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Software Fault Prediction using Ensemble Techniques
Anil Kumar Pandey, Manjari Gupta
DST-CIMS, Banaras Hindu University, Lanka, Varanasi-221005 & Uttar Pradesh, India
Abstract: In recent years, software fault classification became an important research area for the development of reliable and high quality software products. The fault classification helps in identification of fault software modules and allows the developers to concentrate on that module. It helps the developer to save the time and control financial losses to industry. Therefore, in this paper, a novel fault classification method based on feature ranking algorithms and ensemble techniques is proposed. The number of features available in the metrics are selected to represent the fault using feature ranking algorithms and operated on ensemble techniques to check the performance. Also, various hyper parameters are tuned for the ensemble techniques to identify the best model. The experimental result demonstrates the good results for bagging with K-nearest neighbor and random forest in comparison with other methods.
Keywords: Ensemble Technique, Bagging, Nearest- Neighbour, Feature Ranking, Software Fault Prediction (SFP), Cognitive Computing Software Fault Prediction using Ensemble Techniques
DOI:https://doi.org/10.6025/jnt/2020/11/1/25-41
Full_Text   PDF 2.76 MB   Download:   330  times
References:

[1] Chidamber, S. R., Kemerer, C. F. (1994). A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20 (6) 476-493.
[2] Turhan, B., Bener, A. (2009). Analysis of Naive Bayes’ assumptions on software fault data: An empirical study. Data & Knowledge Engineering, 68 (2) 278-290.
[3] Jureczko, M., Madeyski, L. (2010). Prediction of defects based on software metrics - identification of project classes. Proceedings of the National Conference on Software Engineering (KKIO 2010). PWNT, p. 185-192.
[4] Chen, Y., Shen, X. H., Du, P., Ge, B. (2010). February. Research on software defect prediction based on data mining. In: Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on (1, p. 563-567). IEEE.
[5] Najadat, H., Alsmadi, I. (2012). Enhance rule based detection for software fault prone modules. International Journal of Software Engineering and Its Applications, 6 (1) 75-86.
[6] Okutan, A., Yýldýz, O. T. (2014). Software defect prediction using Bayesian networks. Empirical Software Engineering, 19 (1) 154-181.
[7] Phyu, T. N. (2009 March). Survey of classification techniques in data mining. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists (1, p. 18-20).
[8] Kaur, A., Kaur, I. (2014). Empirical evaluation of machine learning algorithms for fault prediction. Lecture Notes on Software Engineering, 2 (2) 176.
[9] Zhang, W., Yang, Y., Wang, Q. (2015). Using Bayesian regression and EM algorithm with missing handling for software effort prediction. Information and Software Technology, 58, 58-70.
[10] Sankar, K., Kannan, S., Jennifer, P. (2014). Prediction of code fault using Naive Bayes and SVM classifiers. Middle-East Journal of Scientific Research, 20 (1) 108-113.
[11] Jureczko, M., Madeyski, L. (2010 September). Towards identifying software project clusters with regard to defect prediction. In: Proceedings of the 6th International Conference on Predictive Models in Software Engineering (p. 9). ACM.
[12] Turhan, B., Menzies, T., Bener, A. B., Stefano, Di., J. (2009). On the relative value of cross-company and within-company data for defect prediction. Empirical Software Engineering, 14 (5) 540-578.
[13] Turhan, B., Bener, A., Menzies, T. (2010 June). Regularities in learning defect predictors. In: International Conference on Product Focused Software Process Improvement (p. 116-130). Springer, Berlin, Heidelberg.
[14] Nagappan, N., Ball, T., Zeller, A. (2006, May). Mining metrics to predict component failures. In: Proceedings of the 28th international conference on Software engineering (p. 452-461). ACM.
[15] Menzies, T., Greenwald, J., Frank, A. (2007). Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering, (1) 2-13.
[16] Tang, M. H., Kao, M. H., Chen, M. H. (1999). An empirical study on object-oriented metrics. In: Software Metrics Symposium, 1999. Proceedings. Sixth International (p. 242-249). IEEE.
[17] Fenton, N. E., Neil, M. (1999). A critique of software defect prediction models. IEEE Transactions on software engineering, 25(5) 675-689.
[18] Friedman, J., Hastie, T., Tibshirani, R. (2001). The elements of statistical learning, 1(10). New York, NY, USA: Springer series in statistics.
[19] Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2) 123-140.
[20] Breiman, L. (2017). Classification and regression trees. Routledge.
[21] Rätsch, G., Onoda, T., Müller, K. R. (2001). Soft margins for AdaBoost. Machine learning, 42(3), 287-320.
[22] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
[23] Merentitis, A., Debes, C. (2015). Many hands make light work-on ensemble learning techniques for data fusion in remote sensing. IEEE Geoscience and Remote Sensing Magazine, 3(3) 86-99.


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