1 / 15

CS 562 Advanced SW Engineering

In the sixth lecture of CS 562, we delve into advanced cost estimation models, focusing on COCOMO II calibration. The session covers various models, including Models D, E, and B, and introduces Bayesian calibration, highlighting its advantages and justifications. We explore the operational implications of the modeling steps, the significance of data consistency, and the processes involved in model building. Key topics also include validating the Bayesian approach and tailoring COCOMO II to fit existing project data, preparing students for challenges in software engineering project management.

lynn
Télécharger la présentation

CS 562 Advanced SW Engineering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Class Format for Today • Return Proposal #1 & Paper #2 • Turn in Proposal #2 • Questions • Lecture #6: • Chapter 4 – Calibration CS 562 - WPI

  3. Questions? • From last week’s class • From the reading • About the papers/project? • Anything else? CS 562 - WPI

  4. COCOMO IICalibration Boehm, et al Chapter 4 CS 562 - WPI

  5. 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? CS 562 - WPI

  6. 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? CS 562 - WPI

  7. 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 CS 562 - WPI

  8. 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? CS 562 - WPI

  9. 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 CS 562 - WPI

  10. 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 CS 562 - WPI

  11. 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 CS 562 - WPI

  12. 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? CS 562 - WPI

  13. 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 CS 562 - WPI

  14. 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? CS 562 - WPI

  15. For Next Time • Read remaining chapters in Brooks • Chapters 10 – 19 • Paper 3 due CS 562 - WPI

More Related