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Introducing Bayesian Nets in AgenaRisk An example based on Software Defect Prediction. Typical Applications. Predicting reliability of critical systems Software defect prediction Aircraft accident traffic risk Warranty return rates of electronic parts

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Presentation Transcript
slide1

Introducing Bayesian Nets in AgenaRisk

An example based on

Software Defect Prediction

typical applications
Typical Applications
  • Predicting reliability of critical systems
  • Software defect prediction
  • Aircraft accident traffic risk
  • Warranty return rates of electronic parts
  • Operational risk in financial institutions
  • Hazards in petrochemical industry
typical applications1
Typical Applications
  • Predicting reliability of critical systems
  • Software defect prediction
  • Aircraft accident traffic risk
  • Warranty return rates of electronic parts
  • Operational risk in financial institutions
  • Hazards in petrochemical industry
typical applications2
Typical Applications
  • Predicting reliability of critical systems
  • Software defect prediction
  • Aircraft accident traffic risk
  • Warranty return rates of electronic parts
  • Operational risk in financial institutions
  • Hazards in petrochemical industry
typical applications3
Typical Applications
  • Predicting reliability of critical systems
  • Software defect prediction
  • Aircraft accident traffic risk
  • Warranty return rates of electronic parts
  • Operational risk in financial institutions
  • Hazards in petrochemical industry
typical applications4
Typical Applications
  • Predicting reliability of critical systems
  • Software defect prediction
  • Aircraft accident traffic risk
  • Warranty return rates of electronic parts
  • Operational risk in financial institutions
  • Hazards in petrochemical industry
typical applications5
Typical Applications
  • Predicting reliability of critical systems
  • Software defect prediction
  • Aircraft accident traffic risk
  • Warranty return rates of electronic parts
  • Operational risk in financial institutions
  • Hazards in petrochemical industry
a detailed example
A Detailed Example
  • What follows is a demo of a simplified version of a Bayesian net model to provide more accurate predictions of software defects
  • Many organisations worldwide have now used models based around this one
predicting software defects

Operational defects

Predicting software defects

The number of operational defects (i.e. those found by customers) is what we are really interested in predicting

predicting software defects1

Residual Defects

Operational defects

Predicting software defects

We know this is clearly dependent on the number of residual defects.

predicting software defects2

Residual Defects

Operational usage

Operational defects

Predicting software defects

But it is also critically dependent on the amount of operational usage. If you do not use the system you will find no defects irrespective of the number there.

predicting software defects3

Defects Introduced

Residual Defects

Operational usage

Operational defects

Predicting software defects

The number of residual defects is determined by the number you introduce during development….

predicting software defects4

Defects Introduced

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects

…minus the number you successfully find and fix

predicting software defects5

Defects Introduced

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects

Obviously defects found and fixed is dependent on the number introduced

predicting software defects6

Defects Introduced

Problem

complexity

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects

The number introduced is influenced by problem complexity…

predicting software defects7

Design process

quality

Defects Introduced

Problem

complexity

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects

….and design process quality

predicting software defects8

Design process

quality

Defects Introduced

Problem

complexity

Testing Effort

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects

Finally, how many defects you find is influenced not just by the number there to find but also by the amount of testing effort

a model in action
A Model in action

Here is that very simple model with the probability distributions shown

a model in action1
A Model in action

We are looking at an individual software component in a system

a model in action2
A Model in action

The prior probability distributions represent our uncertainty before we enter any specific information about this component.

a model in action3
A Model in action

So the component is just as likely to have very high complexity as very low

a model in action4
A Model in action

and the number of defects found and fixed in testing is in a wide range where the median value is about 20.

a model in action5
A Model in action

As we enter observations about the component the probability distributions update

slide32

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

then the expected number of operational defects increases

slide33

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

and we become even more convinced of the inadequate testing

slide34

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

So far we have made no observation about operational usage.

slide35

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

If, in fact, the operational usage is high…

a model in action6
A Model in action

we reset the model and this time use the model to argue backwards

a model in action7
A Model in action

Suppose we know that this is a critical component that has a requirement for 0 defects in operation…

slide56

What the model is saying is that, if these are the true requirements for the component then you are very unlikely to achieve them unless you have a very good design process

making better decisions
Making better decisions
  • That was a simplified version of model produced for Philips
  • Helped Philips make critical decisions about when to release software for electronic components
  • 95% accuracy in defect prediction – much better than can be achieved by traditional statistical methods
model implementation
Model Implementation

In AgenaRisk

www.agenarisk.com

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