1 / 14

COQUALMO and Orthogonal Defect Classification(ODC)

COQUALMO and Orthogonal Defect Classification(ODC). Keun Lee ( keunlee@sunset.usc.edu ) & Sunita Chulani (Sunita_Chulani@us.ibm.com). COQUALMO and Orthogonal Defect Classification(ODC). Current COQUALMO Model - Results and Challenges COQUALMO – ODC Research Approach Example Results

Download Presentation

COQUALMO and Orthogonal Defect Classification(ODC)

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. COQUALMO and Orthogonal Defect Classification(ODC) Keun Lee ( keunlee@sunset.usc.edu) & Sunita Chulani (Sunita_Chulani@us.ibm.com)

  2. COQUALMO and Orthogonal Defect Classification(ODC) • Current COQUALMO Model • - Results and Challenges • COQUALMO – ODC Research Approach • Example • Results • Issues and Research Plans

  3. Current COQUALMO System COCOMO II Software development effort, cost and schedule estimate COQUALMO Software Size Estimate Defect Introduction Model Software platform, Project, product and personnel attributes Number of residual defects Defect density per unit of size Defect Removal Model Defect removal profile levels Automation, Reviews, Testing

  4. Partion of COQUALMO Rating Scale COCOMO II p.263

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

  6. Multiplicative Defect Removal Model - Example : Code Defects; High Ratings • Analysis : 0.7 of defects remaining • Reviews : 0.4 of defects remaining • Testing : 0.31 of defects remaining • Together : (0.7)(0.4)(0.31) = 0.09 of defects remaining • How valid is this? • All catch same defects : 0.31 of defects remaining • Mostly catch different defects : ~0.01 of defects remaining

  7. Example UMD-USC CeBASE Data Comparisons • “Under specified conditions, …” • Peer reviews are more effective than functional testing for faults of omission and incorrect specification(UMD, USC) • Functional testing is more effective than reviews for faults concerning numerical approximations and control flow(UMD,USC) • Both are about equally effective for results concerning typos, algorithms, and incorrect logic(UMD,USC)

  8. ODC Data Attractive for Extending COQUALMO - IBM Results (Chillarege, 1996)

  9. COQUALMO Extension Research Approach • Extend COQUALMO to cover major ODC categories • Collaborate with industry ODC users • IBM, Motorola underway • Two more sources being explored • Obtain first-land experience on USC digital library projects • Completed IBM ODC training • Initial front-end data collection and analysis

  10. Digital Library Analysis to Date - in ODC terms • Artifacts • - Operational Concept, Requirements, Software Architecture documents • Activities • - Perspective-based Fagan inspections • Triggers • - Environment or condition that causes defect

  11. Front End (Information Development) Triggers • Clarity – confusing or difficulty to understand information. • Style – inappropriate or difficulty to understand the manner of expression • Accuracy – incorrect information • Task Orientation - inappropriate presentation to perform task • Organization – relationship between parts is not conveyed • Completeness – missing information. • Consistency – the expression manner is not displayed in a consist manner

  12. Initial Digital Library Project ODC Analysis - Trigger percentage Distribution by Team

  13. Initial Digital Library Project ODC Analysis - Number of Triggers Defects by Team

  14. Issues and Research Plans • Understand anomalies in Digital Library Data • Number of Team 22 defects • Team 4 completeness defects • Due to differences in artifacts or procedures? • Continue Digital Library ODC collection & analysis • - Detailed Design, code, test • Obtain, analyze industry ODC data • - Looking for more sources of ODC Data

More Related