Artificial Neural Network Models For Software Effort Estimation
[Full Text]
AUTHOR(S)
P.Subitsha, J.Kowski Rajan
KEYWORDS
Keywords : Artificial neural network, Cocomo
ABSTRACT
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.
REFERENCES
[1] Jones, T.C. Estimating Software Costs, McGrawHill, 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, FESMADASMA 2001, pp. 413426, 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. Cambridge 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, casebased 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 locallytuned processing units,” Neural Computing, vol. 1, pp. 281–294, 1989.
[12] D. Specht, “A general regression neural network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568–576, 1991.
[13] A. Idri, A. Zahi, E. Mendes, and A. Zakrani, “Software Cost Estimation 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 casebased reasoning model for software effort estimation,” MIS Quarterly, vol. 16, no. 2, pp. 155–171, 1992.
