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Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation Danny Ho, Luiz F. Capretz*, Xishi Huang, Jing Ren NFA Estimation Inc., London, Ontario, Canada *Software Engineering, University of Western Ontario, London, Ontario, Canada October 2005. Agenda. NFA Model Validation of NFA Model
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Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation Danny Ho, Luiz F. Capretz*, Xishi Huang, Jing RenNFA Estimation Inc., London, Ontario, Canada*Software Engineering, University of Western Ontario, London, Ontario, CanadaOctober 2005
Agenda • NFA Model • Validation of NFA Model • NFA Tool • Roadmap and Direction
Preprocessing Neuro-Fuzzy Inference System (PNFIS) RF1 ARF1 NFB1 FM1 Algorithmic Model RF2 Output Metric Mo ARF2 FM2 NFB2 … … FMN ARFN RFN NFBN NFA Model where N is the number of contributing factors, M is the number of other variables in the Algorithmic Model, RF is Factor Rating, ARF is Adjusted Factor Rating, NFB is the Neuro-Fuzzy Bank, FM is Numerical Factor/Multiplier for input to the Algorithmic Model, V is input to the Algorithmic Model, and Mo is Output Metric.
Layer3 Layer4 Layer5 Layer1 Layer2 FMPi1 w1 N Ai1 Ai2 N ARFi FMi … … … FMPi2 AiN N wN FMPiN NFB where ARFi is Adjusted Factor Rating for contributing factor i, is fuzzy set for the k-th rating level of contributing factor i, is firing strength of fuzzy rule k, is normalized firing strength of fuzzy rule k, is parameter value for the k-th rating level of contributing factor i, and is numerical value for contributing factor i.
Agenda • NFA Model • Validation of NFA Model • NFA Tool • Roadmap and Direction
Validation of NFA Model Sources of Project Data • Standard COCOMO Model using 69 project data points • Stepwise ANOVA Model using 63 project data points • Function Point Analysis using 184 project data points
Agenda • NFA Model • Validation of NFA Model • NFA Tool • Roadmap and Direction
Roadmap and Direction • Treat NFA as superset of all estimation models • Filed “System and Method for Software Estimation”, Patent Pending US10/920236, CAN2477919 • Roadmap: research then commercialization • Funding secured from NSERC for 5 years
Roadmap and Direction (cont’) • Short-term objective • validate accuracy of NFA over well-known algorithmic models • cost estimation, size estimation, quality estimation, systems of systems estimation, and system integrator estimation, among others • Long-term objective • apply NFA to other aspects of estimation • prediction of stock performance, investment risk estimation, prediction of medical condition, disease growth, and so on • Series of potential new products • NF COCOMO, NF Function Point, NF SLIM, NF Size, NF Defect, NF SoS, NF SysInteg, NF Stock, NF Medical, and so forth • Refine our IP
References • Haykin S, Neural Networks: A Comprehensive Foundation. Prentice Hall, 1998. • Zadeh L A, Fuzzy Logic. Computer, Vol 21, pp. 83-93, 1988. • Boehm B, Horowitz E, Madachy R, Reifer D, Clark B, Steece B, Brown A, Chulani S, Abts C, Software Cost Estimation with COCOMO II. Prentice Hall, 2000. • Albrecht A, Measuring Application Development Productivity. Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, Oct 1979. • Huang X, Ho D, Capretz L, Ren J, An Intelligent Approach to Software Cost Prediction. 18th International Conference on COCOMO and Software Cost Modeling, Los Angeles, 2003. • Huang X, Ho D, Ren J, Capretz L, A Soft Computing Framework for Software Effort Estimation. Soft Computing Journal, Springer, available at www.springeronline.com, 2005. • Xia W, Capretz L, Ho D, Calibrating Function Points Using Neuro-Fuzzy Technique. IFPUG, (to appear) 2005. • Maxwell D, Forselius P, Benchmarking Software Development Productivity. IEEE Software 17(1):80-88, 2000. • Putnam L, Myers W, Measures for Excellence. Yourdon Press, Englewood Cliffs, 1992. • Lin C S, Khan H A, Huang C C, Can the Neuro-Fuzzy Model Predict Stock Indexes Better than Its Rivals? CIRJE-F-165, August 2002.
