promise keynote
DESCRIPTION
Norman FentonTRANSCRIPT
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Slide 1
New Directions for Software Metrics
Norman Fenton
Agena Ltd and Queen Mary University of London
PROMISE
20 May 2007
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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
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Slide 3
Metrics History: Typical Approach
What I really want to measure (Y)
What I can measure (X)
Y = f (X)
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Slide 4
Metrics History: the drivers
• ‘productivity’=size/effort
• ‘effort’=a*sizeb
• ‘quality’=defects/size
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Slide 5
Metrics history: size matters!
• LOC
• Improved size metrics
• Improved complexity metrics
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Slide 6
Some Decent News About Metrics
• Empirical results/banchmarks
• Significant industrial activity
• Academic/research output
• Metrics in programmer toolkits
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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
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Slide 8
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Slide 9
Regression models….?
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Slide 10
Using metrics and fault data to predict quality
0
0
Post-release faults
10
20
30
40 80 120 160
Pre-release faults
?
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Slide 11
Pre-release vs post-release faults: actual
Post-release faults
0
10
20
30
0 40 80 120 160
Pre-release faults
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Slide 12
What we need
What I think is... ?
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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
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Slide 14
A Causal Model (Bayesian net)
Residual DefectsTesting Effort
Problemcomplexity
Defects found and fixed
Defects IntroducedDesign processquality
Operational defectsOperational usage
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Slide 15
A Model in action
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Slide 17
https://intranet.dcs.qmul.ac.uk/courses/coursenotes/DCS235/
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Slide 19
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Slide 20
A Model in action
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Slide 22
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Slide 23
A Model in action
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Slide 28
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Slide 29
Actual versus predicted defects
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 500 1000 1500 2000 2500
Actual defects
pre
dic
ted
def
ects
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Slide 31
Conclusions
• Heavyweight data and classical statistics NOT the answer
• Empirical studies laid groundwork• Causal models for quantitative risk
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Slide 32
…And
You can use the technology NOW
www.agenarisk.com