Pendekatan Level Data untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software
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Anantula, P. R., & Chamarthi, R. (2011). Defect Prediction and Analysis Using ODC Approach in a Web Application. (IJCSIT) International Journal of Computer Science and Information Technologies, 2(5), 2242-2245.
Attenberg, J., & Ertekin, S. (2013). Class Imbalance and Active Learning. In H. He, & Y. Ma, Imbalanced Learning: Foundations, Algorithms, and Applications (pp. 101-149). New Jersey: John Wiley & Sons.
Batuwita, R., & Palade, V. (2010). Efficient Resampling Methods for Training Support Vector Machines with Imbalanced Datasets. Proceedings of the International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Barcelona: IEEE Computer Society. doi:10.1109/IJCNN.2010.5596787
Bramer, M. (2007). Principles of Data Mining. London: Springer.
Carver, R. H., & Nash, J. G. (2012). Doing Data Analysis with SPSS® Version 18. Boston: Cengage Learning.
Catal, C. (2012). Performance Evaluation Metrics for Software Fault Prediction Studies. Acta Polytechnica Hungarica, 9(4), 193-206.
Chis, M. (2008). Evolutionary Decision Trees and Software Metrics for Module Defects Identification. World Academy of Science, Engineering and Technology, 273-277.
Corder, G. W., & Foreman, D. I. (2009). Nonparametric Statistics for Non-statisticians: A Step-by-step Approach. New Jersey: John Wiley & Sons.
Demšar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 1–30.
Dubey, R., Zhou, J., Wang, Y., Thompson, P. M., & Ye, J. (2014). Analysis of Sampling Techniques for Imbalanced Data: An n = 648 ADNI Study. NeuroImage, 220–241.
Fakhrahmad, S. M., & Sami, A. (2009). Effective Estimation of Modules' Metrics in Software Defect Prediction. Proceedings of the World Congress on Engineering (pp. 206-211). London: Newswood Limited.
Galar, M., Fernández, A., Barrenechea, E., & Herrera, F. (2013). EUSBoost: Enhancing Ensembles for Highly Imbalanced Data-sets by Evolutionary Under Sampling. Pattern Recognition, 3460–3471.
GarcÃa, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced Nonparametric Tests for Multiple Comparisons in the Design of Experiments in Computational Intelligence and Data Mining: Experimental Analysis of Power. Information Sciences, 2044–2064.
Gayatri, N., Nickolas, S., Reddy, A., & Chitra, R. (2009). Performance Analysis Of Data Mining Algorithms for Software Quality Prediction. International Conference on Advances in Recent Technologies in Communication and Computing (pp. 393-395). Kottayam: IEEE Computer Society.
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. Berlin: Springer-Verlag.
Gray, D., Bowes, D., Davey, N., Sun, Y., & Christianson, B. (2011). The Misuse of the NASA Metrics Data Program Data Sets for Automated Software Defect Prediction. Evaluation & Assessment in Software Engineering (EASE 2011), 15th Annual Conference on, (pp. 96-103). Durham.
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2011). A Systematic Literature Review on Fault Prediction Performance in Software Engineering. IEEE Transactions on Software Engineering, Accepted for publication - available online, 1-31.
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). San Francisco: Morgan Kaufmann Publishers Inc.
Huck, S. W. (2012). Reading Statistics and Research. Boston: Pearson Education.
In, H. P., Baik, J., Kim, S., Yang, Y., & Boehm, B. (2006, December). A Quality-Based Cost Estimation Model for the Product Line Life Cycle. Communications of the ACM, 49(12), 85-88. doi:10.1145/1183236.1183273
Japkowicz, N. (2013). Assessment Metrics for Imbalanced Learning. In H. He, & Y. Ma, Imbalanced Learning: Foundations, Algorithms, and Applications (pp. 187-206). New Jersey: John Wiley & Sons.
Jones, C. (2013). Software Defect Origins and Removal Methods. Namcook Analytics.
Khoshgoftaar, T. M., Gao, K., & Seliya, N. (2010). Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction. International Conference on Tools with Artificial Intelligence (pp. 137-144). IEEE Computer Society.
Lehtinen, T. O., Mäntylä, M. V., Vanhanen, J., Itkonen, J., & Lassenius, C. (2014). Perceived Causes of Software Project Failures - An Analysis of Their Relationships. Information and Software Technology, 623–643.
Lessmann, S., Baesens, B., Mues, C., & Pietsch, S. (2008). Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings. IEEE Transactions on Software Engineering, 485-496.
Liu, X.-Y., & Zhou, Z.-H. (2013). Ensemble Methods for Class Imbalance Learning. In H. He, & Y. Ma, Imbalanced Learning: Foundations, Algorithms, and Applications (pp. 61-82). New Jersey: John Wiley & Sons.
López, V., Fernández, A., & Herrera, F. (2014). On the Importance of the Validation Technique for Classification with Imbalanced Datasets: Addressing Covariate Shift when Data is Skewed. Information Sciences, 1–13. doi:10.1016/j.ins.2013.09.038
McDonald, M., Musson, R., & Smith, R. (2008). The Practical Guide to Defect Prevention. Washington: Microsoft Press.
Menzies, T., Greenwald, J., & Frank, A. (2007). Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering, 1-12.
Myrtveit, I., Stensrud, E., & Shepperd, M. (2005). Reliability and Validity in Comparative Studies of Software Prediction Models. IEEE Transactions on Software Engineering, 380-391.
Peng, Y., & Yao, J. (2010). AdaOUBoost: Adaptive Over-sampling and Under-sampling to Boost the Concept Learning in Large Scale Imbalanced Data Sets. Proceedings of the international conference on Multimedia information retrieval (pp. 111-118). Philadelphia, Pennsylvania, USA: ACM.
Riquelme, J. C., Ruiz, R., Rodriguez, D., & Moreno, J. (2008). Finding Defective Modules From Highly Unbalanced Datasets. Actas de los Talleres de las Jornadas de IngenierÃa del Software y Bases de Datos (pp. 67-74). Gijon, España: SISTEDES.
Rodriguez, D., Herraiz, I., Harrison, R., Dolado, J., & Riquelme, J. C. (2014). Preliminary Comparison of Techniques for Dealing with Imbalance in Software Defect Prediction. 18th International Conference on Evaluation and Assessment in Software Engineering (EASE 2014) (pp. 371-380). New York: ACM. doi:10.1145/2601248.2601294
Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., & Folleco, A. (2011). An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. Information Sciences, 1-25.
Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., & Napolitano, A. (2008). Building Useful Models from Imbalanced Data with Sampling and Boosting. Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference (pp. 306-311). California: AAAI Press.
Shepperd, M., Song, Q., Sun, Z., & Mair, C. (2013). Data Quality: Some Comments on the NASA Software Defect Data Sets. IEEE Transactions on Software Engineering, 1208-1215. doi:10.1109/TSE.2013.11
Shull, F., Basili, V., Boehm, B., Brown, A. W., Costa, P., Lindvall, M., . . . Zelkowitz, M. (2002). What We Have Learned About Fighting Defects. METRICS '02 Proceedings of the 8th International Symposium on Software Metrics (pp. 249-258). Washington: IEEE Computer Society.
Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 356-370.
Strangio, M. A. (2009). Recent Advances in Technologies. Vukovar: In-Teh.
Sun, Y., Mohamed, K. S., Wong, A. K., & Wang, Y. (2007). Cost-sensitive Boosting for Classification of Imbalanced Data. Pattern Recognition Society, 3358-3378.
Tao, W., & Wei-hua, L. (2010). Naïve Bayes Software Defect Prediction Model. Computational Intelligence and Software Engineering (CiSE) (pp. 1-4). Wuhan: IEEE Computer Society. doi:10.1109/CISE.2010.5677057
Turhan, B., & Bener, A. (2007). Software Defect Prediction: Heuristics for Weighted Naive Bayes. Proceedings of the 2nd International Conference on Software and Data Technologies (ICSOFT'07), (pp. 244-249).
Wahono, R. S., Suryana, N., & Ahmad, S. (2014, May). Metaheuristic Optimization based Feature Selection for Software Defect Prediction. Journal of Software, 9(5), 1324-1333. doi:10.4304/jsw.9.5.1324-1333
Wang, S., & Yao, X. (2013). Using Class Imbalance Learning for Software Defect Prediction. IEEE Transactions on Reliability, 434-443.
Weiss, C., Premraj, R., Zimmermann, T., & Zeller, A. (2007). How Long will it Take to Fix This Bug? Fourth International Workshop on Mining Software Repositories (MSR'07) (pp. 1-8). Washington: IEEE Computer Society.
Weiss, G. M. (2013). Foundations of Imbalanced Learning. In H. He, & Y. Ma, Imbalanced Learning: Foundations, Algorithms, and Applications (pp. 13-41). New Jersey: John Wiley & Sons.
Yap, B. W., Rani, K. A., Rahman, H. A., Fong, S., Khairudin, Z., & Abdullah, N. N. (2014). An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets. Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). 285, pp. 13-22. Singapore: Springer. doi:10.1007/978-981-4585-18-7_2
Zhang, D., Liu, W., Gong, X., & Jin, H. (2011). A Novel Improved SMOTE Resampling Algorithm Based on Fractal. Computational Information Systems, 2204-2211.
Zhang, H., & Wang, Z. (2011). A Normal Distribution-Based Over-Sampling Approach to Imbalanced Data Classification. Advanced Data Mining and Applications - 7th International Conference (pp. 83-96). Beijing: Springer.
Zhang, H., Jiang, L., & Su, J. (2005). Augmenting Na?ve Bayes for Ranking. ICML '05 Proceedings of the 22nd international conference on Machine learning (pp. 1020 - 1027). New York: ACM Press. doi:http://dx.doi.org/10.1145/1102351.1102480
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