University of Southern California Center for Software Engineering CSE USC USC-CSE Annual Research Review COQUALMO Update John D. Powell March 11, 2002

University of Southern California Center for Software Engineering CSE USC Gather More Data; Refine Model Determine Bayesian A Posteriori Update COQUALMO’s Current Stage of Development Analyze Existing Literature Perform Behavioral Analysis Determine Form of Model Identify relative significance of Factors Perform Expert-Judgment, Delphi Assessment Gather Project Data Boehm et. al. Software Cost Estimation with COCOMO II, Prentice Hall PTR, Upper Saddle River, NJ pp.142

University of Southern California Center for Software Engineering CSE USC COQUALMO - Introduction Defect Introduction COCOMOII Drivers to Collect / Calibrate Baseline Defect Introduction Rates / KSLOC to Calibrate Defect Removal Defect Removal Profile Levels to Collect / Calibrate Adjustment Factors to Calibrate DI_Driver R,1 DI_Driver R,1 QAF =  DM R DR_Driver R,1 DR_Driver R,2 DR_Driver R,3 Rqts 1020 Code 30 Baseline Defect Intro Rates/Ksloc 10* DAF R 20* DAF D 30* DAF C COCOMO II Cost Drivers Analysis Tools Rating Peer Reviews Rating Test Thoroughness and Tool Rating Dsg Delivered Defect Density Estimate

University of Southern California Center for Software Engineering CSE USC Delivered Defects / KSLOC Composite Defect Removal Rating COQUALMO Defect Removal Estimates - Nominal Defect Introduction Rates

University of Southern California Center for Software Engineering CSE USC COQUALMO Input Collection Leveraging COCOMO II / COQUALMO Rosetta Stone –Collect Only Defect Removal Profile Levels Leveraging Data from other COCOMO Suite Models –Collect Data “missing” COCOMO II Inputs –Collect Defect Removal Profile Levels Otherwise, Collect full COQUALMO Input Set

University of Southern California Center for Software Engineering CSE USC COQUALMO Output & Actuals COQUALMO Outputs –Number of Defects Introduced (Req./Des./Code) –Number of Defects Removed (Req./Des./Code) –Number of Residual Defects (Req./Des./Code) Actuals –Defect Reports Opened –Defect Reports Closed –Known Defects Remaining at Delivery

University of Southern California Center for Software Engineering CSE USC COQUALMO Output & Actuals (cont’) Categorization of Tracked Defects ???  Req./Des./Code –Translation – examination and counting –Transducer – automated translation when cost/effort effective –Rosetta Stone approach for common Schemes such as Orthogonal Defect Classification (ODC)

University of Southern California Center for Software Engineering CSE USC ODC Rosetta Stone Issues Targets/Types offer Categorization Opportunities –Targets not a complete & direct mapping –Types offer help in completing the mapping –Each Target category has type associations Mapping must preserve DB Consistency with non-ODC data points Defect Target Type Impact Age (Req/Desg/Code …) (Asgn/Checking/Func/ …)

University of Southern California Center for Software Engineering CSE USC Current Data Collection Efforts Hughes (Raytheon) TRW IBM JPL Northrop Motorola Raytheon 2 Data points complete 2 Data points under analysis 4-12 specifically foreseeable data points Active interest by affiliates for contribution of multiple data points yet to be specified

University of Southern California Center for Software Engineering CSE USC Future Work Continued Data Collection –Calibration & future refinement of COQUALMO –Feedback to Contributors regarding Analysis of their Data Contributions Investment levels for achieving Defect Removal Rating Levels –Relation to Rework Reduction & Cost Savings –Similar to COPROMO Model