1. 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

2. Agenda NFA Model Validation of NFA Model NFA Tool Roadmap and Direction

3. NFA Model FM 2 … NFB 1 NFB N Algorithmic Model NFB 2 Output Metric Mo FM 1 FM N RF 2 RF 1 RF N ARF 1 ARF N Preprocessing Neuro-Fuzzy Inference System (PNFIS) ARF 2 … 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.

4.    N N N A iN A i2 A i1  …… … ARF i FM i FMP i1 FMP iN FMP i2 w1w1 wNwN Layer1 Layer3Layer4Layer5 Layer2 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.

5. Agenda NFA Model Validation of NFA Model NFA Tool Roadmap and Direction

6. 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

7. NFA vs COCOMO Estimate within Actual (%) COCOMO ModelNFA ModelImprovement # ProjectsAccuracy# ProjectsAccuracy 20%4971%6289%18% 30%5681%6492%11% 50%6594%6797%3% 100%69100%69100%0%

8. Estimate within Actual (%) ANOVANFAImprovement # ProjectsAccuracy# ProjectsAccuracy 20%2133%2844%11% 25%2539%3352%13% 30%3352%3758%6% 50%5079%5282%3% 100%63100%63100%0% NFA vs ANOVA

9. NFA vs Function Point Exp.1Exp.2Exp.3Exp.4Exp.5 MMRE Function Point 1.381.581.571.391.42 MMRE NFA 1.101.281.171.031.11 Improvement (%) 20%19%25%26%22%

10. Agenda NFA Model Validation of NFA Model NFA Tool Roadmap and Direction

11. NFA Tool

12. NFA Engine

13. NFA Screen Capture

14. 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

15. 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

16. References 1.Haykin S, Neural Networks: A Comprehensive Foundation. Prentice Hall, 1998. 2.Zadeh L A, Fuzzy Logic. Computer, Vol 21, pp. 83-93, 1988. 3.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. 4.Albrecht A, Measuring Application Development Productivity. Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, Oct 1979. 5.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. 6.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.www.springeronline.com 7.Xia W, Capretz L, Ho D, Calibrating Function Points Using Neuro-Fuzzy Technique. IFPUG, (to appear) 2005. 8.Maxwell D, Forselius P, Benchmarking Software Development Productivity. IEEE Software 17(1):80-88, 2000. 9.Putnam L, Myers W, Measures for Excellence. Yourdon Press, Englewood Cliffs, 1992. 10. 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.

17. THANKS !

18. Any Questions?