Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm
Arfiha Khatoon1 , Rupinder Kaur2
- Dept. of CSE, Jayoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India.
- Dept. of CSE, Jayoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India.
Section:Research Paper, Product Type: Journal Paper
Volume-6 ,
Issue-5 , Page no. 221-226, May-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i5.221226
Online published on May 31, 2018
Copyright © Arfiha Khatoon, Rupinder Kaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Arfiha Khatoon, Rupinder Kaur, “Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.221-226, 2018.
MLA Style Citation: Arfiha Khatoon, Rupinder Kaur "Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm." International Journal of Computer Sciences and Engineering 6.5 (2018): 221-226.
APA Style Citation: Arfiha Khatoon, Rupinder Kaur, (2018). Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm. International Journal of Computer Sciences and Engineering, 6(5), 221-226.
BibTex Style Citation:
@article{Khatoon_2018,
author = {Arfiha Khatoon, Rupinder Kaur},
title = {Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {221-226},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1966},
doi = {https://doi.org/10.26438/ijcse/v6i5.221226}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.221226}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1966
TI - Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - Arfiha Khatoon, Rupinder Kaur
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 221-226
IS - 5
VL - 6
SN - 2347-2693
ER -
VIEWS | XML | |
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Abstract
Software cost estimation is very important in software project management.a major cause of failure of many software projects is the lack of accurate and early estimation. However, irrespective of great deal of importance estimating the time and development cost accurately is still a challenge in software industry. It is used to predict the effort and time need to complete the project. The need of optimization comes in various approaches like genetic algorithm of COCOMO MODEL II for providing better effort estimates and reliability.
Key-Words / Index Term
Genetic Algorithm, Optimization, Evolutionary Algorithms
References
[1] Boehm, B., 1995. Cost Models for Future Software Life Cycle Process: COCOMO2Annals of Software Engineering.
[2] S. Bhatia, A. Bawa, and V. K. Attri, “A Review on Genetic Algorithm to deal with Optimization of Parameters of Constructive Cost Model,”International Journal of Advanced Research in Computer and Commu-nication Engineering, vol. 4, no. 4, April 2015.
[3] S. A. Alaa F. Sheta, “Software effort estimation inspired by cocomo and fp models: A fuzzy logic approach,” International Journal of Advanced Computer Science and Applications, vol. 4, no. 11, pp. 192–197, 2013.
[4] Barry Boehm. Software Engineering Economics. Englewood Cliffs, NJ:PrenticeHall,1981. ISBN 0-13-8221227
[5] B. K. Singh and A. K. Misra, “Software Effort Estimation by GeneticAlgorithm Tuned Parameters of Modified Constructive Cost Model for NASA Software Projects,” International Journal of Computer Applica-tions, vol. 59, no. 9, pp. 22–26, Dec 2012.
[6] Bailey, J. W. and V. R. Basili, 1981. A meta model for software development resource expenditure. Proc. Intl. Conf. Software Engineering, pp: 107-115.
[7] K. Tang, K. Man, S. Kwong, and Q. He, “Genetic algorithms and their applications,” Signal Processing Magazine, IEEE, vol. 13, no. 6, pp.22–37, 1996.
[8] Ekrem Kocaguneli, Tim Menzies, and Jacky Keung “On the Value of Ensemble Effort Estimation”, Journal of IEEE Transactions on Software Engineering, Vol. X, 2012.
[9] X. Huang, J. Ren and L.F. Capretz, (2004), “A Neuro- Fuzzy. Tool for Software Estimation”, Proceedings of the 20th IEEE International Conference on Software Maintenance, pp. 520.
[10] Anna Galinina1, Olga Burceva2, Sergei Parshutin3, 1-3Riga Technical University “The Optimization of COCOMO Model CoefficientsUsing Genetic Algorithms”2015.
[11] Sheta. A. and K. DeJong, 1996. Parameter estimation of nonlinear systems in noisy environment using genetic algorithms. Proc. IEEE Intl. Symp. Intelligent Control (ISIC’96), pp: 360-366.
[12] Alaa Sheta., (2006), “Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects”, Journal of Computer Science 2 (2): 118 123.
[13] Dolado, J.J., 2001. On the problem of the software cost function. Information and Software Technology, 43: 61-72.
[14] Houck, C., J. Joines and M. Kay, 1996. A genetic algorithm for function optimization: A Matlab implementation. ACM Transactionson Mathmatical Software.
[15] Dr. Devesh Kumar Srivastava “Measure Performance of VRS Model using Simulation Approach by Comparing COCOMO Intermediate Model in Software Engineering” International Journal of Computer Science & Engineering Technology (IJCSET)2013.
[16] Kristinsson. K. and G. Dumont, 1992. System identification and control using geneti algorithms. IEEE Transaction on Systems, Man and Cybernetics, 22: 1022-1046.
[17]. Fonseca, C., E. Mendes, Fleming and S.A. Billings, 1993. Nonlinear model term selection with genetic algorithms. Proc. IEE/IEEE Workshop on Natural Algorithms in Signal Process., pp: 27/1 –27/8.
[18] L. H. Putnam, (1987), “A general empirical solution to the macrosoftware sizing and estimating problem”. IEEE Transactions on Software Engineering, SE-4(4) pp 345-361.
[19] A. Idri, T. M. Khoshgoftaar, A. Abran, (2002), “Can neural networks be easily interpreted in software cost estimation?”, IEEE Trans. Software Engineering, Vol. 2, pp.1162 –1167.
[20] Boraso, M., C. Montangero and H. Sedehi, 1996. Software cost estimation: An experimental study of model performances. Tech-nical Report TR-96-22, Departimento Di Informatatica, Uni-versita Di Pisa, Italy.
[21] Belkacem Mahdad, Tarek Bouktir and Kamel Srairi. (2008).Optimal power Flow of the Algerian Network using Genetic Algorithms/Fuzzy Rules, Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1-8.
[22] Bhim Singh, B P Singh and (Ms) K Jain. (2003). Implementation of DSP Based Digital Speed Controller for Permanent Magnet Brushless dc Motor, IE(I) Journal-EL, 84, pp.16-21.
[22] B Mrozek, Z Mrozek. (2000). Modeling and Fuzzy Control of DC Drive, in Proceedings of 14th European Simulation Multi conference, pp 186-190.
[23] Chu-Kuei Tu and Tseng-Hsien Lin. (2000). Applying Genetic Algorithms On Fuzzy Logic System For Underwater Acoustic Signal Recognition, Proceedings of the 2000 International Symposium on Underwater Technology, pp. 405-410.
[24] Ching-Hung Wang, Tzung-Pei Hong, and Shian-Shyong Tseng. (1998). Integrating Fuzzy Knowledge by Genetic Algorithms, IEEE Transactions On Evolutionary Computation,2(4), pp.138-149.
[25] P. K. Nandam, and P. C. Sen. (1986). A comparative study of oportional-integral (P-I) and integral-proportional (I-P) controllers for dc motor drives, Int. Jour. Of Control,44, pp. 283-297.
[26] Raj Subbu, Arthur C. Sanderson and Piero P. Bonissone. (1998). Fuzzy Logic Controlled Genetic Algorithms versus Tuned Genetic Algorithms: An Agile Manufacturing Application, Proceedings of the Intelligent Control (ISIC), IEEE Joint Conference, pp.434-440.
[27] S. H. Yakhchali and S. H. Ghodsypour. (2008). A Hybrid Genetic Algorithms for Computing the Float of an Activity in Networks withImprecise Durations, Proceedings of the IEEE International Conference on Fuzzy Systems, pp.1789-1794.