Page 143 - 《软件学报》2025年第9期

4054 软件学报 2025 年第 36 卷第 9 期

Proc. of the 42nd Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Paris: ACM, 2019. 125–134. [doi: 10.
1145/3331184.3331188]
[36] Cheng ZY, Ding Y, Zhu L, Kankanhalli M. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In: Proc. of the
2018 World Wide Web Conf. Lyon: Int’l World Wide Web Conf. Steering Committee, 2018. 639–648. [doi: 10.1145/3178876.3186145]
[37] Wang X, Wang DX, Xu CR, He XN, Cao YX, Chua TS. Explainable reasoning over knowledge graphs for recommendation. In: Proc. of
the 33rd AAAI Conf. on Artificial Intelligence. Honolulu: AAAI, 2019. 5329–5336. [doi: 10.1609/aaai.v33i01.33015329]
[38] Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proc. of the
25th Conf. on Uncertainty in Artificial Intelligence. Montreal: AUAI Press, 2009. 452–461.
[39] Ma H, King I, Lyu MR. Effective missing data prediction for collaborative filtering. In: Proc. of the 30th Annual Int’l ACM SIGIR Conf.
on Research and Development in Information Retrieval. Amsterdam: ACM, 2007. 39–46. [doi: 10.1145/1277741.1277751]
[40] Wu JC, Wang X, Feng FL, He XN, Chen L, Lian JX, Xie X. Self-supervised graph learning for recommendation. In: Proc. of the 44th Int’l
ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2021. 726–735. [doi: 10.1145/3404835.3462862]
[41] Zhao XY, Xia L, Zou LX, Liu H, Yin DW, Tang JL. UserSim: User simulation via supervised generativeadversarial network. In: Proc. of
the 2021 Web Conf. Ljubljana: ACM, 2021. 3582–3589. [doi: 10.1145/3442381.3450125]
[42] Dai J, Li QS, Xie SL, Li DZ, Chu H. PCG: A joint framework of graph collaborative filtering for bug triaging. Journal of Software:
Evolution and Process, 2024, 36(9): e2673. [doi: 10.1002/smr.2673]
[43] Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM,
1992, 35(12): 61–70. [doi: 10.1145/138859.138867]
[44] Zhu ZY, Li Y, Wang Y, Wang YJ, Tong HH. A deep multimodal model for bug localization. Data Mining and Knowledge Discovery,
2021, 35(4): 1369–1392. [doi: 10.1007/s10618-021-00755-7]
[45] Wan Y, Shu JD, Sui YL, Xu GD, Zhao Z, Wu J, Yu P. Multi-modal attention network learning for semantic source code retrieval. In:
Proc. of the 34th IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). San Diego: IEEE, 2019. 13–25. [doi: 10.1109/ASE.
2019.00012]
[46] Ye X, Shen H, Ma X, Bunescu R, Liu C. From word embeddings to document similarities for improved information retrieval in software
engineering. In: Proc. of the 38th Int’l Conf. on Software Engineering. Austin: ACM, 2016. 404–415. [doi: 10.1145/2884781.2884862]
[47] Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv:1301.3781, 2013.
[48] Zheng W, Chen JZ, Wu XX, Chen X, Xia X. Empirical studies on deep-learning-based security bug report prediction methods. Ruan Jian
Xue Bao/Journal of Software, 2020, 31(5): 1294–1313 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5954.htm
[doi: 10.13328/j.cnki.jos.005954]
[49] Guo SK, Zhang XY, Yang X, Chen R, Guo C, Li H, Li TT. Developer activity motivated bug triaging: Via convolutional neural network.
Neural Processing Letters, 2020, 51(3): 2589–2606. [doi: 10.1007/s11063-020-10213-y]
[50] Bock HH. Clustering methods: A history of K-means algorithms. In: Brito P, Cucumel G, Bertrand P, Carvalho F, eds. Selected
Contributions in Data Analysis and Classification. Berlin: Springer, 2007. 161–172. [doi: 10.1007/978-3-540-73560-1_15]
[51] Syakur MA, Khotimah BK, Rochman EMS, Satoto BD. Integration K-means clustering method and elbow method for identification of
the best customer profile cluster. IOP Conf. Series: Materials Science and Engineering, 2018, 336: 012017. [doi: 10.1088/1757-899X/336/
1/012017]
[52] Dai J. Research on self-supervised learning for collaborative triaging of software bugs [Ph.D. Thesis]. Xi’an: Xidian University, 2023 (in
Chinese with English abstract). [doi: 10.27389/d.cnki.gxadu.2023.000028]
[53] Ma QL, Zheng ZJ, Zheng JW, Li S, Zhuang WQ, Cottrell GW. Joint-label learning by dual augmentation for time series classification. In:
Proc. of the 35th AAAI Conf. on Artificial Intelligence. AAAI, 2021. 8847–8855. [doi: 10.1609/aaai.v35i10.17071]
[54] Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv:1412.6980, 2017.
[55] Google. Issues—Chromium. 2023. https://bugs.chromium.org/p/chromium/issues/list
[56] Mozilla. Components for core. 2023. https://bugzilla.mozilla.org/describecomponents.cgi?product=Core
[57] Mozilla. Components for Firefox. 2023. https://bugzilla.mozilla.org/describecomponents.cgi?product=Firefox

附中文参考文献:
[1] 张天伦, 陈荣, 杨溪, 祝宏玉. 基于代价极速学习机的软件缺陷报告分类方法. 软件学报, 2019, 30(5): 1386–1406. http://www.jos.org.
cn/1000-9825/5725.htm [doi: 10.13328/j.cnki.jos.005725]
[10] 何吉元, 孟昭鹏, 陈翔, 王赞, 樊向宇. 一种半监督集成跨项目软件缺陷预测方法. 软件学报, 2017, 28(6): 1455–1473. http://www.jos.