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Particle Swarm Optimization in the fine-tuning of Fuzzy Software Cost Estimation Models
Prasad Reddy P.V.G.D
Pages - 12 - 23     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 1   Issue - 2    |    Publication Date - July / August 2010  Table of Contents
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KEYWORDS
Particle Swarm Optimization Algorithm (PSOA), , Fuzzy Estimate,, software cost estimation, Effort Estimation,
ABSTRACT
Software cost estimation deals with the financial and strategic planning of software projects. Controlling the expensive investment of software development effectively is of paramount importance. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Fuzzy logic is one such technique which can cope with uncertainties. In the present paper, Particle Swarm Optimization Algorithm (PSOA) is presented to fine tune the fuzzy estimate for the development of software projects . The efficacy of the developed models is tested on 10 NASA software projects, 18 NASA projects and COCOMO 81 database on the basis of various criterion for assessment of software cost estimation models. Comparison of all the models is done and it is found that the developed models provide better estimation
CITED BY (12)  
1 Bardsiri, V. K., Jawawi, D. N. A., Hashim, S. Z. M., & Khatibi, E. (2014). A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons. Empirical Software Engineering, 19(4), 857-884.
2 Sandhu, G. S., & Salaria, D. S. (2014). A Bayesian Network Model of the Particle Swarm Optimization for Software Effort Estimation. International Journal of Computer Applications, 96(4).
3 Kumar, G., & Bhatia, P. K. (2014). Automation of software cost estimation using neural network technique. Memory, 1(1.06), 1-21.
4 Ganesh, M. S., & Thanushkodi, K. (2013). FAHSCEP: Fuzzy and Analogy Based Hybrid Software Cost Estimation Process. International Review on Computers and Software (IRECOS), 8(7), 1497-1505.
5 Kamal, S., & Nasir, J. A. (2013). A Fuzzy Logic Based Software Cost Estimation Model. International Journal of Software Engineering and Its Applications, 7(2), 7-18.
6 Bardsiri, V. K., Jawawi, D. N. A., Hashim, S. Z. M., & Khatibi, E. (2013). A PSO-based model to increase the accuracy of software development effort estimation. Software Quality Journal, 21(3), 501-526.
7 Rao, S., Hari, C. H., & Prasad Reddy, P. V. G. D. (2013). Predictive and Stochastic Approach for Software Effort Estimation. Int. J. of Software Engineering, IJSE, 6.
8 FSKSM, U. T. M. A Fuzzy Logic Based Software Cost Estimation Model.
9 PVGD, P. R., & VMK, H. C. (2012). Fuzzy and Swarm Intelligence for Software Cost Estimation. Global Journal of Computer Science and Technology, 11(22).
10 Papatheocharous, E., & Andreou, A. S. (2012).A hybrid software cost estimation approach utilizing decision trees and fuzzy logic. International Journal of Software Engineering and Knowledge Engineering, 22(03), 435-465.
11 PVGDP, R., CHVMK, H., & Rao, T. S. (2011). Multi objective particle swarm optimization for software cost estimation. Int J Comput Appl, 32(3), 13-17.
12 saboktakin Rizi, E., & Reshadinezhad, M. Comparing the Impact of Bandwidth and Congestion on Selection of Heterogeneous Lines Applying Fuzzy-Genetic.
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Professor Prasad Reddy P.V.G.D
Andhra University - India
prasadreddypvgd@gmail.com


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