SOFTWARE METRICS

Software Metrics :Roadmap Norman E Fenton and Martin Neil

Presented by Santhosh Kumar Grandai

OVERVIEW

• What is Software Metric?

• About Software Metrics

• Regression Models

• Causal Models - Bayesian Belief Net(BBN)

• Why do we need Causal Model?

• Conclusion

SOFTWARE METRIC

• Metrics is Measurement.

• Various Metrics on various Phases of life cycle model.

• Purpose – To detect problems early in the software process.

About Software Metrics..

• Over 30 years old.

• Mid 1960’s – LOC used as the basis for measuring Programming productivity and Effort.

• Has Two Components. 1. Component Concerned with defining the actual measures.

2. Component Concerned with how we collect,manage and use the measures.

About Software Metrics…

About Software Metrics….

• External Attributes

- Ones interested to know about.

• Internal Attributes

- Control and Measure Directly.

• To Predict Effort/Cost of development Process.• To Predict quality of software products.

About Software Metrics and Regression

Models

• First Key Metric was Lines of code(LOC).

• Quality Prediction

- Defect Density

• Quality and Effort

Quality

Effort/Cost

Product Size

REGRESSION MODELS

• Does not support quantitative managerial decision making during software life cycle

- No support for risk assessment

- No support for risk reduction• Misunderstanding between cause and effect.• Does not consider causality,uncertainty,evidence.

REGRESSION MODEL

• An practical Example,

- Data on car accidents show that Jan and Feb are the months will fewest fatalities.

- An regression model is built from available data.

- No causal relationship. - Sensible decision about safest time to drive cannot be

made.

Month Number OfFatalities

CAUSAL MODEL

• An practical Example,Month

Weather Conditions

Road Conditions

Number ofJourneys

Average Speed

Number of fatalities

CAUSAL MODEL

• In Software Metrics - Dominated by Regression Models.

- Need causal Models.

Problem complexity

size

Effortschedule

Resourcequality

Product Quality

CAUSAL MODEL

• Can give answers to questions

- For a specification of this complexity,and given these limited resources,how likely I am achieve a product of suitable quality?

- How much can I scale down the resources if I am prepared to put up with a product of specified quality?.

• Regression models cannot.

Analysis

• We see that only defect counts are being used in measure of quality. Not true

• Consider Hypothesis

“Suppose you know that a large number of defects are found in a software module prior to release.Is it likely that this module will reveal many defects post-release?.”

- Yes, Popularly believed.

- Empirical Evidence shows it is an invalid hypothesis.

Analysis

Analysis

• Modules with high pre – release faults had less post-release faults,

- The amount of testing must be incorporated into any predictive module of defects.

- Operational usage must also be incorporated.

REGRESSION MODELS

• Regression Models,

- cannot consider resourcing constraints.

- cannot handle uncertainty.

- no cause and effect relationship.• Not suitable for risk assessment and reduction.

CAUSAL MODEL

• Causal models can handle,

- Diverse process and product variables.

- Genuine cause and effect relationship.

- Empirical evidence and expert judgement.

- Uncertainty.

• It covers the crucial concepts missing from the classical regression-based approaches.

BBN

• Bayesian Belief Nets(BBN) is a type of causal model,which uses Bayesian probability.

• BBN is a graphical network together with the associated set of probability tables.

- Nodes represent Uncertain values.

- Causal relationship.

BBN

• To predict defect counts for software modules.

BBN

BBN

• For given Input pre-release defects(less than 10) and many post-release(between 30 and 40),

Output is ‘very low’ amount of testing was done.• Given the Evidence of a variable BBN calculates

the Probability of each state.

BBN

• Absence of BBN for a long time

- No proper algorithm.

- No software tool.

• Hugin tool is used.

BBN

• Used to,

- Provide safety or reliability arguments for critical systems.

- Provide improved reliability predictions of prototype military vehicles.

- Provide predictions of insurance risk and operational risk.• Drawback, - cannot be used in decision making in deployment of systems . -lacks political,financial,environmental criteria. - Multi Criteria Decision Aid(MCDA) deals with the above criteria. - Deployment of system – combination of two.

BBN

• Technology Transfer

- Project managers are more likely to use this model for decision – making.

- They do not understand the underlying theory.

- Provide simple,configurable front ends.

Conclusion

• Statistical models do not provide decision – support for risk assessment and reduction.

• Causal models like BBN do provide decision – support for risk assessment and reduction.

• Organizations that collect basic metrics data and follow defined life-cycles,will be able to apply causal models effectively.

References

• Software Metrics : Roadmap,Norman E Fenton & Martin Neil,Computer Science Department,Queen Mary and Westfield College,London.

• http://www.dcs.qmul.ac.uk/~norman/BBNs/BBNs.htm

Thank you!!!

Questions ?…