MEASURING THE CODE QUALITY USING SOFTWARE METRICS – TO IMPROVE THE EFFICIENCY OF SPECIFICATION

MINING

Guided By

Ms.P.R.Piriyankaa.,ME

Assistant Professor.

Presented By,

M.Geethanjali (ME).,

Sri Krishna College of Engg and Tech.

INTRODUCTION

Incorrect and buggy software costs up to $70 Billion each year in US.

Formal Specifications defines testing, optimization, refactoring, documentation, debugging and repair.

False Positive rates – We think there is a vulnerability but actually that is not present.

PROBLEM STATEMENT

The cost of Software Maintenance consumes up to 90% of the total project cost and 60% of the maintenance time.

Formal Specifications are very necessary but they are difficult for programmers to write them manually.

Existing automatic specification mining produces high false positive rates.

EXISTING SYSTEM

Formal specification is done for each and every software and the quality of the code is checked.

Set of software Metrics are used to measure the quality of the software.General Quality MetricsChidamber and Kemerer Metrics.

These Software Metrics are used to measure the quality of the code.

EXISTING SYSTEM CONT...

The quality of the code is lifted with the results obtained.

Prediction is used to compare the obtained results with randomly generated learned data items.

Automatic specification miner that balances the true and false positive specifications.True positive – Required behaviour.False positives – Non-Required behaviour.

DISADVANTAGES

The false positive rates are reduced from 90% to an average of 30%.

The accuracy of the software is only 80%.

The computation time is low.

PROPOSED SYSTEM

PROPOSED SYSTEM

The classification is based on Support Vector Machine Algorithm.

The measured attributes of the software is compared with the training dataset.

The accuracy of the software is calculated.

The False Positive rate for the specific software is also found.

ADVANTAGES

Reduces the burden of manual inspection of the code.

By knowing the quality of the code before the deployment the developers can easily lift the quality.

The accuracy of the software is about 95%. Minimises the false positive rates from 90% to

5%.

BLOCK DIAGRAM

LIST OF MODULES

General Code Quality Metrics.

Code quality of complexity metrics.

Implementation of mining algorithm – Naive Bayes

Algorithm

Implementation of mining algorithm – Support

Vector Machine Algorithm.

Finding the False positive rates using learning model.

GENERAL QUALITY METRICS

The quality of the software is implemented using the following metrics:Code ChurnsCode clonesAuthor RankCode ReadabilityPath FrequencyPath Density

CHIDAMBER & KEMERER METRICS

These are also known as Object Oriented Metrics:Weighted Methods per class (WMC)Depth of Inheritance (DIT)Number of children (NOC)Coupling between Objects (CBO)

PREDICTION ANALYSIS

The dataset will contain the randomly generated learned data items.

Naive Bayes algorithm is used. The measured result of the software is compared

along with the data set. The predicted result for the selected software

will be displayed. Using this result the quality of the code can be

determined.

PREDICTION USING SVM

The measured attributes are compared with the learned dataset.

The accuracy of the for the selected software will be displayed.

The false positive rates are obtained.

GENERAL CODE QUALITY METRICS

CODE QUALITY OF CK METRICS

PREDICTION ANALYSIS

FALSE POSITIVES & ACCURACY USING SVM

COMPARISON OF ACCURACY

COMPARISON OF FALSE POSITIVE RATE

CONCLUSION

Since the quality of the code is checked before deploying the software, the quality of the software will be assured.

The cost spent for maintenance will also be reduced.

Compared to other automatic miners the false positive rate is reduced to a negligible value.

REFERENCES

Measuring Code Quality to improve specification mining – Claire Le Goues.

A study of consistent and inconsistent changes to code clones –Jens Krinke.

Who are are Source code contributers and how do they change? – Massimiliano Di Penta.

The road not taken: Estimating the Path Execution Frequency Statically – Raymond P.L.Buse

THANK YOU!!!