Analysis of Fuzzy Approaches for COCOMO II
Full Text (PDF, 735KB), PP.68-75
Software cost estimation is one of the most challenging task in project management. However, the process of estimation is uncertain in nature as it largely depends upon some attributes that are quite unclear during the early stages of development. In this paper a soft computing technique is explored to overcome the uncertainty and imprecision in estimation. The main objective of this research is to investigate the role of fuzzy logic technique in improving the effort estimation accuracy using COCOMO II by characterizing inputs parameters using Gaussian, trapezoidal and triangular membership functions and comparing their results. NASA (93) dataset is used in the evaluation of the proposed Fuzzy Logic COCOMO II. After analyzing the results it had been found that effort estimation using Gaussian member function yields better results for maximum criterions when compared with the other methods.
Cite This Paper
Ashita Malik, Varun Pandey, Anupama Kaushik,"An Analysis of Fuzzy Approaches for COCOMO II", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.5, pp.68-75, 2013.DOI: 10.5815/ijisa.2013.05.08
S.G. MacDonell, and A. R. Gray, “A comparison of techniques for software development effort prediction”, International Conference on Neural Information Processing and Intelligent Control Systems, NewZealand, 1997, pp. 1-4.
A.C. Hodgkinson, and P.W. Garratt, “ A neuro fuzzy cost estimator”, Proceedings of Third International Conference on Software Engineering and Applications, 1999, pp. 401-406.
Mockus A., Weiss D.M. and Zhang P. “Understanding and Predicting Efforts in Software Projects”, IEEE Proceedings of 25th International Conference on Software Engineering (ICSE’03), pp. 274-84.
I. Somerville, Software Engineering, 6th ed., Addison– Wesley Publishers Limited, 2001.
B. W. Boehm, Software Engineering Economics, Englewoods Cliffs, NJ,Prentice-Hall, 1981.
B.Boehm, C. Abts, S.Chulani,”Software Development Cost Estimation Approaches: A Survey,” University of Southern California Centre for Software Engineering, Technical Report, USC-CSE-2000-505, 2000.
L.H. Putnam, “A general empirical solution to the macro software sizing and estimating problem”, IEEE transactions on Software Engineering, 1978, Vol. 2, pp. 345- 361.
Hodgkinson, A.C. and P.W. Garratt, “ A neuro fuzzy cost estimator, ” Proceedings of the 3rd International Conference on Software Engineering and Applications,(SEA’99), pp.401-406.
Burgess C.J. and Lefley M., “Can genetic programming improve software effort estimation? A comparative evaluation”, Information and Software Technology, 2001, Vol. 43, No. 14, pp. 863 -873.
A. Idri, A. Abrian, and L. Kjiri, “COCOMO Cost Model using Fuzzy Logic”, International Conference on Fuzzy Theory and Technology Atlantic, New Jersey, 2000.
B. Boehm, B. Clark, E. Horwitz, R. Madachy, C. Abts, S.Chulani, A.W.Brown and B. Steece, “COCOMO II model definition manual”, Universityof South California Center for Software Engineering, 2000.
M. Jorgenson and D.I.K. Sjoberg, “The impact of customer expectation on software development effort estimates”, International Journal of Project Management,2004, Vol. 22, No. 4, pp. 317-325
Zadeh. L. A., Fuzzy Sets, Information and Control, 1965, Vol. 8, pp. 338-353.