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Tuning of COCOMO II Model Parameters for Estimating Software Development Effort using GA for PROMISE Project Data Set

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 1
Year of Publication: 2014
Chandra Shekhar Yadav
Raghuraj Singh

Chandra Shekhar Yadav and Raghuraj Singh. Article: Tuning of COCOMO II Model Parameters for Estimating Software Development Effort using GA for PROMISE Project Data Set. International Journal of Computer Applications 90(1):37-43, March 2014. Full text available. BibTeX

	author = {Chandra Shekhar Yadav and Raghuraj Singh},
	title = {Article: Tuning of COCOMO II Model Parameters for Estimating Software Development Effort using GA for PROMISE Project Data Set},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {1},
	pages = {37-43},
	month = {March},
	note = {Full text available}


In this paper, we have tuned the parameters of COCOMO II model to estimate the software development effort using genetic algorithm (GA). Results obtained by applying GA are have been compared with results obtained by applying particle swarm optimization (PSO) published in previous paper. COCOMO II model is modified by introducing some more parameters to predict the software development effort more precisely. The performance of this parametric model is tested on the past PROMISE and NASA projects data set.


  • Alaa F. Sheta 2006 Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects. Journal of Computer Science 2(2) Science Publication pp 118-123
  • Alaa F. Sheata, Alaa Al-Afeef 2010. A GP Effort Estimation Model Utilizing Line of Code and Methodology for NASA Software Projects" 978-4244-8136-1 IEEE Transaction
  • Alaa F. Sheta et al. 1996. Parameter estimation of nonlinear systems in noisy environment using genetic algorithms. Proc. IEEE Intl. Symp. Intelligent Control(ISIC'96), pp:360-366
  • Anil Kumar, C. S. Yadav et al 2012. Parameter tuning of COCOMO Model for software effort estimation using PSO. ICIAICT ISBN 978-93-81583-34-0 pp 99-105
  • K. K. Shukla 2000. Neuro-genetic prediction of software development effort. Information and Software Technology 42 701-713 ELSEVIER
  • Boehm,B. 1981. Software Engineering Economics. Englewood Cliffs NJ Prentice Hall.
  • Sehra S. K. et al 2011. Soft Computing Techniques for Software project Effort Estimation. International Journal of Advanced Computer and Mathematical Science, ISSN 2230-9624, vol-2 Issue 3, pp 160-167
  • Sultaan Aljahdali, Alaa F. Sheta 2010. Software Effort Estimation by Tuning COCOMO Model parameters using differential evolutions.
  • Holland, J. H. "Adaptation in Neural and Artificial Systems", University of Michigan Press, Ann Arbor, MI, 1975.
  • Data Web Site: http://promise. site. uottawa. ca /SERepository/datasets/cocomo81. arff
  • Chandra Shekhar Yadav et al. 2014. Reliability of Object oriented system using vague lambda-tau modeling. International journal of Fuzzy computation and modeling. InderScience Publishers [in Press].
  • Idri et al. Investigating Soft Computing in Case-Based Reasoning for Software Cost Estimation. Engineering Intelligent Systems for Electrical Engineering and Communications, 2002 10(3): p. 147-157.
  • Hakutta et al. A Software Size Estimation Model and Its Evaluation. Journal of Systems and Software, 1997. 37(3): p. 253-263.
  • Shepper. et al. 1997. Estimating software project effort using analogies. IEEE Tran. Software Engg. 23; 736-743
  • H. Garg et al. Behavior Analysis of Pulping Unit in a paper mill with Weibull Fuzzy Distribution Function using ABCBLT Technique. Int. J. of Appl. Math. And Mech. 8(4):86-96, 2012.
  • A. Doostparast et al. A Fuzzy Approach to Sequential Failure Analysis Using Petri nets. 2010 21(2) pp. 53-60 IJIE
  • Naveen Kumar et al. Reliability analysis of waste clean-up manipulator using genetic algorithms and fuzzy methodology. Computers & Operations Research 39(2012) 310-319.
  • Somesh et al. Hybrid evolutionary techniques in feed forward neural network with distributed error for classification of handwritten Hindi 'SWARS' 2013 Connection Science vol. 25, No. 4, 197-215 Taylor & Francis.
  • Baily, J. W. et al. A meta model for software development resource expenditure" Proc. Int. Conf. Software Engineering, pp. 107-115 1981.
  • Kennedy, J. and Eberhart, R. Particle Swarm Optimization. Proceeding of the fourth IEEE international Conference on Neural Networks, Perth, Australia IEEE Service Center (1995)
  • Kennedy, J. et al. SWARM Intelligence, Morgan Kaufmann, 2001.