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Software Lifecycle Management Lecture

Software Lifecycle Management Lecture. r2SEF for COCOMO Integrating Duration, Effort, Cost, Defects, and Uncertainty. TM. Presentation to 22 nd International Forum on Systems, Software, and COCOMO Cost Modeling. Mike Ross President & CEO r2E STIMATING , LLC 7755 E. Evening Glow Drive

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Software Lifecycle Management Lecture

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  1. Software Lifecycle Management Lecture r2SEF forCOCOMOIntegrating Duration,Effort, Cost, Defects,and Uncertainty TM Presentation to22nd International Forum on Systems, Software, and COCOMO Cost Modeling Mike RossPresident & CEOr2ESTIMATING, LLC 7755 E. Evening Glow Drive Scottsdale, Arizona 85262-1295 (o) 480.488.8382 (f) 480.488.8420 mike.ross@r2estimating.com November 2007

  2. r2 Software Estimating Framework(r2SEF) Goals • Unify Existing Model Mathematics • Software Productivity • Defect Propensity • Management Stress • Provide More Calibration Flexibility • Provide Visual Tools for Analyzing Tradeoffs • Provide Visual Tools for Analyzing Uncertainty

  3. r2 Software Estimating Framework (r2SEF) Equations (Ross, 2007a)

  4. r2SEF Variables (Ross, 2007a)

  5. r2SEF Variables (Ross, 2007a)

  6. COCOMO II to r2SEFSoftware Productivity Equation (Boehm, et al., 2000), (Ross, 2007a)

  7. COCOMO II to r2SEFSoftware Productivity Equation Variables (Ross, 2007a)

  8. COCOMO II to r2SEFDefect Propensity Equation (Boehm, et al., 2000), (Ross, 2007a)

  9. COCOMO II to r2SEFDefect Propensity Equation (Ross, 2007a)

  10. COCOMO II to r2SEFDefect Propensity Equation Variables (Ross, 2007a)

  11. COCOMO II to r2SEFManagement Stress Equation (Boehm, et al., 2000), (Ross, 2007a)

  12. COCOMO II to r2SEFManagement Stress Equation Variables (Ross, 2007a)

  13. COCOMO II to r2SEFManagement Stress Equation Variables (Boehm, 1981), (Ross, 2007a)

  14. Single Point Estimate andGoal Satisfaction Stress (M) Size/Efficiency Ratio

  15. Uncertainty in Size and Efficiency 99% Size/Efficiency Ratio 50% 1%

  16. Distribution of Solutions for a Given Management Stress Value Stress (M) 99% Size/Efficiency Ratio 50% 1%

  17. Ranges of Possible Outcomes Stress (M) 99% Size/Efficiency Ratio 50% 1%

  18. Typical (Nominal Stress) Solution at70% Desired Confidence Probabilities Stress (M) 70% Size/Efficiency Ratio 50%

  19. Example Software ProjectEstimating Scenario • Real time embedded avionics software for commercial air transport application • Effective software size: • [45,000; 50,000; 60,000] SLOC (triangular) • Nominal defect density: • [1.06; 1.48; 2.07] defects/KSLOC (triangular) • Cost of labor: 40 ph/pw; $100/ph • Constraints: • Duration: Goal ≤ 104 weeks; Confidence ≥ 80% • Effort: Goal ≤ 2,000 pw; Confidence ≥ 50% • Cost: Goal ≤ $7,000,000; Confidence ≥ 80% • Defects: Goal ≤ 100 deliv. defects; Confidence ≥ 90%

  20. COCOMO II Post ArchitectureScale Driver and Effort Multiplier Inputs

  21. Typical (Nominal Management Stress)

  22. Minimum Acceptable Duration

  23. Minimum Necessary Duration

  24. Identifying and Analyzing Alternatives • Change Assumptions • Reduce the effective software size (i.e., postpone or eliminate functionality), • Reduce the uncertainty range around effective software size (i.e., refine the size estimate and secure functionality freezes to reduce variability and potential for growth), • Increase efficiency (i.e., better people, better processes/tools, less complex product, etc.), • Reduce the uncertainty range around efficiency (i.e., lock down decisions about the product technology and the development environment). • Change Constraints • Relax one or more of the goal values, • Relax one or more of the desired confidence probabilities.

  25. Reduced Effective Software Size

  26. Relaxed Duration Goal

  27. High Duration Risk

  28. Composite Solution #1Reduced Size & Relaxed Duration Goal

  29. Composite Solution #2Relaxed Effort, Cost, & Defects Goals

  30. References Boehm, Barry W. 1981. Software Engineering Economics. Englewood Cliffs : Prentice-Hall, Inc., 1981. Boehm, Barry W., et al. 2000. Software Cost Estimation with COCOMO II. Upper Saddle River : Prentice-Hall, Inc., 2000. Browne, J. 2001. Probabilistic Design: Course Notes. Melbourne, Australia : Swinburne University of Technology, 2001. Jensen, Randall W. n.d.. An Improved Macrolevel Software Development Resource Estimation Model. s.l. : Software Engineering, Inc., n.d. Musa, John D. 2004. Software Reliability Engineering: More Reliable Software Faster and Cheaper. 2nd. Bloomington : AuthorHouse, 2004. Norden, Peter V. 1977. Project Life Modeling: Background and Application of Life Cycle Curves. Proceedings, Software Life Cycle Management Workshop. Airlie, VA, USA : Sponsored by USACSC, 1977. Putnam, Lawrence H. 1980. Software Cost Estimating and Life-Cycle Control: Getting the Software Numbers. New York : IEEE Computer Society, 1980.

  31. References Ross, Michael A. 2007a. Next Generation Software Project Estimating: 25 Years and Thousands of Projects Later. Proceedings, Joint ISPA / SCEA 2007 Conference. New Orleans, LA, USA : The International Society of Parametric Analysts and The Society of Cost Estimating and Analysis, June 2007a. —. 2007b. Next Generation Software Project Estimating: Know the Odds Before Placing Your Bet. Proceedings, AIAA SPACE 2007 Conference & Exhibition. Long Beach, CA, USA : American Institute of Aeronautics and Astronautics, September 2007b. AIAA 2007-6022.

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