References
- H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng., Vol.21, No.9, pp.1263-1284, Sep., 2009. https://doi.org/10.1109/TKDE.2008.239
- D. Ryu, J.-I. Jang, and J. Baik, "A transfer cost-sensitive boosting approach for cross-project defect prediction," Softw. Qual. J., pp.1-38, 2015.
- Z. Geem, J. Kim, and G. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, Vol.76, No.2, pp.60-68, 2001. https://doi.org/10.1177/003754970107600201
- M. Jureczko and L. Madeyski, "Towards identifying software project clusters with regard to defect prediction," Proc. 6th Int. Conf. Predict. Model. Softw. Eng. - PROMISE '10, p. 1, 2010.
- T. Menzies, B. Caglayan, Z. He, E. Kocaguneli, J. Krall, F. Peters, and B. Turhan, "The PROMISE Repository of empirical software engineering data," 2012. [Online]. Available: http://openscience.us/repo/.
- T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell, "A Systematic Literature Review on Fault Prediction Performance in Software Engineering," IEEE Trans. Softw. Eng., Vol.38, No.6, pp.1276-1304, Nov., 2012. https://doi.org/10.1109/TSE.2011.103
- E. Arisholm, L. C. Briand, and E. B. Johannessen, "A systematic and comprehensive investigation of methods to build and evaluate fault prediction models," J. Syst. Softw., Vol.83, No.1, pp.2-17, Jan., 2010. https://doi.org/10.1016/j.jss.2009.06.055
- M. D'Ambros, M. Lanza, and R. Robbes, "Evaluating defect prediction approaches: A benchmark and an extensive comparison," Empir. Softw. Eng., Vol.17, No.4-5, pp.531-577, Aug., 2012. https://doi.org/10.1007/s10664-011-9173-9
- K. Dejaeger, "Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers," Softw. Eng. IEEE Trans., Vol.39, No.2, pp.237-257, 2013. https://doi.org/10.1109/TSE.2012.20
- K. O. Elish and M. O. Elish, "Predicting defect-prone software modules using support vector machines," J. Syst. Softw., Vol.81, No.5, pp.649-660, May, 2008. https://doi.org/10.1016/j.jss.2007.07.040
- Y. Singh, A. Kaur, and R. Malhotra, "Empirical validation of object-oriented metrics for predicting fault proneness models," Softw. Qual. J., Vol.18, No.1, pp.3-35, Jul., 2009. https://doi.org/10.1007/s11219-009-9079-6
- T. Zimmermann, N. Nagappan, H. Gall, E. Giger, and B. Murphy, "Cross-project defect prediction," in Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, 2009, pp.91-100.
- B. Turhan, T. Menzies, A. B. Bener, and J. Di Stefano, "On the relative value of cross-company and within-company data for defect prediction," Empir. Softw. Eng., Vol.14, No.5, pp.540-578, Jan., 2009. https://doi.org/10.1007/s10664-008-9103-7
- Z. He, F. Shu, Y. Yang, M. Li, and Q. Wang, "An investigation on the feasibility of cross-project defect prediction," Autom. Softw. Eng., Vol.19, No.2, pp.167-199, Jul., 2011. https://doi.org/10.1007/s10515-011-0090-3
- Y. Ma, G. Luo, X. Zeng, and A. Chen, "Transfer learning for cross-company software defect prediction," Inf. Softw. Technol., Vol.54, No.3, pp.248-256, Mar., 2012. https://doi.org/10.1016/j.infsof.2011.09.007
- D. Ryu, O. Choi, and J. Baik, "Value-cognitive boosting with a support vector machine for cross-project defect prediction," Empir. Softw. Eng., Vol.21, No.1, pp.43-71, Feb., 2016. https://doi.org/10.1007/s10664-014-9346-4
- D. Ryu, J. Jang, and J. Baik, "A Hybrid Instance Selection using Nearest-Neighbor for Cross-Project Defect Prediction," J. Comput. Sci. Technol., Vol.30, No.5, pp.969-980, 2015. https://doi.org/10.1007/s11390-015-1575-5
- G. Canfora, A. De Lucia, M. Di Penta, R. Oliveto, A. Panichella, and S. Panichella, "Defect prediction as a multiobjective optimization problem," Softw. Testing, Verif. Reliab., Vol.25, Issue 4, pp.426-459, 2015. https://doi.org/10.1002/stvr.1570
- D. Ryu and J. Baik, "Effective Multi-Objective Naive Bayes Learning for Cross-Project Defect Prediction," Appl. Soft Comput. J., Vol.49, pp.1062-1077, 2016. https://doi.org/10.1016/j.asoc.2016.04.009
- M. Harman, P. McMinn, J. De Souza, and S. Yoo, "Search based software engineering: Techniques, taxonomy, tutorial," Empir. Softw. Eng. Verif., pp.1-59, 2012.
- S. Merler, C. Furlanello, B. Larcher, and A. Sboner, "Tuning cost-sensitive boosting and its application to melanoma diagnosis," Mult. Classif. Syst., pp.32-42, 2001.
- S. Wang and X. Yao, "Using Class Imbalance Learning for Software Defect Prediction," IEEE Trans. Reliab., Vol.62, No.2, pp.434-443, Jun., 2013. https://doi.org/10.1109/TR.2013.2259203
- S. Wang, H. Chen, and X. Yao, "Negative correlation learning for classification ensembles," 2010 Int. Jt. Conf. Neural Networks, pp.1-8, Jul., 2010.
- D. Manjarres, I. Landa-Torres, S. Gil-Lopez, J. Del Ser, M.N. Bilbao, S. Salcedo-Sanz and Z.W. Geem, "A survey on applications of the harmony search algorithm," Eng. Appl. Artif. Intell., Vol.26, No.8, pp.1818-1831, Sep., 2013. https://doi.org/10.1016/j.engappai.2013.05.008
- N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE?: Synthetic Minority Over-sampling Technique," J. Artif. Intell. Res., Vol.16, pp.321-357, 2002.
- I. Tomek, "Two modifications of CNN," IEEE Trans. Syst. Man Cybern., pp.769-772, 1976.
- L. Chen, B. Fang, Z. Shang, and Y. Tang, "Negative samples reduction in cross-company software defects prediction," Inf. Softw. Technol., Vol.62, pp.67-77, 2015. https://doi.org/10.1016/j.infsof.2015.01.014
- W. Fan, S. Stolfo, J. Zhang, and P. Chan, "AdaCost: misclassification cost-sensitive boosting," ICML, 1999.
- Y. Sun, A. Wong, and Y. Wang, "Parameter inference of costsensitive boosting algorithms," in International Conference on Machine Learning and Data Mining, 2005, pp.21-30.
- M. Hall, E. Frank, and G. Holmes, "The WEKA data mining software: an update," ACM SIGKDD Explor. Newsl., Vol.11, No.1, pp.10-18, 2009. https://doi.org/10.1145/1656274.1656278
- Z. W. Geem, "Optimal cost design of water distribution networks using harmony search," Eng. Optim., Vol.38, pp.259-277, 2006. https://doi.org/10.1080/03052150500467430
- Z. W. Geem, "State-of-the-Art in the Structure of Harmony Search Algorithm," in Recent Advances In Harmony Search Algorithm, Springer Berlin Heidelberg, 2010, pp.1-10.
- T. Menzies, Z. Milton, B. Turhan, B. Cukic, Y. Jiang, and A. Bener, "Defect prediction from static code features: current results, limitations, new approaches," Autom. Softw. Eng., Vol.17, No.4, pp.375-407, May, 2010. https://doi.org/10.1007/s10515-010-0069-5