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Seven Key Measures for Software Testing

Specialist Interest Group in Software Testing. Seven Key Measures for Software Testing. Graham Thomas RCOG, 15 th June 2006. Abstract. Last year I came across the worst measurement example that I had seen in over 20 years experience of IT.

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Seven Key Measures for Software Testing

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  1. Specialist Interest Groupin Software Testing Seven Key Measures for Software Testing Graham Thomas RCOG, 15th June 2006

  2. Abstract Last year I came across the worst measurement example that I had seen in over 20 years experience of IT. Part of the problem comes from the fact that there isn’t a standard set of measures, so should we actually get upset when software testers measure the wrong thing, in the wrong way, and then report it badly? Actually no! Not until there is a standard definition for software test measurement and reporting. So there is the challenge for this presentation. To present a standard set of measures, metrics and reports for software testing so that there can no longer be any excuse. This presentation proposes 7 key measures across the software testing lifecycle, covering; Planning, Risk, Test Preparation, Test Execution and Defect analysis. The presentation will also identify effective ways to present the 7 key measures in the form of a practical model. 2

  3. Agenda • An example Test Report • Definition of Measurement and Metric • Seven Key Measures • Weekly Reporting • More measures and metrics • Tips for success • Conclusion 3

  4. An example Test Report • Lets look at an example test report • Example Test Report • To summarise • Poor presentation • Unable to quickly, simply and easily get a view of testing • Too much information • Difficult to compare data • Real message obscured • Actually unintelligible ! 4

  5. DefinitionMeasurement - Metric • Measurement “The act or process of measuring” • Metric “A calculated term or enumeration representing some aspect of biological assemblage, function, or other measurable aspect and is a characteristic of the biota that changes in some predictable way with increased human influence” Ref: Minnesota Pollution Control Agency Biological Monitoring Program Glossary of Terms 5

  6. Testing Progress 6

  7. Scripting Progress 7

  8. Risk Profile 8

  9. Risk Mitigation 9

  10. Fault S-Curve 10

  11. Environment Availability 11

  12. Coverage 12

  13. Config Status Metrics Analysis Risk Weekly Report 13

  14. More Measures and Metrics The Stroop Effect ZYP QLEKF SUWRG XCIDB WOPR ZYP QLEKF XCIDB SUWRG WOPR SUWRG ZYP XCIDB QLEKF WOPR RED BLACK YELLOW BLUE RED GREEN YELLOW BLACK BLUE BLACK RED YELLOW GREEN BLUE GREEN • Use these views to support the view and message • Defects by Type / Severity / Priority • Defect Hot Spot Analysis • Defect Age Analysis • Causal Analysis • Metrics • Defects / Faults per KLOC / KXLOC • Defects per Requirement • Days Test Effort per Requirement • DDP 14

  15. A Few Tips for successful charting • EasilyDistinguishableColours • Consistent look and feel • If you shade then -light at the topdark at the bottom • REDmeansDANGER ! • Cumulative totals enable to be spotted • Remember it is the content that is important Trends 15

  16. Conclusions • There are other things to measure than just defects • Report trends rather than snapshot measurements • Limit the amount that you report to increase impact • Be consistent in your reporting • Explain what you mean by your measurements - don’t assume that others will automatically know 16

  17. Contact Details Graham Thomas Independent Consultant  graham@badgerscroft.com  +44 7973 387 853 www.badgerscroft.com 17

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