The week's pick
Random Articles
Reseach Article
Bagged SVM Classifier for Software Fault Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 62 - Number 15 |
| Year of Publication: 2013 |
| Authors: Shanthini. A, Vinodhini. G, Chandrasekaran. R M |
10.5120/10156-5030
|
Shanthini. A, Vinodhini. G, Chandrasekaran. R M . Bagged SVM Classifier for Software Fault Prediction. International Journal of Computer Applications. 62, 15 ( January 2013), 21-24. DOI=10.5120/10156-5030
Abstract
Defective modules in the software pose considerable risk by decreasing customer satisfaction and by increasing the development and maintenance costs. Therefore, in software development life cycle, it is essential to predict defective modules in the early stage so as to improve software developers' ability to identify the defect-prone modules and focus quality assurance activities. Many researchers focused on classification algorithm for predicting the software defect. On the other hand, classifiers ensemble can effectively improve classification performance when compared with a single classifier. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for fault prediction. Ensemble classifier was examined for Eclipse Package level dataset and NASA KC1 dataset. From the research, it is clear that proposed ensemble of Support Vector Machine is superior to individual approach for software fault prediction in terms of classification rate through Root Mean Square Error Rate (RMSE), Area Under ROC Curve (AUC- ROC) and Area Under Precision and Recall curve (AUC-PR).
References
- Knab, P. , Pinzger, M. , and Bernstein, A. , 2006. "Predicting defect densities in source code files with decision tree learners," in the 2006 International Workshop on Mining Software Repositories.
- Sandhu, Parvinder Singh, Sunil Kumar and Hardeep Singh, 2007 "Intelligence System for Software Maintenance Severity Prediction",Journal of Computer Science, Vol. 3 (5), pp. 281-288.
- Catal, C. , Diri, B. , and Ozumut, B. , 2007. "An Artificial Immune System Approach for Fault Prediction in Object-Oriented Software," in 2nd International Conference on Dependability of Computer Systems DepCoS-RELCOMEX.
- Menzies, T. , Greenwald, J. , & Frank, A. (2007). Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering, 33(1),
- Olague, H. M. , Etzkorn, L. H. , Gholston, S. , Quattlebaum, S. , 2007. Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Transactions on Software Engineering 33 (6), 402– 419.
- S. Kanmani, V. R. Uthariaraj, V. Sankaranarayanan, P. Thambidurai, Objected-oriented software fault prediction using neural networks, Information and software Technology 49 (5 (2007)) 483 – 492.
- Dr Kadhim M. Breesam, "Metrics for Object Oriented design focusing on class Inheritance metrics", 2nd International conference on dependability of computer system IEEE, 2007.
- K. O. Elish, M. O. Elish, Predicting defect-prone software modules using support vector machines, Journal of Systems and Software 81 (5) (2008) 649– 660.
- I. Gondra, Applying machine learning to software fault-proneness prediction, Journal of System and Software 81(2) (2008) 186-195.
- Amjan Shaik, Dr C. R. K. Reddy, Dr A Damodaran, "Statistical Analysis for Object Oriented Design Software security metrics", International journal of engineering and technology, vol. 2, pg 1136-1142,2010
- Cagatay Catal, "Software fault prediction: A literature review and current trends", Expert Systems with Applications 38 (2011) 4626 – 4636.
Index Terms
Keywords