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SPIN 23 February 2006 Norman Fenton Agena Ltd and Queen Mary University of London

Improved Software Defect Prediction. SPIN 23 February 2006 Norman Fenton Agena Ltd and Queen Mary University of London. Pre-release testing. Post-release operation. delivery. Using fault data to predict reliability. 30. ?. 20. Post-release faults. 10. 0. 0. 40. 80. 120.

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SPIN 23 February 2006 Norman Fenton Agena Ltd and Queen Mary University of London

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  1. Improved Software Defect Prediction SPIN 23 February 2006 Norman Fenton Agena Ltd and Queen Mary University of London

  2. Pre-release testing Post-release operation delivery Using fault data to predict reliability

  3. 30 ? 20 Post-release faults 10 0 0 40 80 120 160 Pre-release faults Pre-release vs post-release faults?

  4. Pre-release vs post-release faults: actual 30 20 Post-release faults 10 0 0 40 80 120 160 Pre-release faults

  5. Software metrics….?

  6. Regression models….?

  7. Solution: causal models (risk maps)

  8. What’s special about this approach? • Structured • Visual • Robust

  9. The specific problem for Philips actual Defectsfound predicted time

  10. Agena Risk MODIST Fixed Risk Map (AID) Reliability models Background to work with Philips 2004 2001 2002 2003 2000

  11. Projects in the trial (actual number of projects shown in brackets)

  12. Factors used at Philips • Existing code base… • Complexity and management of new requirements ... • Development, testing, rework, process … • Overall project management …

  13. Actual versus predicted defects Correlation coefficient 95%

  14. Validation Summary (Philips’ words) “Bayesian Network approach is innovative and one of the most promising techniques for predicting defects in software projects” “Our evaluation showed excellent results for project sizes between 10 and 90 KLOC” “Initially, projects outside this range were not within the scope of the default model so predictions were less accurate, but accuracy was significantly improved after standard model tailoring” “AgenaRisk is a valuable tool as is”

  15. Specific benefits • Accurate predictions of defects at different phases • Know when to stop testing • Identify where to allocate testing • Minimise the cost of rework • Highlight areas for improvement • Use out of the box now if you have no data • Approach fully customisable • Models arbitrary project life-cycles

  16. Summary • Risk maps – the way forward • Validation results excellent

  17. …And You can use the technology NOW www.agenarisk.com

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