[1] X. Chen, Y. Zhao, Q. Wang, and Z. Yuan, "MULTI: Multi-objective effort-aware just-in-time software defect prediction," Information and Software Technology, vol. 93, pp. 1-13, 2018, doi: 10.1016/j.infsof.2017.08.004.
[2] G. G. Cabral, L. L. Minku, E. Shihab, and S. Mujahid, "Class imbalance evolution and verification latency in just-in-time software defect prediction," in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), 2019: IEEE, pp. 666-676, doi: 10.1109/ICSE.2019.00076.
[3] A. K. Gangwar, S. Kumar, and A. Mishra, "A Paired Learner-Based Approach for Concept Drift Detection and Adaptation in Software Defect Prediction," Applied Sciences, vol. 11, no. 14, p. 6663, 2021, doi: 10.3390/app11146663.
[4] F. Bayram, B. S. Ahmed, and A. Kassler, "From concept drift to model degradation: An overview on performance-aware drift detectors," Knowledge-Based Systems, vol. 245, p. 108632, 2022, doi: 10.1016/j.knosys.2022.108632.
[5] F. Hinder, V. Vaquet, and B. Hammer, "One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift," Frontiers in Artificial Intelligence, vol. 7, p. 1330257, 2024, doi: 10.3389/frai.2024.1330257.
[6] S. Agrahari and A. K. Singh, "Concept drift detection in data stream mining: A literature review," Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 10, pp. 9523-9540, 2022, doi: 10.1016/j.jksuci.2021.11.006.
[7] M. Jain, G. Kaur, and V. Saxena, "A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection," Expert Systems with Applications, vol. 193, p. 116510, 2022, doi: 10.1016/j.eswa.2022.116510.
[8] S. McIntosh and Y. Kamei, "Are fix-inducing changes a moving target? a longitudinal case study of just-in-time defect prediction," in Proceedings of the 40th International Conference on Software Engineering, 2018, pp. 560-560, doi: 10.1145/3180155.3182514.
[9] D. Lin, C. Tantithamthavorn, and A. E. Hassan, "The impact of data merging on the interpretation of cross-project just-in-time defect models," IEEE Transactions on Software Engineering, vol. 48, no. 8, pp. 2969-2986, 2021, doi: 10.1109/TSE.2021.3073920.
[10] G. K. Rajbahadur, S. Wang, G. A. Oliva, Y. Kamei, and A. E. Hassan, "The impact of feature importance methods on the interpretation of defect classifiers," IEEE Transactions on Software Engineering, vol. 48, no. 7, pp. 2245-2261, 2021, doi: 10.1109/TSE.2021.3056941.
[11] W. Zheng, T. Shen, X. Chen, and P. Deng, "Interpretability application of the Just-in-Time software defect prediction model," Journal of Systems and Software, vol. 188, p. 111245, 2022, doi: 10.1016/j.jss.2022.111245.
[12] K. Fathi, M. Sadurski, T. Kleinert, and H. W. van de Venn, "Source component shift detection & classification for improved remaining useful life estimation in alarm-based predictive maintenance," in 2023 23rd international conference on control, automation and systems (iccas), 2023: IEEE, pp. 975-980, doi: 10.23919/ICCAS59377.2023.10316874.
[13] B. Turhan, "On the dataset shift problem in software engineering prediction models," Empirical Software Engineering, vol. 17, pp. 62-74, 2012, doi: 10.1007/s10664-011-9182-8.
[14] J. Lu, A. Liu, F. Dong, F. Gu, J. Gama, and G. Zhang, "Learning under concept drift: A review," IEEE transactions on knowledge and data engineering, vol. 31, no. 12, pp. 2346-2363, 2018, doi: 10.1109/TKDE.2018.2876857.
[15] F. Dong, J. Lu, K. Li, and G. Zhang, "Concept drift region identification via competence-based discrepancy distribution estimation," in 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017: IEEE, pp. 1-7, doi: 10.1109/ISKE.2017.8258734.
[16] R. F. Haase and M. V. Ellis, "Multivariate analysis of variance," Journal of Counseling Psychology, vol. 34, no. 4, p. 404, 1987.
[17] J. Gama, Knowledge discovery from data streams. CRC Press, 2010.
[18] O. A. Mahdi, E. Pardede, N. Ali, and J. Cao, "Fast reaction to sudden concept drift in the absence of class labels," Applied Sciences, vol. 10, no. 2, p. 606, 2020, doi: 10.3390/app10020606.
[19] J. Jiarpakdee, C. K. Tantithamthavorn, H. K. Dam, and J. Grundy, "An empirical study of model-agnostic techniques for defect prediction models," IEEE Transactions on Software Engineering, vol. 48, no. 1, pp. 166-185, 2020, doi: 10.1109/TSE.2020.2982385.
[20] E. Štrumbelj, I. Kononenko, and M. R. Šikonja, "Explaining instance classifications with interactions of subsets of feature values," Data & Knowledge Engineering, vol. 68, no. 10, pp. 886-904, 2009, doi: 10.1016/j.datak.2009.01.004.
[21] M. T. Ribeiro, S. Singh, and C. Guestrin, "" Why should i trust you?" Explaining the predictions of any classifier," in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016, pp. 1135-1144, doi: 10.1145/2939672.2939778.
[22] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," Advances in neural information processing systems, vol. 30, 2017.
[23] D. Vreš and M. Robnik-Šikonja, "Preventing deception with explanation methods using focused sampling," Data Mining and Knowledge Discovery, vol. 38, no. 5, pp. 3262-3307, 2024, doi: 10.1007/s10618-022-00900-w.
[24] Y. Kamei, T. Fukushima, S. McIntosh, K. Yamashita, N. Ubayashi, and A. E. Hassan, "Studying just-in-time defect prediction using cross-project models," Empirical Software Engineering, vol. 21, pp. 2072-2106, 2016, doi: 10.1007/s10664-015-9400-x.
[25] W. Li, W. Zhang, X. Jia, and Z. Huang, "Effort-aware semi-supervised just-in-time defect prediction," Information and Software Technology, vol. 126, p. 106364, 2020, doi: 10.1016/j.infsof.2020.106364.
[26] G. J. Ross, N. M. Adams, D. K. Tasoulis, and D. J. Hand, "Exponentially weighted moving average charts for detecting concept drift," Pattern recognition letters, vol. 33, no. 2, pp. 191-198, 2012.
[27] C. Tantithamthavorn, A. E. Hassan, and K. Matsumoto, "The impact of class rebalancing techniques on the performance and interpretation of defect prediction models," IEEE Transactions on Software Engineering, vol. 46, no. 11, pp. 1200-1219, 2020, doi: 10.1109/TSE.2018.2876537.
[28] L. Pelayo and S. Dick, "Applying novel resampling strategies to software defect prediction," in NAFIPS 2007-2007 Annual meeting of the North American fuzzy information processing society, 2007: IEEE, pp. 69-72, doi: 10.1109/NAFIPS.2007.383813.
[29] L. Chen, B. Fang, Z. Shang, and Y. Tang, "Tackling class overlap and imbalance problems in software defect prediction," Software Quality Journal, vol. 26, pp. 97-125, 2018, doi: 10.1007/s11219-016-9342-6.
[30] O. I. Sheluhin and S. A. Sekretarev, "Concept drift detection in streaming classification of mobile application traffic," Automatic Control and Computer Sciences, vol. 55, pp. 253-262, 2021, doi: 10.1016/j.procs.2017.11.440.
[31] S. Tabassum, L. L. Minku, and D. Feng, "Cross-project online just-in-time software defect prediction," IEEE Transactions on Software Engineering, vol. 49, no. 1, pp. 268-287, 2022, doi: 10.1109/TSE.2022.3150153.
[32] Y. Gao, Y. Zhu, and Y. Zhao, "Dealing with imbalanced data for interpretable defect prediction," Information and software technology, vol. 151, p. 107016, 2022, doi: 10.1016/j.infsof.2022.107016.
[33] A. Mockus and D. M. Weiss, "Predicting risk of software changes," Bell Labs Technical Journal, vol. 5, no. 2, pp. 169-180, 2000, doi: 10.1002/bltj.2229.
[34] A. E. Hassan, "Predicting faults using the complexity of code changes," in 2009 IEEE 31st international conference on software engineering, 2009: IEEE, pp. 78-88, doi: 10.1109/ICSE.2009.5070510.
[35] R. Purushothaman and D. E. Perry, "Toward understanding the rhetoric of small source code changes," IEEE Transactions on Software Engineering, vol. 31, no. 6, pp. 511-526, 2005, doi: 10.1109/TSE.2005.74.
[36] P. J. Guo, T. Zimmermann, N. Nagappan, and B. Murphy, "Characterizing and predicting which bugs get fixed: an empirical study of microsoft windows," in Proceedings of the 32Nd ACM/IEEE International Conference on Software Engineering-Volume 1, 2010, pp. 495-504, doi: 10.1145/1806799.1806871.
[37] L. Torgo and M. L. Torgo, "Package ‘dmwr’," Comprehensive R archive network, 2013.
[38] A. L. Suárez-Cetrulo, D. Quintana, and A. Cervantes, "A survey on machine learning for recurring concept drifting data streams," Expert Systems with Applications, vol. 213, p. 118934, 2023, 10.1016/j.eswa.2022.118934.
[39] A. Bifet, J. Read, B. Pfahringer, G. Holmes, and I. Žliobaitė, "CD-MOA: Change detection framework for massive online analysis," in International Symposium on Intelligent Data Analysis, 2013: Springer, pp. 92-103, doi: 10.1007/978-3-642-41398-8_9.
[40] J. Demšar and Z. Bosnić, "Detecting concept drift in data streams using model explanation," Expert Systems with Applications, vol. 92, pp. 546-559, 2018, doi: 10.1016/j.eswa.2017.10.003.
[41] W. J. Conover and R. L. Iman, "Rank transformations as a bridge between parametric and nonparametric statistics," The American Statistician, vol. 35, no. 3, pp. 124-129, 1981.
[42] R. C. Blair and J. J. Higgins, "The power of t and Wilcoxon statistics: A comparison," Evaluation Review, vol. 4, no. 5, pp. 645-656, 1980, doi: 10.1177/0193841X8000400506.
