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刘子扬 等: 基于个性化联邦学习的跨项目软件缺陷预测方法 2933
建一个多层次、智能化的缺陷预测生态系统, 具有重要的研究价值与应用前景.
References
[1] Hall T, Beecham S, Bowes D, Gray D, Counsell S. A systematic literature review on fault prediction performance in software
engineering. IEEE Trans. on Software Engineering, 2012, 38(6): 1276–1304. [doi: 10.1109/TSE.2011.103]
[2] Ostrand TJ, Weyuker EJ, Bell RM. Predicting the location and number of faults in large software systems. IEEE Trans. on Software
Engineering, 2005, 31(4): 340–355. [doi: 10.1109/TSE.2005.49]
[3] Nam J, Pan SJ, Kim S. Transfer defect learning. In: Proc. of the 35th Int’l Conf. on Software Engineering. San Francisco: IEEE, 2013.
382–391. [doi: 10.1109/ICSE.2013.6606584]
[4] Wang S, Liu TY, Tan L. Automatically learning semantic features for defect prediction. In: Proc. of the 38th IEEE/ACM Int’l Conf. on
Software Engineering. Austin: IEEE, 2016. 297–308. [doi: 10.1145/2884781.2884804]
[5] Li J, He PJ, Zhu JM, Lyu MR. Software defect prediction via convolutional neural network. In: Proc. of the 2017 IEEE Int’l Conf. on
Software Quality, Reliability and Security. Prague: IEEE, 2017. 318–328. [doi: 10.1109/QRS.2017.42]
[6] He ZM, Shu FD, Yang Y, Li MS, Wang Q. An investigation on the feasibility of cross-project defect prediction. Automated Software
Engineering, 2012, 19(2): 167–199. [doi: 10.1007/s10515-011-0090-3]
[7] Turhan B, Mısırlı AT, Bener A. Empirical evaluation of the effects of mixed project data on learning defect predictors. Information and
Software Technology, 2013, 55(6): 1101–1118. [doi: 10.1016/j.infsof.2012.10.003]
[8] McMahan B, Moore E, Ramage D, Hampson S, Agüera y Arcas B. Communication-efficient learning of deep networks from
decentralized data. In: Proc. of the 20th Int’l Conf. on Artificial Intelligence and Statistics. 2017. 1273–1282.
[9] Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated optimization in heterogeneous networks. In: Proc. of the 3rd
Conf. on Machine Learning and Systems. 2020. 429–450.
[10] Yang Q, Liu Y, Chen TJ, Tong YX. Federated machine learning: Concept and applications. ACM Trans. on Intelligent Systems and
Technology (TIST), 2019, 10(2): 12.
[11] Lin Y, Sun J, Xue YX, Liu Y, Dong JS. Feedback-based debugging. In: Proc. of the 39th IEEE/ACM Int’l Conf. on Software
Engineering. Buenos Aires: IEEE, 2017. 393–403. [doi: 10.1109/ICSE.2017.43]
[12] Dinh CT, Tran NH, Nguyen TD. Personalized federated learning with Moreau envelopes. In: Proc. of the 34th Int’l Conf. on Neural
Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 1796.
[13] Wang JY, Liu QH, Liang H, Joshi G, Vincent Poor H. Tackling the objective inconsistency problem in heterogeneous federated
optimization. In: Proc. of the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 638.
[14] Dwork C. Differential privacy. In: Proc. of the 33rd Int’l Colloquium on Automata, Languages, and Programming. Venice: Springer,
2006. 1–12. [doi: 10.1007/11787006_1]
[15] Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L. Deep learning with differential privacy. In: Proc. of the
2016 ACM SIGSAC Conf. on Computer and Communications Security. Vienna: ACM, 2016. 308–318. [doi: 10.1145/2976749.2978318]
[16] Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQS, Poor HV. Federated learning with differential privacy: Algorithms
and performance analysis. IEEE Trans. on Information Forensics and Security, 2020, 15: 3454–3469. [doi: 10.1109/TIFS.2020.2988575]
[17] Zheng W, Liu CY, Wu XX, Chen X, Cheng JY, Sun XB, Sun RY. Cross-project prediction method of security bug reports based on
knowledge graph. Ruan Jian Xue Bao/Journal of Software, 2024, 35(3): 1257–1279 (in Chinese with English abstract). http://www.jos.
org.cn/1000-9825/6812.htm [doi: 10.13328/j.cnki.jos.006812]
[18] Li SY, Ji YD, Shi DY, Liao WD, Zhang LP, Tong YX, Xu K. Data federation system for multi-party security. Ruan Jian Xue Bao/Journal
of Software, 2022, 33(3): 1111–1127 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6458.htm [doi: 10.13328/j.cnki.
jos.006458]
[19] Bird C, Nagappan N, Murphy B, Gall H, Devanbu P. Don’t touch my code! Examining the effects of ownership on software quality. In:
Proc. of the 19th ACM SIGSOFT Symp. and the 13th European Conf. on Foundations of Software Engineering. Szeged: ACM, 2011.
4–14. [doi: 10.1145/2025113.2025119]
[20] Pasquale F. The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge: Harvard University Press,
2015. 1–320.
[21] Chen JX, Ding JL, Tan KC, Qian JC, Li K. MBL-CPDP: A multi-objective bilevel method for cross-project defect prediction. IEEE
Trans. on Software Engineering, 2025, 51(8): 2305–2328. [doi: 10.1109/TSE.2025.3577808]
[22] Herbold S. Training data selection for cross-project defect prediction. In: Proc. of the 9th Int’l Conf. on Predictive Models in Software
Engineering. Baltimore: ACM, 2013. 6. [doi: 10.1145/2499393.2499395]

