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Predictive Models to Achieve Business Results

Predictive Models to Achieve Business Results. Place your image on top of this gray box. If no graphic i applicable, delete gray box and notch-out behind gray box, from the Title Master. 19th International Forum on COCOMO and Software Cost Modeling

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Predictive Models to Achieve Business Results

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  1. Predictive Models to Achieve Business Results Place your image on top of this gray box. If no graphic i applicable, delete gray box and notch-out behind gray box,fromthe Title Master 19th International Forum on COCOMO and Software Cost Modeling Cvetan Redzic, Michael Crowley, Nancy Eickelmann, Jongmoon Baik Motorola, Inc. October 26, 2004

  2. Outline • Overview • Business Goals • Models Used • COQUALMO • CoQ-DES • MotoROI • Primary Model Inputs • CMM • Life Cycle Scope • PCE / PSE • Results • Cost • Quality

  3. + Satisfier Features 3 Delighters Attractive 2 Quality 1 Must Be - Business Goal – Improved Customer Satisfaction SW Quality • Type of needs • Basic Expectations (Must Be) • Satisfier - Features • Delighters (Attractive) Kano Analysis

  4. Improved Customer Satisfaction Cause & Effect Diagram

  5. Integrating Predictive Models • Models Used • COQUALMO • CoQ-DES • MotoROI

  6. COQUALMO

  7. Combined COQUALMO Injection Factors

  8. CoQ-DES

  9. CoQ-DES Simulation

  10. MotoROI

  11. MotoROI - DOORS ROI Analysis

  12. Model Integration - Primary Model Inputs • CMM • Life Cycle • PCE / PSE

  13. CMM – Process Maturity • COQUALMO • PMAT (process maturity has the greatest +/-impact) on injection rates • CoQ-DES • Not Used directly but is inherent in organizational calibration • MotoROI • Process maturity as represented by the cost of quality/cost of poor quality financial structure is a primary factor. Knox Theoretical Model of TCOQ (About 50% at CMM Level 3)

  14. Requirement System Test Design Component/Integration Test Testing Inspections Implementation Unit Test Code Life Cycle • COQUALMO • Req., Des., Imp., and Code • CoQ-DES • Full Life Cycle • MotoROI • Full Life Cycle or Individual Phases

  15. PCE and PSE • COQUALMO • PCE and PSE as evidenced by injection and removal rates • CoQ-DES • PCE and PSE as evidenced by injection and removal rates • MotoROI • PCE for DP or PSE for technology effectiveness Phase Containment Effectiveness & Phase Screening Effectiveness

  16. Measuring and Monitoring Results • Quality • Cost

  17. Quality - Sources of Variation For Release with about 100 Delta KLOC, no significant difference estimates & actuals in DI & DR For large size Release over 100 Delta KLOC, there is significant difference b/w estimates & actuals in DI & DR for Code Actual vs. COQUALMO Estimate

  18. Quality - Sigma Level From PCE, SRE & CRUD data Sigma Level: Defects per Million Opportunities DPMO = 1M * D/(N*O) D = 2464 HS Faults (from PCE) N = 139,595 Delta LOC DPMO = 1M * 2464/139,595 DPMO = 17651 - 3.61 s Stable processes Need Leap improvement: SEI CMM Level 5 TCM • What is Sigma Level from release perspective ? • Relatively stable across the releases

  19. Quality - SRE Goal Setting

  20. Quality: As-Is Process

  21. Quality - Rayleigh Model Analysis

  22. Quality - Impact of Tactical Changes Monte-Carlo simulation, to include uncertainty & risks In the expert based opinion

  23. Quality - New Process Baseline

  24. Cost - Vital X Monthly Review Charts SLIM

  25. Quality - Vital X Monthly Review Charts Fault Injection & Removal vs. Baselines

  26. Quality - Vital X Monthly Review Charts SRE

  27. CRUD Goal Tracking

  28. Summary • Integrating predictive models provides multiple views of project quality, cost and schedule issues. • More accurate predictions of defect injection are possible • More accurate predictions of defect removal are possible • More accurate predictions of overall staffing and project cost are possible

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