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Reseach Article

Perceptual Evolution for Software Project Cost Estimation using Ant Colony System

by Nikhat Akhtar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 14
Year of Publication: 2013
Authors: Nikhat Akhtar
10.5120/14185-2385

Nikhat Akhtar . Perceptual Evolution for Software Project Cost Estimation using Ant Colony System. International Journal of Computer Applications. 81, 14 ( November 2013), 23-30. DOI=10.5120/14185-2385

@article{ 10.5120/14185-2385,
author = { Nikhat Akhtar },
title = { Perceptual Evolution for Software Project Cost Estimation using Ant Colony System },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 14 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number14/14185-2385/ },
doi = { 10.5120/14185-2385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:03.934885+05:30
%A Nikhat Akhtar
%T Perceptual Evolution for Software Project Cost Estimation using Ant Colony System
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 14
%P 23-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

My proposed work is inspired by the experiment that uses expert judgment for estimation of the cost on the basis of previous project results. In this paper estimator can use Analogical strategies as well as Algorithmic Strategies as they wish. The proposed method is divided into two phases. First phase computed the probability of each selected factors by ant colony system. Second phase combines the value of these factors to calculate the cost overhead for the project by using Bayesian belief network. Once this overhead is computed productivity is directly calculated which can be converted in effort and cost. Our computation gives the Cost Overhead that depends on various factors. Till date Ant Colony Optimization Algorithm has provided solutions for the problems that have multiple solution and user are interested in best solution. This algorithm provides a proper heuristic for the problem and computes the best possible solution. It gives the solutions in terms of probability, i. e. The most likely occurred solution and the best solution. It was first introduced in Travelling Salesman Problem for finding the minimum cost path. We have mapped our problem in a simple graph by using a questionnaire. That gives the minimum length path, the path that obtains minimum deviation from the nominal project for each factor and theirs encouraging results from proposed technique.

References
  1. Barry Boehm, Chris Abts, "Software Development Cost Estimation Approaches – A Survey1" , University of Southern California Los Angeles, CA 90089-0781 Sunita Chulani IBM Research 650 Harry Road, San Jose, CA 95120.
  2. M. Dorigo, V. Maniezzo, and A. Colorni, " Ant System: Optimization by a colony of cooperating agents" IEEE Transactions on Systems, Man, and Cybernetics – Part B, vol. 26, no. 1, pp. 29–41, 1996.
  3. K. M. Sim and W. H. Sun, 2003, "Ant colony optimization for routing and load-balancing: Survey and new directions " IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans.
  4. V. A. Cicirello and S. F. Smith, 2001, "Ant colony control for autonomous decentralized shop floor routing " in Proceedings of the International Symposium on Autonomous Decentralized Systems. IEEE Computer Society Press, pp. 383–390.
  5. Briand, L. C. , Wieczorek, I. "Software Resource Estimation" , Marciniak J. J. (ed. ), Encyclopedia of Software Engineering, vol. 2, John Wiley & Sons, 2002, pp. 1160-1196.
  6. Doerner K, Gutjahr WJ, Hartl RF, Strauss C, Stummer C. Pareto, " ant colony optimization: A metaheuristic approach to multiobjective portfolio selection " , Ann Oper Res 2004 , vol 131, pp 79–99.
  7. Dorigo M, Stützle T. Ant Colony optimization. Cambridge, MA: MIT Press; 2004.
  8. T. St utzle and H. H. Hoos, "Improving the Ant System: A detailed report on the MAX–MIN Ant System" FG Intellektik, FB Informatik, TU Darmstadt, Germany, Tech. Rep. AIDA–96–12, Aug. 1996.
  9. Steven S. Vicinanza, Tridas Mukhopadhyay, Michael J. Prietula, "Software-Effort Estimation: An Exploratory Study of Expert Performance", Energy Management Associates, Inc. 100 Northcreek Atlanta, Georgia S0327
  10. Lionel C. Briand, Khaled El Emam, and Frank Bomarius Fraunhofer, "COBRA: A Hybrid Method for Software Cost Estimation, Benchmarking, and Risk Assessment", IESE Sauerwiesen 6D-67661 Kaiserslautern,Germany.
  11. Jones, Capers, "Applied Software Measurement: Assuring Productivity and Quality". 2ed. McGrraw-Hill, 1996.
  12. Steven S. Vicinanza,Tridas Mukhopadhyay, Michael J. Prietula, "Software-Effort Estimation: An Exploratory Study of Expert Performance", Energy Management Associates, Inc. 100 Northcreek Atlanta, Georgia S0327.
  13. Yunsik Ahn, Jungseok Suh, Seungryceol Kim, Hyunsoo Kim, April 2003, "The Software maintenance project effort estimation model based on function points", Journal of Software Maintenance & Evolution: Research and Practice, vol 15, issue 2.
  14. Tridas Mukhopadhyay, Steven S. Vicinanza and Michael J. Prietula, "Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation", MIS Quarterly, Vol. 16, No. 2 June, 1992.
  15. L. M. Gambardella and M. Dorigo, "Solving symmetric and asymmetric TSPs by ant colonies," in Proc. IEEE International Conference on Evolutionary Computation (ICEC'96),T. Baeck et al. , Eds. IEEE Press, Piscataway, NJ, pp. 622–627, 1996.
  16. M. Reimann, K. Doerner, and R. F. Hartl, 2004, "D-ants: Savings based ants divide and conquer the vehicle routing problem," Computers & Operations Research, vol. 31, no. 4, pp. 563–591.
Index Terms

Computer Science
Information Sciences

Keywords

Cost Estimation Bayesian network Ant Colony Algorithmic-Estimation Strategy Optimization Swarm Intelligence.