1. 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.
2. 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.
3. 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.
4. 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 Metrics
Chidamber and Kemerer Metrics.
These Software Metrics are used to measure the
quality of the code.
5. 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.
6. 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.
8. 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.
9. 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%.
11. 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.
12. GENERAL QUALITY METRICS
The quality of the software is implemented using
the following metrics:
Code Churns
Code clones
Author Rank
Code Readability
Path Frequency
Path Density
13. 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)
14. 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.
15. 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.
22. 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.
23. 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