Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction
•Download as PPT, PDF•
1 like•550 views
Norman Fenton, Martin Neil, William Marsh, Peter Hearty, Lukasz Radinski, Paul Krause
![Overview ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Recommended
More Related Content
What's hot
What's hot (19)
Similar to Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction
Similar to Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction (20)
Recently uploaded
Recently uploaded (20)
Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction
- 1. Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction Norman Fenton Martin Neil, William Marsh, Peter Hearty and Łukasz Radliński, Paul Krause PROMISE 20 May 2007
- 4. Causal model (Bayesian network) Probability of finding defect Testing process effectiveness Testing process quality Testing effort Testing staff experience Quality of documented test cases Testing process well-defined
- 6. Schematic view of model Existing code base Defect insertion and recovery Testing and rework Design and development Specification and documentation Common influences Scale of new required functionality
- 10. Defects vs size
- 11. Actual versus predicted defects
Editor's Notes
- 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.