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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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Page 1: Promise Keynote

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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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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Metrics History: Typical Approach

What I really want to measure (Y)

What I can measure (X)

Y = f (X)

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Metrics History: the drivers

• ‘productivity’=size/effort

• ‘effort’=a*sizeb

• ‘quality’=defects/size

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Metrics history: size matters!

• LOC

• Improved size metrics

• Improved complexity metrics

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Some Decent News About Metrics

• Empirical results/banchmarks

• Significant industrial activity

• Academic/research output

• Metrics in programmer toolkits

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….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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Regression models….?

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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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Pre-release vs post-release faults: actual

Post-release faults

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Pre-release faults

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What we need

What I think is... ?

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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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A Causal Model (Bayesian net)

Residual DefectsTesting Effort

Problemcomplexity

Defects found and fixed

Defects IntroducedDesign processquality

Operational defectsOperational usage

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A Model in action

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https://intranet.dcs.qmul.ac.uk/courses/coursenotes/DCS235/

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A Model in action

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A Model in action

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Actual versus predicted defects

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1200

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1600

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2000

0 500 1000 1500 2000 2500

Actual defects

pre

dic

ted

def

ects

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Conclusions

• Heavyweight data and classical statistics NOT the answer

• Empirical studies laid groundwork• Causal models for quantitative risk

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…And

You can use the technology NOW

www.agenarisk.com