1. 1 CORADMO in 2001: A RAD Odyssey Cyrus Fakharzadeh fakharza@usc.edu 16th International Forum on COCOMO and Software Cost Modeling University of Southern California Center for Software Engineering CSE USC

2. 2 Introduction RAD (Rapid Application Development) an application of any of a number of techniques or strategies to reduce software development cycle time CORADMO COCOMO II model extension Focuses on software development schedules and costs using RAD techniques University of Southern California Center for Software Engineering CSE USC

3. 3 Constructive Rapid Application Development Model Calculates/predicts –schedule (months, M) –personnel (P) –adjusted effort (person-months, PM) Based on –Effort and schedule distribution to the various phases –Selected schedule driver ratings impacts on the M, P, and PM of each phase. University of Southern California Center for Software Engineering CSE USC

4. 4 Six Classes of Strategies for RAD Reuse, Very High-level Languages (RVHL) Development Process Reengineering (DPRS) Collaboration Support (CLAB) Architecture, Risk Resolution (RESL) Prepositioning Assets (PPOS) RAD Capability of Personnel (RCAP) University of Southern California Center for Software Engineering CSE USC

5. 5 RAD Opportunity Tree University of Southern California Center for Software Engineering CSE USC

6. 6 Background COCOMO II Schedule shortfalls: Reflects projects optimized for minimum cost Model does not address RAD strategies COCOMO II.2000 Duration Calculation Cube Root Law: Months ~ 3.67 (Person-Months) f where 0.28  f  0.34 CORADMO differs from COCOMO: A square root instead in computing the number of months needed to complete a small project Square root law (i.e. f = 0.5) University of Southern California Center for Software Engineering CSE USC

7. 7 COPSEMO Constructive Phased Schedule and Effort Model Inputs: the baseline effort and schedule from COCOMO II Outputs: the effort and schedule by phase needed for CORADMO. Phases: Inception, Elaboration, Construction, and Transition Source: MBASE/RUP (Model-Based Architecting & Software Engineering/Rational Unified Process) life-cycle model University of Southern California Center for Software Engineering CSE USC

8. 8 University of Southern California Center for Software Engineering CSE USC

9. 9 Physical Model University of Southern California Center for Software Engineering CSE USC

10. 10 Results Delphi Exercise Forms distributed Experts from Academia, Industry and Government –Affiliates, Professors, and Researchers EMR (Effort Multiplier Range) –Highest divided by Lowest across the rating scale for effort SMR (Schedule Multiplier Range) –Highest divided by Lowest across the rating scale. University of Southern California Center for Software Engineering CSE USC

11. 11 % Effort per phase OriginalDelphi Mean Delphi Standard Deviation Inception – I 6.010.294.75 Elaboration – E 24.023.715.38 Construction – C 76.071.2912.00 Total I, E, & C 118.0105.294.24 University of Southern California Center for Software Engineering CSE USC

12. 12 % Schedule per phase OriginalDelphi Mean Delphi Standard Deviation Inception – I 12.515.714.99 Elaboration – E 37.529.865.64 Construction – C 62.563.1411.94 Total I, E, & C 112.5108.716.94 University of Southern California Center for Software Engineering CSE USC

13. 13 Reuse, Very High-level Languages RVHLEMRSMR Original Delphi Mean Delphi Standard Deviation Original Delphi Mean Delphi Standard Deviation Inception 1.161.250.081.161.220.06 Elaboration 1.071.250.091.071.240.08 Construction 1.001.160.111.001.130.09 University of Southern California Center for Software Engineering CSE USC Degree to which re-use of artifacts other than code and/or very high-level languages are utilized

14. 14 Development Process Reengineering University of Southern California Center for Software Engineering CSE USC Measures the degree to which the project and organization allow and encourage streamlined or reengineered development processes DPRSEMRSMR Original Delphi Mean Delphi Standard Deviation Original Delphi Mean Delphi Standard Deviation Inception 1.331.320.021.331.250.05 Elaboration 1.211.240.041.21 0.02 Construction 1.211.300.061.211.220.04

15. 15 Collaboration Support University of Southern California Center for Software Engineering CSE USC Accounts for Multisite tool support plus special collaboration tools, yields a reduced effect on schedule and effort CLABEMRSMR Original Delphi Mean Delphi Standard Deviation Original Delphi Mean Delphi Standard Deviation Inception 1.511.340.091.511.270.13 Elaboration 1.341.230.071.341.230.06 Construction 1.181.230.081.181.210.05

16. 16 Architecture, Risk Resolution University of Southern California Center for Software Engineering CSE USC Same as COCOMO II RESL RESLEMRSMR Original Delphi Mean Delphi Standard Deviation Original Delphi Mean Delphi Standard Deviation Inception 1.001.240.341.001.210.35 Elaboration 1.001.240.341.001.230.35 Construction 1.001.270.331.331.350.29

17. 17 Prepositioning Assets University of Southern California Center for Software Engineering CSE USC Degree to which assets are pre-tailored to a project and furnished to the project for use on demand PPOSEMRSMR Original Delphi Mean Delphi Standard Deviation Original Delphi Mean Delphi Standard Deviation Inception 1.101.130.021.251.220.02 Elaboration 1.101.140.041.251.230.02 Construction 1.101.200.041.251.260.03

18. 18 RAD Capability of Personnel University of Southern California Center for Software Engineering CSE USC Accounts for the effects of RAD personnel capability & experience in RAD projects RCAPEMRSMR Original Delphi Mean Delphi Standard Deviation Original Delphi Mean Delphi Standard Deviation Inception 1.501.480.153.002.480.26 Elaboration 1.501.440.143.002.480.25 Construction 1.501.460.143.002.490.25

19. 19 Example University of Southern California Center for Software Engineering CSE USC With RCAP = Nominal => PM=25, M=5, P=5 Result: The square root law: 5 people for 5 months: 25 PM With RCAP=XH (Extra High) => PM=20, M=2.8, P=7.1 Result: A super team can put on 7 people and finish in 2.8 months: 20 PM With RCAP = XL (Extra Low) => PM=30, M=7, P=4 Result: Trying to do RAD with an unqualified team makes them less efficient (30 PM)

20. 20 RCAP Effort/Schedule Effect University of Southern California Center for Software Engineering CSE USC

21. 21 Next Steps University of Southern California Center for Software Engineering CSE USC Complete another Delphi Round Gather more RAD data  Please contact me if you have some Analyze Data from RAD projects Bayesian Analysis Calibrate Model

22. 22 Issues for Breakout Group University of Southern California Center for Software Engineering CSE USC Complete another Delphi Round Treatment of square root, cube root models Treatment of RAD drivers Relevance to your RAD experience Expediting data collection