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Multi Objective Particle Swarm Optimization for Software Cost Estimation

by Prasad Reddy P.V.G.D, Hari CH.V.M.K., Srinivasa Rao T.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 3
Year of Publication: 2011
Authors: Prasad Reddy P.V.G.D, Hari CH.V.M.K., Srinivasa Rao T.
10.5120/3884-5437

Prasad Reddy P.V.G.D, Hari CH.V.M.K., Srinivasa Rao T. . Multi Objective Particle Swarm Optimization for Software Cost Estimation. International Journal of Computer Applications. 32, 3 ( October 2011), 13-17. DOI=10.5120/3884-5437

@article{ 10.5120/3884-5437,
author = { Prasad Reddy P.V.G.D, Hari CH.V.M.K., Srinivasa Rao T. },
title = { Multi Objective Particle Swarm Optimization for Software Cost Estimation },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 3 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number3/3884-5437/ },
doi = { 10.5120/3884-5437 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:11.212481+05:30
%A Prasad Reddy P.V.G.D
%A Hari CH.V.M.K.
%A Srinivasa Rao T.
%T Multi Objective Particle Swarm Optimization for Software Cost Estimation
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 3
%P 13-17
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software Project Management activities are classified as planning, monitoring-control and termination. Planning is the most important activity in project management which defines the resources required to complete the project successfully. Software Cost Estimation is the process of predicting the cost and time required to complete the project. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM’s). In this paper we proposed a model for software cost estimation using Multi Objective (MO) Particle Swarm Optimization. The parameters of model tuned by using MOPSO considering two objectives Mean Absolute Relative Error and Prediction. The COCOMO dataset is considered for testing the model. It was observed that the model gives better results when compared with the standard COCOMO model. It is also observed, when provided with enough classification among training data may give better results.

References
  1. Lawrence H.P, A general empirical solution to the macro software sizing and estimating problem, IEEE Transactions on Software Engineering, Vol. SE-4, NO. 4, , July 1978, pp. 345-361.
  2. Hans-Dieter Joos et.al, A multi-objective optimisation-based software environment for control systems design, 2002 IEEE International symposium computer added control system design proceedings, pp: 7-14, sep 18-20.
  3. Kavita C , GA based Optimization of Software Development effort estimation, IJCST, Vol 1, Issue 1, pp: 38-40, sep -2010.
  4. Daniel Rodríguez, Multi Objective simulation optimization in software project management, ACM 978-1-4503-0557-0/11/07, GECCO’11, July 12–16, 2011.
  5. Marco Laumanns, Evolutionary Multi Objective optimization, International Journal of Computational Intelligence Research, ISSN 0973-1873 Vol.2, 2006.
  6. John W.B and Victor R.B, A meta model for software development resource expenditures, proceedings of the Fifth International Conference on Software Engineering,DOI:CH-1627-9/81/0000/0107500.75@IEEE,1981,pp.107-129.C
  7. Chris F.K, An Empirical Validation of Software Cost Estimation Models, Management Of Computing-Communications of ACM, Vol: 30 No. 5, , May 1987, pp.416-429.
  8. Rajiv D.B and Chris F.K, Scale Economies in New Software Development, IEEE Transactions on Software Engineering, Vol. 15. No. 10, October 1989 pp.1199-1205.
  9. KrishnaMurthy S and Douglas F (1995), Machine Learning Approaches to estimating software development effort, IEEE Transactions on Software Engineering, Vol. 21, No. 2, February 1995, pp. 126-137.
  10. Pedrycz W, Peters J.F, Ramanna S, A Fuzzy Set Approach to Cost Estimation of Software Projects, proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, May 9-12 1999, pp.1068-1073.
  11. Wu B, Zheng Y, Liu S, and Shi Z, CSIM: A Document Clustering Algorithm Based on Swarm Intelligence, DOI:0-7803-7282-4/02@IEEE, 2002, pp.477-482.
  12. Wei P, Kang-ping W, Chun-guang Z, Long-jiang D, Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem, Proceedings of the Fourth International Conference on Computer and Information Technology (CIT’04), DOI:0-7695-2216-5/04, 2004, pp.1-5.
  13. Matthew S, An Introduction to Particle Swarm Optimization, Department of Computer Science, University of Idaho, November 7, 2005, pp.1-8.
  14. Nasser T, Neural Network Approach for Software Cost Estimation, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’05), DOI: 0-7695-2315-3/05IEE, 2005.
  15. Xishi H, Danny H, Jing R, Luiz F. C, Improving the COCOMO model using a neuro-fuzzy approach, DOI:10.1016 /j.asoc. 2005.06.007, Elsevier-Applied Soft Computing 7 (2007) 2005, pp. 29–40.
  16. Ajith A, He G, and Hongbo L, Swarm Intelligence: Foundations, Perspectives and Applications, Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg (SCI), 2006, pp. 3–25.
  17. Alaa F.S, Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects, Journal of Computer Science Vol: 2, No: 2, 2006, pp.118-123.
  18. Felix T. S. Chan and Manoj Kumar Tiwari, (2007), Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, I-TECH Education and Publishing, ISBN 978-3-902613-09-7, pp: 1- 548, 2007.
  19. Harish M and Pradeep B, Optimization Criteria for Effort Estimation using Fuzzy Technique, CLEI Electronic Journal, Vol. 10, Number 1, Paper 2, June 2007, pp. 1-11.
  20. Magne J and Martin S, A Systematic Review of Software Development Cost Estimation Studies, IEEE Transactions On Software Engineering, Vol. 33, No. 1, January 2007, pp. 33-53.
  21. Rahul P and Thomas Z, Building Software Cost Estimation Models using Homogenous Data, First International Symposium on Empirical Software Engineering and Measurement, DOI: 0-7695-2886-4/07, IEEE, 2007, pp. 393-400.
  22. Alaa S, David R and Aladdin A, Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques, IEEE Transaction, 978-1-4244-1823-7/08/IEEE, 2008, pp. 1283-1289.
  23. Prasad Reddy P.V.G.D, Sudha, K.R., Rama S.P, Ramesh .S.N.S.V.S.C, Software Effort Estimation using Radial Basis and Generalized Regression Neural Networks, Journal of Computing, Vol.2 No.5, May 2010, pp.87-92.
  24. Prasad Reddy P.V.G.D, Sudha, K.R., Rama Sree .P, Ramesh .S.N.S.V.S.C, Fuzzy Based Approach for Predicting Software Development Effort, International Journal of Software Engineering, Vol.1 No.1,2010, pp.1-11.
  25. Prasad Reddy P.V.G.D, Particle Swarm Optimization In The Fine Tuning Of Fuzzy Software Cost Estimation Models, International Journal of Software Engineering, Vol.1 No.2,2010, pp. 12-23.
Index Terms

Computer Science
Information Sciences

Keywords

KDLOC-thousands of delivered lines of code PM- person months PSO- particle swarm optimization COCOMO- constructive cost estimation MO- Multi Objective

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