1 / 10

USC-CSE Annual Research Review

USC-CSE Annual Research Review. COQUALMO Update John D. Powell March 11, 2002. 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.

chibale
Télécharger la présentation

USC-CSE Annual Research Review

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. USC-CSE Annual Research Review COQUALMO Update John D. Powell March 11, 2002

  2. 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 Determine Bayesian A Posteriori Update Gather More Data; Refine Model Boehm et. al. Software Cost Estimation with COCOMO II, Prentice Hall PTR, Upper Saddle River, NJ pp.142

  3. 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 Baseline Defect Intro Rates/Ksloc Rqts Code Dsg 10 20 30 DI_Driver R,1 COCOMO II Cost Drivers DI_Driver R,1 P QAF = DM R 10* 20* 30* Analysis DAF DAF DAF R D C Tools Rating DR_Driver Delivered Defect Density Estimate Peer Reviews R,1 Rating DR_Driver R,2 Test Thoroughness DR_Driver and Tool Rating R,3

  4. COQUALMO Defect Removal Estimates- Nominal Defect Introduction Rates Delivered Defects / KSLOC Composite Defect Removal Rating

  5. 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

  6. 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

  7. 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)

  8. 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 Target (Req/Desg/Code …) Type (Asgn/Checking/Func/ …) Defect Age Impact

  9. 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 Current Data Collection Efforts

  10. 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

More Related