1. University of Southern California Center for Software Engineering CSE USC 9/14/05 1 COCOMO II: Airborne Radar System Example Ray Madachy madachy@usc.edu CSCI 510 September 14, 2005

2. University of Southern California Center for Software Engineering CSE USC 9/14/05 2 Outline Overview the Airborne Radar System (ARS) Demonstrate progressive usage of different COCOMO sub-models within an evolutionary spiral development process Cover estimation of reuse, modification, COTS, and automated translation Show how an aggregate estimate is refined in greater detail

3. University of Southern California Center for Software Engineering CSE USC 9/14/05 3 ARS Estimation Use Applications Composition, Early Design and Post-Architecture submodels Two Post-Architecture estimates are demonstrated: top-level and detailed –scale drivers apply to overall system in both estimates –cost drivers are rated for the aggregate system in the top-level estimate (17 ratings) –cost drivers are refined for each individual software component in the detailed estimate (17*6 components=102 ratings)

4. University of Southern California Center for Software Engineering CSE USC 9/14/05 4 ARS System Overview

5. University of Southern California Center for Software Engineering CSE USC 9/14/05 5 Software Components Radar Unit Control –controls radar hardware Radar Item Processing –extracts information from returned radar to identify objects Radar Database –maintains radar object tracking data Display Manager –high level displays management Display Console –user input device interface and primitive graphic processing Built In Test –hardware monitoring and fault localization

6. University of Southern California Center for Software Engineering CSE USC 9/14/05 6 COCOMO Coverage in Evolutionary Lifecycle Process * both top-level and detailed estimates shown

7. University of Southern California Center for Software Engineering CSE USC 9/14/05 7 Prototype Size and Effort Productivity is “high” at 25 NAP/PM Effort = NAP/ Productivity = 136.3/25 = 5.45 PM (or 23.6 person-weeks) Personnel = 23.5 person-weeks/6 weeks ~ 4 full-time personnel

8. University of Southern California Center for Software Engineering CSE USC 9/14/05 8 Precedentedness (PREC) Development Flexibility (FLEX) Risk/Architecture Resolution (RESL) Team Cohesion (TEAM) Process Maturity (PMAT) Nominal Low High Nominal Factor Rating Scale Factors for Breadboard

9. University of Southern California Center for Software Engineering CSE USC 9/14/05 9 Early Design Cost Drivers for Breadboard High Very High High Nominal Factor Rating Product Reliability and Complexity (RCPX) Required Reuse (RUSE) Platform Difficulty (PDIF) Personnel Capability (PERS) Personnel Experience (PREX) Facilities (FCIL) Schedule (SCED)

10. University of Southern California Center for Software Engineering CSE USC 9/14/05 10 Breadboard System Size Calculations

11. University of Southern California Center for Software Engineering CSE USC 9/14/05 11 Early Design Estimate for Breadboard

12. University of Southern California Center for Software Engineering CSE USC 9/14/05 12 ARS Full Development for IOC Use Post-Architecture estimation model –same general techniques as the Early Design model for the Breadboard system, except for elaborated cost drivers Two estimates are demonstrated: top-level and detailed –scale drivers apply to overall system in both estimates –cost drivers are rated for the aggregrate system in the top-level estimate (17 ratings) –cost drivers are refined for each individual software component in the detailed estimate (17*6 components=102 ratings)

13. University of Southern California Center for Software Engineering CSE USC 9/14/05 13 ARS Top-Level Size Calculations

14. University of Southern California Center for Software Engineering CSE USC 9/14/05 14 Post-Architecture Estimate for IOC (Top-level)

15. University of Southern California Center for Software Engineering CSE USC 9/14/05 15 Post-Architecture Estimate for IOC (Detailed)

16. University of Southern California Center for Software Engineering CSE USC 9/14/05 16 Sample Incremental Estimate

17. University of Southern California Center for Software Engineering CSE USC 9/14/05 17 Increment Phasing

18. University of Southern California Center for Software Engineering CSE USC 9/14/05 18 Increment Summary

19. University of Southern California Center for Software Engineering CSE USC 9/14/05 19 We provided an overview of the ARS example provided in Chapter 3 We demonstrated using the COCOMO sub-models for differing lifecycle phases and levels of detail –the estimation model was matched to the known level of detail We showed increasing the level of component detail in the Post-Architecture estimates Incremental development was briefly covered Summary and Conclusions