International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)

IJTEEE >> Volume 2 - Issue 4, April 2014 Edition

International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289

Artificial Neural Network Models For Software Effort Estimation

[Full Text]



P.Subitsha, J.Kowski Rajan



Keywords : Artificial neural network, Cocomo



ABSTRACT: Software estimation accuracy is one of the greatest challenges for software developers. Software cost and time estimation supports the planning and tracking of software projects. The present paper is directed to design a model which should be accurate and comprehensible in order to inspire confidence in a business setting. Software effort estimation models which adopt a neural network technique provide a solution to improve the accuracy. However, no univocal conclusion to which technique is the most suited has been reached. This study addresses this issue by reporting on the results of a large scale benchmarking study. Different types of techniques are under consideration including techniques such as Multilayered Perceptron Network, Radial Basis Function Neural Network, Support Vector Machines, Extreme Learning Machines and Particle Swarp Optimization. Studies are made using COCOMO II data. Results are provided for MMRE and PRED (25) accuracy measures.



[1] Jones, T.C. Estimating Software Costs, McGraw-Hill, 1998.

[2] Thayer, H.R., Software Engineering Project Management, Second Edition IEEE CS Press, 2001.

[3] C. Symons, “Come Back Function Point Analysis (Modernized) – All is Forgiven!”, Proc. of the 4th European Conference on Software Measurement and ICT Control, FESMA-DASMA 2001, pp. 413-426, 2001.

[4] C. M. Bishop, Neural networks for pattern recognition. Oxford J. M. Zurada, Introduction to artificial neural systems. Boston: PWS Publishing Company, 1995.

[5] B. D. Ripley, Pattern Recognition and Neural Networks. Cam-bridge University Press, 1996.

[6] G. Finnie, G. Wittig, and J.-M. Desharnais, “A comparison of soft- ware effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models,” Journal of Systems and Software, vol. 39, pp. 281–289, 1997.

[7] C. Burgess and M. Lefley, “Can genetic programming improve software effort estimation? a comparative evaluation,” Information and Software Technology, vol. 43, pp. 863–873, 2001.

[8] M. Lefley and M. Shepperd, “Using genetic programming to improve software effort estimation based on general data sets,” in Lecture Notes in Computer Science, vol. 2724, 2003, pp. 2477–2487.

[9] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989.

[10] M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994.

[11] J. Moody and C. Darken, “Fast learning in networks of locally-tuned processing units,” Neural Computing, vol. 1, pp. 281–294, 1989.

[12] D. Specht, “A general regression neural network,” IEEE Trans-actions on Neural Networks, vol. 2, no. 6, pp. 568–576, 1991.

[13] A. Idri, A. Zahi, E. Mendes, and A. Zakrani, “Software Cost Esti-mation Models Using Radial Basis Function Neural Networks,” in Lecture Notes in Computer Science, vol. 4895, 2008, pp. 21–31.

[14] A. Heiat, “Comparison of artificial neural networks and regression models for estimating software development effort,” Information and Software Technology, vol. 44, no. 15, pp. 911–922, 2002.

[15] V. N. Vapnik, Statistical Learning Theory. New York, USA: John Wiley, 1998.

[16] P. Rao, Nonparametric Functional Estimation. Orlando, USA: Academic Press, 1983.

[17] V. Kumar, V. Ravi, M. Carr, and R. Kiran, “Software development cost estimation using wavelet neural networks,” The Journal of Systems and Software, vol. 81, pp. 1853–1867, 2008.

[18] G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks”, in: Proceedings of International Joint Conference on Neural Networks (IJCNN2004), 25–29 July, 2004, Budapest, Hungary.

[19] G.-B. Huang, Q.-Y. Zhu, K.Z. Mao, C.-K. Siew, P. Saratchandran, N.

[20] Sundararajan, “Can threshold networks be trained directly?”, IEEE Trans. Circuits Syst. II 53 (3) (2006) 187–191.

[21] I. Myrtveit and E. Stensrud, “A controlled experiment to assess the benefits of estimation with analogy and regression models,” IEEE Transactions on Software Engineering, vol. 25, no. 4, pp. 510–525,

[22] B. Littlewood, P. Popov, and L. Strigini, “Modeling software design diversity a review,” ACM Computing Surveys, vol. 33, no. 2, pp. 177–208, 2001.

[23] R. M. Dawes, D. Faust, and P. E. Meehl, “Clinical versus actuarial judgement,” Science, vol. 243, no. 4899, pp. 1668–1674, 1989.

[24] M. Jørgensen, “Forecasting of software development work effort: Evi- dence on expert judgement and formal models,” International Journal of Forecasting, vol. 23, pp. 449–462, 2007.

[25] T. Mukhopadhyay, S. S. Vicinanza, and M. J. Prietula, “Examining the feasibility of a case-based reasoning model for software effort estimation,” MIS Quarterly, vol. 16, no. 2, pp. 155–171, 1992.