1. May 18, 2004CS 562 - WPI1 CS 562 Advanced SW Engineering Lecture #6 Tuesday, May 18, 2004

2. May 18, 2004CS 562 - WPI2 Class Format for Today Return Proposal #1 & Paper #2 Turn in Proposal #2 Questions Lecture #6: Chapter 4 – Calibration

3. May 18, 2004CS 562 - WPI3 Questions? From last week’s class From the reading About the papers/project? Anything else?

4. May 18, 2004CS 562 - WPI4 COCOMO II Calibration Boehm, et al Chapter 4

5. May 18, 2004CS 562 - WPI5 Overview Cost Estimation Models: Models D, E and B What are each of these? Bayesian Calibration: Takes the best from D & E to produce B Which is a priori vs. a posteriori ? What justification is given for this approach?

6. May 18, 2004CS 562 - WPI6 Modeling Methodology 7 Modeling Steps in Figure 4.1, page 142 What do they mean? Operational Implications How do they impact COCOMO II estimates? What does it mean for the user? What suggestions are given to deal with this complication?

7. May 18, 2004CS 562 - WPI7 Data Collection Approach How does COCOMO II use consistency? Data collection forms in Appendix C 2000 candidate project data points filtered down to 161 The Rosetta Stone What is it? How is it used? Review Table 4.1, page 146 Differences between COCOMO 81 & COCOMO II

8. May 18, 2004CS 562 - WPI8 Model Building Statistical Process: review Fig. 4.2, page 152 Model problems vs. data problems Observational vs. experimental data What is Collinearity? Review Eq. 4.1, page 153 and Eq. 4.2, page 154 What do they mean? Sampling of predictor region Review Figs. 4.3-4.5, page 155 – Interpretation? What are outliers & influential observations?

9. May 18, 2004CS 562 - WPI9 COCOMO II.1997 Calibration What are Equations 4.3-4.5 about? (156,157) Review Tables 4.7, 4.8 (158, 159) Example of RUSE effort multiplier How do Tables 49 a & b relate? (Page 160) Why are regression coefficients negative? What reasons are given? Explanation? How is this issue resolved in COCOMO II? See Table 4.10, page 163

10. May 18, 2004CS 562 - WPI10 COCOMO II.2000 Calibration What approach was used in the 2000 calibration that differs from the 1997 version? Review Eqs. 4.6, 4.7, 4.8a & b (162-164) Discuss the Delphi exercise Purpose, approach, pros & cons Discuss sample information Purpose, approach Review Figures 4.8 & 4.9, page 167

11. May 18, 2004CS 562 - WPI11 Posterior Bayesian Update How are the expert judgment (prior) data and sample data combined? From the text: “ The resulting posterior precision will always be higher than the a priori precision or the sample data precision.” Do you agree? Why / why not? Review Figure 4.12, page 171 Productivity ranges & variances

12. May 18, 2004CS 562 - WPI12 Validating Bayesian Approach How do the authors determine that the Bayesian approach is valid? Cross-validation (Section 4.5.2.2, p. 173) Further validation (Sect. 4.5.2.3, p. 173-175) Review Tables 4.15 (p. 173) & 4.16 (p. 174) What results were obtained from the analysis?

13. May 18, 2004CS 562 - WPI13 Tailoring COCOMO II Calibrating the model to existing project data Multiplicative constant, A See Equations 4.9, 4.10, p. 176 Tables 4.17, 4.18 pages 176-177 Baseline exponent, B See Equation 4.11, page 179 Table 4.22, p. 180 Review Table 4.23, page 182

14. May 18, 2004CS 562 - WPI14 More Tailoring Consolidating or eliminating redundant parameters Why bother? Examples? Review Table 4.24, page 183 Adding new significant cost drivers not already explicit in the model Why bother? Examples?

15. May 18, 2004CS 562 - WPI15 For Next Time Read remaining chapters in Brooks Chapters 10 – 19 Paper 3 due