Applying COCOMO II Effort Multipliers to Simulation Models - PowerPoint PPT Presentation

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Applying COCOMO II Effort Multipliers to Simulation Models

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  1. Applying COCOMO II Effort Multipliers to Simulation Models 16th International Forum on COCOMO and Software Cost Modeling Jongmoon Baik and Nancy Eickelmann Software and System Engineering Research Laboratory Motorola Labs Oct 25, 2001

  2. Outline • Problems • What is Model & Simulation? • Simulation Model Comparison • Why COCOMO II Parameters? • Example • System Test Process • DSD (Distributed Software Development) • Future Work

  3. Problems • Most Critical Decision  Early in the lifecycle • Little information available to assess the proposed process changes and technology insertions • Difficult to understand problems in the current complex processes

  4. Model and Simulation • Model “ An abstraction or simplified representation of a real or conceptual complex system. It is designed to display significant features and characteristics of the system under study.” • Simulation “Computerized model that possesses the characteristics described above and that represents some dynamic system or phenomenon.”

  5. Primary Benefits of Simulation • Process Characterization • Quantitative Analysis for Proposed Process Changes and Technology Insertions • Support Decision Making and Risk Assessment • Support to achieve Higher CMM levels • Quantitative process Management and software Quality Management (CMM-Level 4) • Process/Technology Change Management and Continuous Process Improvement (CMM_Level 5) • Project Planning including Effort/Schedule/Cost Estimation

  6. Proposed Plan for Process Changes and Technology Insertions Refine Implementation of Process Changes and Technology Insertions Process Manager, Team, or Cost Estimators Feed Back Proposed Plan for Process Changes and Technology Insertions Process Manager, Team, or Cost Estimators Simulation Model Feed Forward Refine Quantitative Control Feed Back Implementation of Process Changes and Technology Insertions Analysis Simulation Model Change Process Comparison

  7. Simulation Model Comparison

  8. Why COCOMO II Parameters? • Widely accepted public cost model • Calibrated with 161 actual project data • Objective impact analysis for each factor - If There is no data available for the parameter • Can be calibrated from the organization’s historical data • Dynamic effect can be added into simulation model • Different rating values over time (COCOMO81 Detailed COCOMO)

  9. COCOMO II EMs to Simulation Product: RELY, DATA, DOCU,CPLX, RUSE Project: TOOL, SITE, SCED Personnel: ACAP, APEX, PCAP, PLEX, LTEX, PCON Platform: TIME, STOR, PVOL

  10. SystemRequirements SystemBetaTest SystemSystemTest Text SystemFunctional Requirements Text SystemIntegrationTest SystemArchitectureRequirements eMSC SystemEarlyIntegrationtesting SystemEarly SDLIntegrationtesting SystemDetailedDesign SL + SDL BoxSystemTest BoxRqmts Text BoxDesign eMSC eMSC, SDL(blocs) Box IntegrationTest SubsystemSystem Test(host/target) SubsystemIntegrationTest SDL (blocs) System Validation Activities SDL(process), C Box Validation Activities Box(auto)Coding BoxUnit Test eMSC Extended Message Sequence Charts SDL Specification and Design Language SL “SL”, data language System Test Process Simulation • Baseline a current System Test Process • Predict impact of process change and technology insertion on schedule, quality, effort

  11. SDL Example: LTEX • Used to assess the impact of SDL/MSC insertion into a system test process • Map SDL Ranks (1-5) to LTEX ratings (VL to VH)

  12. Schedule Saving by LTEX • Total Savings • Nominal (N to VH)  10.05 Days • Maximum (VL to VH)  24.51 Days

  13. DSD [Distributed Software Development] TEAM SITE • Communication Breakdown • Coordination Breakdown • Geographical Dispersion • Loss of Team Cohesiveness • Cultural Differences Source: IEEE Software March/April 2001, Robert D. Battin et. al. “Leveraging Resources in Global Software Development” TOOL PREC PMAT

  14. Future Work • More Application of Effort Multipliers to Simulation of Process Segments • Refine the simulation model Through the calibration of applied COCOMO II parameters • Expand simulation model to find out the dynamic impacts of the parameters