Promise Keynote
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- The document discusses the history and limitations of traditional software metrics approaches, noting a lack of commercial relevance and failure to meet the objective of quantitative risk assessment. - It proposes that causal Bayesian network models provide a better approach to using metrics to quantitatively predict and manage risks, allowing decision-making and intervention. - An example causal model is presented relating defects introduced and found to factors like complexity, testing effort, and design quality, with results showing actual versus predicted defects.
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Promise Keynote
- 1. New Directions for Software Metrics Norman Fenton Agena Ltd and Queen Mary University of London PROMISE 20 May 2007
- 3. Metrics History: Typical Approach What I really want to measure (Y) What I can measure (X) Y = f (X)
- 10. Using metrics and fault data to predict quality 0 0 Post-release faults 10 20 30 40 80 120 160 Pre-release faults ?
- 11. Pre-release vs post-release faults: actual Post-release faults 0 10 20 30 0 40 80 120 160 Pre-release faults
- 12. What we need What I think is... ?
- 14. A Causal Model (Bayesian net) Residual Defects Testing Effort Problem complexity Defects found and fixed Defects Introduced Design process quality Operational defects Operational usage
- 15. A Model in action
- 20. A Model in action
- 23. A Model in action
- 29. Actual versus predicted defects
Notas del editor
- Joke about Savoy. Who I am – show my book. I’ll have something more to say about this book shortly. One of the best books on software metrics was written by Bob Grady of Hewlett Packard in 1987. Bob was responsible for what I believe was recognised as the first true company-wide metrics programme. And his book described the techniques and experiences associated with that at HP. A few years later I was at a meeting where Bob told an interesting story about that metrics programme. He said that one of the main objectives of the programme was to achieve process improvement by learning from metrics what process activities worked and what ones didn’t. To do this they looked at those projects that in metrics terms were considered most successful. These were the projects with especially low rates of customer-reported defects. The idea was to learn what processes characterised such successful projects. It turned out that what they learned from this was very different to what they had expected. I am not going to tell you what it was they learnt until the end of my presentation; by then you may have worked it out for yourselves.