The week's pick
Reseach Article
Adaptive Approach of Fault Prediction in Software Modules by using Discriminative and Generative Model of Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 12 |
| Year of Publication: 2013 |
| Authors: Varneet Kaur, Amit Arora |
10.5120/12937-9964
|
Varneet Kaur, Amit Arora . Adaptive Approach of Fault Prediction in Software Modules by using Discriminative and Generative Model of Machine Learning. International Journal of Computer Applications. 74, 12 ( July 2013), 17-22. DOI=10.5120/12937-9964
Abstract
Software quality assurance is the most important activity during the development of software. Defective software modules may increase costs and decrease customer satisfaction. Hence, effective defect prediction models or techniques are very important in order to deliver efficient software. In this research different machine learning algorithms are used to predict three main prediction performance measures i. e. precision, recall and f-measure. The accuracy of the software modules is being calculated. Different classifiers are also used in order to predict the values of these measures by using important attributes only. The results obtained after applying both the techniques i. e. attribute selection and without attribute selection, on all the datasets, are then analysed and best predicted results are chosen in order to predict the correct values of prediction performance measures. The accuracy of some software modules can be improved to 91. 16%, recall and precision to 1 after using attribute selection techniques in CM1 dataset. In PC1 dataset the accuracy has been improved to 93. 778%.
References
- T. Menzies et al. , "Mining Repositories to Assist in Project Planning and Resource Allocation," Proc. 1st Workshop on Mining Software Repositories (MSR 04), 2004, http://msr. uwaterloo. ca/papers/Menzies. pdf.
- T. M. Khoshgoftaar and N. Seliya, "The Necessity ofAssuring Quality in Software Measurement Data," Proc. 10th Int'l Symp. Software Metrics (METRICS 04),IEEE CS Press, 2004, pp. 119–130.
- L. Guo et al. , "Robust Prediction of Fault Proneness by Random Forests," Proc. 15th Int'l Symp. Software Reliability Eng. (ISSRE 04), IEEE CS Press, 2004, pp. 417–428.
- A. G. Koru and J. Tian, "An Empirical Comparison and Characterization of High Defect and High Complexity Modules," J. Systems and Software, vol. 67, no. 3, 2003, pp. 153–163.
- J. S. Shirabad and T. J. Menzies, "The PROMISE Repository of Software Engineering Databases," School of Information Technology and Engineering, University of Ottawa, Canada, 2005.
- Lan Guo, Yan Ma, Bojan Cukic, Harshinder Singh, " Robust Prediction of Fault-pronness of Random Forests".
- Ian H. Witten and Eibe Frank, "Data Mining- Practical Machine learning Tools and Techniques", Second Edition, © 2005 by Elsevier Inc.
- Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, John C. Platt, Microsoft Research jplatt@microsoft. com Technical Report MSR-TR-98-14 April 21, 1998.
- http://www. stat. berkeley. edu/users/breiman/RandomForests
- http://promise. site. uottawa. ca/SERepository/datsaets-page. html
- http://www. stat. berkeley. edu/users/breiman/RandomForests/,http://docs. opencv. org/modules/ml/doc/random_trees. html
Index Terms
Keywords