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

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Introducing Bayesian Nets in AgenaRisk

An example based on

Software Defect Prediction

- 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

- 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

- 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

Operational defects

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

Residual Defects

Operational defects

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

Residual Defects

Operational usage

Operational 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.

Defects Introduced

Residual Defects

Operational usage

Operational defects

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

Defects Introduced

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

…minus the number you successfully find and fix

Defects Introduced

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

Obviously defects found and fixed is dependent on the number introduced

Defects Introduced

Problem

complexity

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

The number introduced is influenced by problem complexity…

Design process

quality

Defects Introduced

Problem

complexity

Defects found

and fixed

Residual Defects

Operational usage

Operational defects

….and design process quality

Design process

quality

Defects Introduced

Problem

complexity

Testing Effort

Defects found

and fixed

Residual Defects

Operational usage

Operational 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

Here is that very simple model with the probability distributions shown

We are looking at an individual software component in a system

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

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

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

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

Note how the other distributions changed.

The model is doing forward inference to predict defects in operation…..

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

but actually the most likely explanation is very low testing quality.

…and lower than average complexity.

But if we find out that the complexity is actually high…..

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…

Then we have an example of a component with no defects in test ..

…but probably many defects in operation.

But suppose we find out that the test quality was very high.

Then we completely revise out beliefs

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

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

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

The model looks for explanations for such a state of affairs.

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

But if we know it will be subject to high usage…

Then the model adjusts the beliefs about the other uncertain variables.

A combination of lower than average complexity…..

…Higher than average design quality…..

and much higher than average testing quality …..

But suppose we cannot assume our testing is anything other than average…

Then better design quality …..

..and lower complexity are needed …..

But if complexity is very high …..

…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

- 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

In AgenaRisk

www.agenarisk.com