Promise Keynote
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Transcript of Promise Keynote
Slide 1
New Directions for Software Metrics
Norman Fenton
Agena Ltd and Queen Mary University of London
PROMISE
20 May 2007
Slide 2
Overview
• History of software metrics
• Good and bad newsHard project constraintsProject trade-offsDecision-making and intervention
• The true objective of software metrics?
• Why we need a causal approach
• Models in action
• Results
Slide 3
Metrics History: Typical Approach
What I really want to measure (Y)
What I can measure (X)
Y = f (X)
Slide 4
Metrics History: the drivers
• ‘productivity’=size/effort
• ‘effort’=a*sizeb
• ‘quality’=defects/size
Slide 5
Metrics history: size matters!
• LOC
• Improved size metrics
• Improved complexity metrics
Slide 6
Some Decent News About Metrics
• Empirical results/banchmarks
• Significant industrial activity
• Academic/research output
• Metrics in programmer toolkits
Slide 7
….But Now the Bad News
• Lack of commercial relevance
• Programmes doomed by data
• Activity poorly motivated
Failed to meet true objective of quantitative risk assessment
Slide 8
Slide 9
Regression models….?
Slide 10
Using metrics and fault data to predict quality
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Post-release faults
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20
30
40 80 120 160
Pre-release faults
?
Slide 11
Pre-release vs post-release faults: actual
Post-release faults
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10
20
30
0 40 80 120 160
Pre-release faults
Slide 12
What we need
What I think is... ?
Slide 13
The Good News
• It is possible to use metrics to meet the real objective
• Don’t need a heavyweight ‘metrics programme’
• A lot of the hard stuff has been done
Slide 14
A Causal Model (Bayesian net)
Residual DefectsTesting Effort
Problemcomplexity
Defects found and fixed
Defects IntroducedDesign processquality
Operational defectsOperational usage
Slide 15
A Model in action
Slide 16
Slide 17
https://intranet.dcs.qmul.ac.uk/courses/coursenotes/DCS235/
Slide 18
Slide 19
Slide 20
A Model in action
Slide 21
Slide 22
Slide 23
A Model in action
Slide 24
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Slide 29
Actual versus predicted defects
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200
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800
1000
1200
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1600
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0 500 1000 1500 2000 2500
Actual defects
pre
dic
ted
def
ects
Slide 31
Conclusions
• Heavyweight data and classical statistics NOT the answer
• Empirical studies laid groundwork• Causal models for quantitative risk
Slide 32
…And
You can use the technology NOW
www.agenarisk.com