PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Hosseinzadeh, M., Rahmani, A. M., Vo, B., Bidaki, M., Masdari, M., & Zangakani, M. (2021). Improving security using SVM-based anomaly detection: Issues and challenges. Soft Computing, 25(4), 3195-3223. https://doi.org/10.1007/s00500-020-05373-x

• Illes-Seifert, T., & Paech, B. (2010). Exploring the relationship of a file’s history and its fault-proneness: An empirical method and its application to open source programs. Information and Software Technology, 52(5), 539-558. https://doi.org/10.1016/j.infsof.2009.11.010

• Kassie, N. B., & Singh, J. (2020). A study on software quality factors and metrics to enhance software quality assurance. International Journal of Productivity and Quality Management, 29(1), 24-44. https://doi.org/10.1504/IJPQM.2020.104547

• Kastro, Y., & Bener, A. B. (2008). A defect prediction method for software versioning. Software Quality Journal, 16(4), 543-562. https://doi.org/10.1007/s11219-008-9053-8

• Khan, L., Awad, M., & Thuraisingham, B. (2007). A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal, 16(4), 507-521. https://doi.org/10.1007/s00778-006-0002-5

• Khoshgoftaar, T. M., Seliya, N., & Sundaresh, N. (2006). An empirical study of predicting software faults with case-based reasoning. Software Quality Journal, 14(2), 85-111. https://doi.org/10.1007/s11219-006-7597-z

• Kim, S., Whitehead, E., & Zhang, Y. (2008). Classifying software changes: Clean or buggy? IEEE Transactions on Software Engineering, 34(2), 181-196. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4408585

• Kitchenham, B. A., Budgen, D., & Brereton, O. P. (2011). Using mapping studies as the basis for further research - A participant-observer case study. Information and Software Technology, 53(6), 638-651. https://doi.org/10.1016/j.infsof.2010.12.011

• Mendes-Moreira, J., Soares, C., Jorge, A. M., & de Sousa, J. F. (2012). Ensemble approaches for regression. ACM Computing Surveys, 45(1), 1-40. https://doi.org/10.1145/2379776.2379786

• Mohammed, M. N., & Sulaiman, N. (2012). Intrusion detection system based on SVM for WLAN. Procedia Technology, 1, 313-317. https://doi.org/10.1016/j.protcy.2012.02.066

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• Peters, F., Menzies, T., & Marcus, A. (2013). Better cross company defect prediction. In 2013 10th Working Conference on Mining Software Repositories (MSR) (pp. 409-418). IEEE Publishing. https://doi.org/10.1109/MSR.2013.6624057

• Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008, June 26-27). Systematic mapping studies in software engineering. In 12th International Conference on Evaluation and Assessment in Software Engineering, EASE 2008 (pp. 1-10). University of Bari, Italy. https://doi.org/10.14236/ewic/EASE2008.8

• Rathore, S. S., & Kumar, S. (2017). A study on software fault prediction techniques. Artificial Intelligence Review, 51(2), 255-327. https://doi.org/10.1007/s10462-017-9563-5

• Rosli, M. M., Teo, N. H. I., Yusop, N. S. M., & Mohammad, N. S. (2011). The design of a software fault prone application using evolutionary algorithm. In 2011 IEEE Conference on Open Systems (pp. 338-343). IEEE Publishing. https://doi.org/10.1109/ICOS.2011.6079246

• Seo, Y. S., & Bae, D. H. (2013). On the value of outlier elimination on software effort estimation research. Empirical Software Engineering, 18(4), 659-698. https://doi.org/10.1007/s10664-012-9207-y

• Shin, Y., Bell, R., Ostrand, T., & Weyuker, E. (2009). Does calling structure information improve the accuracy of fault prediction? In 2009 6th IEEE International Working Conference on Mining Software Repositories (pp. 61-70). IEEE Publishing. https://doi.org/10.1109/MSR.2009.5069481

• Shin, Y., Meneely, A., Williams, L., & Osborne, J. A. (2011). Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities. IEEE Transactions on Software Engineering, 37(6), 772-787. https://doi.org/10.1109/TSE.2010.81

• Vandecruys, O., Martens, D., Baesens, B., Mues, C., De Backer, M., & Haesen, R. (2008). Mining software repositories for comprehensible software fault prediction models. Journal of Systems and Software, 81(5), 823-839. https://doi.org/10.1016/j.jss.2007.07.034

• Weyuker, E. J., Ostrand, T. J., & Bell, R. M. (2007). Using developer information as a factor for fault prediction. In Third International Workshop on Predictor Models in Software Engineering (PROMISE’07: ICSE Workshops 2007) (pp. 8-8). IEEE Publishing. https://doi.org/10.1109/PROMISE.2007.14

• Yadav, H. B., & Yadav, D. K. (2015). A fuzzy logic based approach for phase-wise software defects prediction using software metrics. Information and Software Technology, 63, 44-57. https://doi.org/10.1016/j.infsof.2015.03.001

• Zhou, Y., & Leung, H. (2006). Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Transactions on Software Engineering, 32(10), 771-789. https://doi.org/10.1109/TSE.2006.102