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Introducing Bayesian Nets in AgenaRisk An example based on Software Defect Prediction

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Typical ApplicationsTypical ApplicationsTypical 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 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 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

- 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

- 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

- 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

- 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

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

Operational defects

Predicting software defectsWe know this is clearly dependent on the number of residual defects.

Operational usage

Operational defects

Predicting software defectsBut 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.

Residual Defects

Operational usage

Operational defects

Predicting software defectsThe number of residual defects is determined by the number you introduce during development….

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects…minus the number you successfully find and fix

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defectsObviously defects found and fixed is dependent on the number introduced

Problem

complexity

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defectsThe number introduced is influenced by problem complexity…

quality

Defects Introduced

Problem

complexity

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defects….and design process quality

quality

Defects Introduced

Problem

complexity

Testing Effort

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Predicting software defectsFinally, 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

Here is that very simple model with the probability distributions shown

A Model in action

We are looking at an individual software component in a system

A Model in action

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

A Model in action

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

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 action

As we enter observations about the component the probability distributions update

Here we have entered the observation that this component had 0 defects found and fixed in testing

..and backwards inference to make deductions about design process quality.

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

then the expected number of operational defects increases

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

and we become even more convinced of the inadequate testing

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

So far we have made no observation about operational usage.

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

If, in fact, the operational usage is high…

We are now pretty convinced that the module will be fault free in operation

…And the ‘explanation’ is that the design process is likely to be very high quality

A Model in action

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

A Model in action

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

The most obvious way to achieve such a result is to not use the component much.

…Then we are left with a very skewed distribution for design process quality.

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

- 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

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