Software cost estimation using computational intelligence techniques

2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings

Volumn , Issue , 2009, Pages 849-854

  Pahariya, J S ^a   Ravi, V ^a   Carr, M ^a  

^a INSTITUTE FOR DEVELOPMENT AND RESEARCH IN BANKING TECHNOLOGY   (India)

Author keywords

Classification and Regression Tree (CART); Counter Propagation Neural Network (CPNN); Dynamic Evolving Neuro Fuzzy Inference System (DENFIS); Genetic Programming (GP); Group Method of Data Handling (GMDH); Multilayer FeedForward Neural Network (MPFF); Multiple Linear Regression (MLR); Multivariate Adaptive Regression Splines (MARS); Polynomial regression; Radial Basis Function Neural Network (RBF); Support Vector Regression (SVR); Tree net

Indexed keywords

CLASSIFICATION AND REGRESSION TREE; COUNTER PROPAGATION NEURAL NETWORK (CPNN); COUNTER PROPAGATION NEURAL NETWORKS; GROUP METHOD OF DATA HANDLING; MULTILAYER FEEDFORWARD NEURAL NETWORKS; MULTIPLE LINEAR REGRESSIONS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; NEURO-FUZZY INFERENCE SYSTEMS; POLYNOMIAL REGRESSION; RADIAL BASIS FUNCTION NEURAL NETWORKS; SUPPORT VECTOR REGRESSIONS;

ARTIFICIAL INTELLIGENCE; ATTITUDE CONTROL; CLASSIFICATION (OF INFORMATION); COMPUTER SOFTWARE; DATA HANDLING; FEATURE EXTRACTION; FUZZY INFERENCE; FUZZY SYSTEMS; LINEAR REGRESSION; MULTILAYER NEURAL NETWORKS; MULTILAYERS; POLYNOMIALS; RADIAL BASIS FUNCTION NETWORKS; SPLINES;

GENETIC PROGRAMMING;

EID: 77949583526     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NABIC.2009.5393534     Document Type: Conference Paper

References (29)

1. A.J. Albrecht and J.E. Gaffney, "Software function, source lines of code, and development effort prediction: a software science validation," IEEE Transactions on Software Engineering, 1983, pp. 639-647.
2. B.W.Boehm, "Software Engineering Economics," Prentice-Hall, Englewood Cliffs, NJ, USA, 1981.
3. L.H.Putnam, "A general empirical solution to the macro software sizing and estimation problem," IEEE Transactions on Software Engineering, July 1978, pp. 345-361.
4. B. Kitchenham, L.M. Pickard, S. Linkman and P.W. Jones, "Modeling software bidding risks," IEEE Transactions on Software Engineering, 2003, pp. 542-554.
5. J. Verner and G. Tate, "A Software Size Model," IEEE Transactions on Software Engineering, 1992, No. 4.
6. K. Vinaykumar, V. Ravi, M. Carr and N. Rajkiran, "Software cost estimation using wavelet neural networks," Journal of Systems and Software, 2008, pp. 1853-1867
7. T. Foss, E. Stensrud, B. Kitchenham and I. Myrtveit, "A simulation study of the model evaluation criterion MMRE," IEEE Transactions on Software Engineering, 2003, pp. 985-995.
8. Z. Xu and T.M. Khoshgoftaar "Identification of fuzzy models of cost estimation," Fuzzy Sets and Systems, 2004, pp. 141-163
9. J.E. Matson, B.E Barrett and J.M. Mellichamp, "Software development cost estimation using function points," IEEE Transactions on Software Engineering, 1994, pp. 275-287.
10. A.Idri, T.M. Khosgoftaar and A. Abran, "Can neural networks be easily interpreted in software cost estimation," World Congress on Computational Intelligence, Honolulu, Hawaii, USA, 2002, pp. 12-17.
11. A.R. Gray, "A simulation-based comparison of empirical modeling techniques for software metric models of development effort," In: Proceedings of ICONIP, Sixth International Conference on Neural Information Processing, Perth, WA, Australia, 1999, pp. 526-531.
12. X.Huang, L.F.Capetz,J. Ren and D.Ho, "A neuro-fuzzy model for software cost estimation," Proceedings of the third International Conference on Quality Software, 2003, pp. 126-133.
13. K.K. Aggarwal, Y. Singh, P.Chandra and M.Puri, "An expert committee model to estimate line of code," ACM New York, NY, USA, 2005, pp. 1-4.
14. K. Vinay Kumar, V. Ravi and Mahil Carr, "Software Cost Estimation using Soft Computing Approaches," Handbook on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, Eds. E. Soria, J.D. Martin, R. Magdalena, M.Martinez, A.J. Serrano, IGI Global, USA, 2009.
15. Y.F. Li, M. Xie and T.N. Goh, "A study of project selection and feature weighting for analogy based software cost estimation," Journal of Systems and Software, 2009, pp. 241-252
16. N.H. Chiu and S.J.Huang, "The adjusted analogy-based software effort estimation based on similarity distances," System and Software, 2007, pp.628-640
17. D. Srinivasan, "Energy demand prediction using GMDH networks," Neurocomputing, 2008, pp. 625-629.
18. R. Poli, W.B. Langdon and J.R. Koza, "A field guide to Genetic Programming," publisher- Lulu.com, United Kingdom, 2008.
19. I. Kuzmanovski and M. Novic, "Counter-Propagation Neural Networks in Matlab," Chemometrics and Intelligent Laboratory Systems, 2008, pp. 84-91
20. G. Dematos, M. S Boyd, B. Kermanshahi, N. Kohzadi and I. Kaastra, "Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates," Asia-Pacific Financial Markets, 1996, pp. 59-75.
21. M. Lefley and M. J. Shepperd, "Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets", LNCS, Genetic and Evolutionary Computation - GECCO 2003, ISBN: 978-3-540-40603-7, page-208.
22. A.G. Ivakhnenko, "The group method of data handling - a rival of the method of stochastic approximation", Soviet Automatic Control, 13(3), 1966, pp. 43-55.
23. R. Hecht-Nielsen, Counterpropagation Networks, Applied Optics, v.26, #23, 1987, pp. 4979-4984.
24. V.N. Vapnik, 'Statistical Learning Theory", John Wiley, New York, 1998.
25. L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, "Classification and Regression Trees", Wadsworth, Belmont-CA, 1984.
26. J.H. Friedman, "Multivariate adaptive regression splines", Ann Stat, 1991, 19, 150.
27. N.K. Kasabov, Q. Song, "DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction". IEEE Transactions on Fuzzy Systems 10 (2), 2002, pp. 144-154.
28. J.H. Friedman, "Stochastic gradient boosting", Stanford, Statistics Department, Stanford University, 1999.
29. J. Moody, C.J. Darken, "Fast learning in networks of locally tuned processing units', Neural Computation. 1, 1989, pp. 281-294.