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[23] Herbold S, Trautsch A, Grabowski J. A comparative study to benchmark cross-project defect prediction approaches. IEEE Trans. on
Software Engineering, 2018, 44(9): 811–823. [doi: 10.1109/TSE.2017.2724538]
[24] Kanwar S, Awasthi LK, Shrivastava V. Candidate project selection in cross project defect prediction using hybrid method. Expert
Systems with Applications, 2023, 218: 119625. [doi: 10.1016/j.eswa.2023.119625]
[25] Li K, Xiang ZL, Chen T, Tan KC. BiLO-CPDP: Bi-level programming for automated model discovery in cross-project defect prediction.
In: Proc. of the 35th IEEE/ACM Int’l Conf. on Automated Software Engineering. Melbourne: IEEE, 2020. 573–584.
[26] Singh M, Chhabra JK. An efficient instance selection algorithm for fast training of support vector machine for cross-project software
defect prediction. Journal of Computer Languages, 2024, 81: 101301. [doi: 10.1016/j.cola.2024.101301]
[27] Bai JJ, Jia JD, Capretz LF. A three-stage transfer learning framework for multi-source cross-project software defect prediction.
Information and Software Technology, 2022, 150: 106985. [doi: 10.1016/j.infsof.2022.106985]
[28] Zhang F, Zheng Q, Zou Y, Hassan AE. Cross-project defect prediction using a connectivity-based unsupervised classifier. In: Proc. of the
38th IEEE/ACM Int’l Conf. on Software Engineering. Austin: IEEE, 2016. 309–320. [doi: 10.1145/2884781.2884839]
[29] Li ZQ, Niu JW, Jing XY, Yu WY, Qi C. Cross-project defect prediction via landmark selection-based kernelized discriminant subspace
alignment. IEEE Trans. on Reliability, 2021, 70(3): 996–1013. [doi: 10.1109/TR.2021.3074660]
[30] Jin C. Cross-project software defect prediction based on domain adaptation learning and optimization. Expert Systems with Applications,
2021, 171: 114637. [doi: 10.1016/j.eswa.2021.114637]
[31] Tang SQ, Huang S, Zheng CY, Liu EH, Zong C, Ding YX. A novel cross-project software defect prediction algorithm based on transfer
learning. Tsinghua Science and Technology, 2022, 27(1): 41–57. [doi: 10.26599/TST.2020.9010040]
[32] Dai HM, Xi JQ, Dai HL. Improve cross-project just-in-time defect prediction with dynamic transfer learning. Journal of Systems and
Software, 2025, 219: 112214. [doi: 10.1016/j.jss.2024.112214]
[33] Bal PR, Kumar S. Cross project defect prediction using dropout regularized deep learning and unique matched metrics. ACM Trans. on
Management Information Systems, 2025, 16(3): 20. [doi: 10.1145/3698109]
[34] Pandey SK, Tripathi AK. Cross-project setting using deep learning architectures in just-in-time software fault prediction: An
investigation. In: Proc. of the 2023 IEEE/ACM Int’l Conf. on Automation of Software Test. Melbourne: IEEE, 2023. 24–34. [doi: 10.
1109/AST58925.2023.00007]
[35] Chen JY, Hu KK, Yu Y, Chen ZZ, Xuan Q, Liu Y, Filkov V. Software visualization and deep transfer learning for effective software
defect prediction. In: Proc. of the 42nd IEEE/ACM Int’l Conf. on Software Engineering. Seoul: IEEE, 2020. 578–589.
[36] Poon WN, Bennin KE, Huang JL, Phannachitta P, Keung JW. Cross-project defect prediction using a credibility theory based naive Bayes
classifier. In: Proc. of the 2017 IEEE Int’l Conf. on Software Quality, Reliability and Security. Prague: IEEE, 2017. 434–441. [doi: 10.
1109/QRS.2017.53]
[37] Gong L, Jiang SJ, Bo LL, Jiang L, Qian JY. A novel class-imbalance learning approach for both within-project and cross-project defect
prediction. IEEE Trans. on Reliability, 2020, 69(1): 40–53. [doi: 10.1109/TR.2019.2895462]
[38] Jing XY, Wu F, Dong XW, Xu BW. An improved SDA based defect prediction framework for both within-project and cross-project class-
imbalance problems. IEEE Trans. on Software Engineering, 2017, 43(4): 321–332. [doi: 10.1109/TSE.2016.2597849]
[39] Ryu D, Baik J. Effective multi-objective naïve Bayes learning for cross-project defect prediction. Applied Soft Computing, 2016, 49:
1062–1077. [doi: 10.1016/j.asoc.2016.04.009]
[40] Xia X, Lo D, Pan SJ, Nagappan N, Wang XY. HYDRA: Massively compositional model for cross-project defect prediction. IEEE Trans.
on Software Engineering, 2016, 42(10): 977–988. [doi: 10.1109/TSE.2016.2543218]
[41] Sharma BU, Sadam R. How far does the predictive decision impact the software project? The cost, service time, and failure analysis from
a cross-project defect prediction model. Journal of Systems and Software, 2023, 195: 111522. [doi: 10.1016/j.jss.2022.111522]
[42] Tong HN, Zhang DL, Liu JQ, Xing WW, Lu LY, Lu W, Wu YM. MASTER: Multi-source transfer weighted ensemble learning for
multiple sources cross-project defect prediction. IEEE Trans. on Software Engineering, 2024, 50(5): 1281–1305. [doi: 10.1109/TSE.2024.
3381235]
[43] Xia D, Liu XY, Yao YM, Yang H, Wang AL, Wu HB. Heterogeneous defect prediction based on robust federated learning. In: Proc. of
the 2025 Int’l Conf. on Measurement, Communication, and Virtual Reality, Vol. 13634. Harbin: SPIE, 2025. 136340B. [doi: 10.1117/12.
3066036]
[44] Wang AL, Feng YX, Yang MJ, Wu HB, Iwahori Y, Chen HS. Cross-project software defect prediction using differential perception
combined with inheritance federated learning. Electronics, 2024, 13(24): 4893. [doi: 10.3390/electronics13244893]
[45] Yang YM, Hu X, Gao ZP, Chen JF, Ni C, Xia X, Lo D. Federated learning for software engineering: A case study of code clone detection
and defect prediction. IEEE Trans. on Software Engineering, 2024, 50(2): 296–321. [doi: 10.1109/TSE.2023.3347898]
[46] Liu Y, Li Y, Wen M, Zhang WJ. Privacy protection optimization for federated software defect prediction via benchmark analysis. Journal