Agenda

1. A Neuro-Fuzzy Model with SEER-SEM for Software Effort Estimation Wei Lin Du, Danny Ho*, Luiz F. CapretzSoftware Engineering, University of Western Ontario, London, Ontario, Canada* NFA Estimation Inc., Richmond Hill, Ontario, CanadaNovember 2010

2. Agenda • Purpose • SEER-SEM • NF SEER-SEM • Evaluation • Conclusion

3. Purpose • Integrate neuro-fuzzy (NF) technique with SEER-SEM • Evaluate estimation performance of NF SEER-SEM versus SEER-SEM

4. Agenda • Purpose • SEER-SEM • NF SEER-SEM • Evaluation • Conclusion

5. SEER-SEM • SEER-SEM was trademarked by Galorath Associates, Inc. (GAI) in 1990 • Effort estimation is one of the SEER-SEM algorithmic models Size Effort Personnel Cost SEER-SEM Estimation Processing Environment Schedule Complexity Risk Constraints Maintenance

6. SEER-SEM Effort Estimation • Software Size • Lines, function points, objects, use cases • Technology and Environment Parameters • Personal capabilities and experience (7) • Development support environment (9) • Product development requirements (5) • Product reusability requirements (2) • Development environment complexity (4) • Target environment (7)

7. SEER-SEM Equations where:E Development effort K Total lifecycle effort including development and maintenance Se Effective size D Staffing complexity Cte Effective technology Ctb Basic technology

8. Agenda • Purpose • SEER-SEM • NF SEER-SEM • Evaluation • Conclusion

9. Preprocessing Neuro-Fuzzy Inference System (PNFIS) RF1 ARF1 NFB1 FM1 Algorithmic Model RF2 Output Metric Mo ARF2 FM2 NFB2 … … FMN ARFN RFN NFBN NFA USA Patent No. US-7328202-B2 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.

10. 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.

11. NF SEER-SEM Size, SIBR Effort Estimation SEER-SEM Effort Estimation Software Estimation Algorithmic Model P1 ACAP NF1 P2 AEXP NF2 P34 … Complexity (Staffing) NFm

12. Agenda • Purpose • SEER-SEM • NF SEER-SEM • Evaluation • Conclusion

13. Performance Metrics • Relative Error (RE) = (Est. Effort – Act. Effort) / Act. Effort • Magnitude of Relative Error (MRE) = |Est. Effort – Act. Effort | / Act. Effort • Mean Magnitude of Relative Error (MMRE) = (∑MRE) / n • Prediction Level (PRED) PRED(L) = k / n

14. Design of Evaluation

15. MMRE Results Negative value of MMRE change means improvement

16. MMRE Results

17. PRED Results Positive value of PRED change means improvement

18. Summary of Evaluation Results • MMRE is improved in all cases, with the greatest improvement over 25% • Average PRED(100%) is increased by 12% • NF SEER-SEM improves MMRE by reducing large MREs

19. Agenda • Purpose • SEER-SEM • NF SEER-SEM • Evaluation • Conclusion

20. Conclusion • NF with SEER-SEM improves estimation accuracy • General soft computing framework works with various effort estimation algorithmic models

21. Future Directions • Evaluate with original SEER-SEM dataset • Evaluate general soft computing framework with: • more complex algorithmic models • other domains of estimation

22. THANKS !

23. Any Questions?