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1616 软件学报 2024 年第 35 卷第 4 期
[26] Radford A, Narasimhan K, Salimans T, et al. Improving language understanding by generative pre-training. OpenAI, 2018.
[27] Lewis M, Liu Y, Goyal N, et al. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation,
and comprehension. arXiv:1910.13461, 2019.
[28] Ahmad WU, Chakraborty S, Ray B, et al. Unified pre-training for program understanding and generation. arXiv:2103.06333, 2021.
[29] Feng Z, Guo D, Tang D, et al. CodeBERT: A pre-trained model for programming and natural languages. arXiv:2002.08155, 2020.
[30] Guo D, Ren S, Lu S, et al. GraphCodeBERT: Pre-training code representations with data flow. arXiv:2009.08366, 2020.
[31] Wang X, Wang Y, Mi F, et al. SynCoBERT: Syntax-guided multi-modal contrastive pretraining for code representation. arXiv:
2108.04556, 2021.
[32] Guo D, Lu S, Duan N, et al. UniXcoder: Unified cross-modal pre-training for code representation. arXiv:2203. 03850, 2022.
[33] Husain H, Wu HH, Gazit T, et al. CodeSearchNet challenge: Evaluating the state of semantic code search . arXiv:1909.09436,
2019.
[34] Svajlenko J, Islam JF, Keivanloo I, et al. Towards a big data curated benchmark of inter-project code clones. In: Proc. of the 2014
IEEE Int’l Conf. on Software Maintenance and Evolution. IEEE, 2014. 476−480.
[35] Mou L, Li G, Zhang L, et al. Convolutional neural networks over tree structures for programming language processing. In: Proc. of
the 30th AAAI Conf. on Artificial Intelligence (AAAI-16). 2016.
[36] Lü TG, Hong RC, He J, et al. Multimodal-guided local feature selection for few-shot learning. Ruan Jian Xue Bao/Journal of
Software, 2023, 34(5): 2068−2082 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6771.htm [doi: 10.13328/j.
cnki.jos.006771]
[37] Gu X, Zhang H, Kim S. Deep code search. In: Proc. of the 40th Int’l Conf. on Software Engineering. 2018.
[38] Yang G, Chen X, Cao J, et al. Comformer: Code comment generation via transformer and fusion method-based hybrid code
representation. In: Proc. of the 8th Int’l Conf. on Dependable Systems and Their Applications (DSA). 2021.
[39] Liu B, Li RL, Feng JF. A brief introduction to deep metric learning. CAAI Trans. on in Telligent Systems, 2019, 14(6): 1064−1072
(in Chinese with English abstract).
[40] Gao T, Yao X, Chen D. SimCSE: Simple contrastive learning of sentence embeddings. arXiv:2104.08821, 2021.
[41] Bui ND, Yu Y, Jiang L. Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving
transformations. In: Proc. of the 44th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. 2021.
511−521.
[42] Chen Q, Lacomis J, Schwartz EJ, et al. VarCLR: Variable semantic representation pre-training via contrastive learning. In: Proc. of
the 44th Int’l Conf. on Software Engineering. 2022. 2327−2339.
[43] Neelakantan A, Xu T, Puri R, et al. Text and code embeddings by contrastive pre-training. arXiv:2201.10005, 2022.
[44] Jain P, Jain A, Zhang T, et al. Contrastive code representation learning. arXiv:2007.04973, 2020.
[45] Du L, Shi X, Wang Y, et al. Is a single model enough? MuCoS: A multi-model ensemble learning for semantic code search.
arXiv:2107.04773, 2021.
[46] Rabin MR, Bui ND, Wang K, et al. On the generalizability of neural program models with respect to semantic-preserving program
transformations. Information and Software Technology, 2021, 135: 106552.
[47] Wei M, Zhang LP. Research progress of code search methods. Application Research of Computers, 2021, 38(11): 3215−3221, 3230
(in Chinese with English abstract). [doi: 10.19734/j.issn.1001-3695.2021.04.0096]
[48] Cho K, Van Merriënboer B, Bahdanau D, et al. On the properties of neural machine translation: Encoder-decoder approaches.
arXiv:1409.1259, 2014.
附中文参考文献:
[3] 成思强, 刘建勋, 彭珍连, 等. 以 CodeBERT 为基础的代码分类研究. 计算机工程与应用, 2023, 59(24): 277−288. [doi:
10.3778/j.issn.1002-8331.2209-0402].
[7] 周志华, 陈世福. 神经网络集成. 计算机学报, 2002, 25(1): 1−8.
[16] 王霞, 梁瑶, 谢春丽. 深度学习在代码表征中的应用综述. 计算机工程与应用, 2021, 57(20): 53−63. [doi: 10.3778/j.issn.1002-
8331.2106-0368]
[36] 吕天根, 洪日昌, 何军, 等. 多模态引导的局部特征选择小样本学习方法. 软件学报, 2023, 34(5): 2068−2082. http://www.jos.
org.cn/1000-9825/6771.htm [doi: 10.13328/j.cnki.jos.006771]
[39] 刘冰, 李瑞麟, 封举富. 深度度量学习综述. 智能系统学报, 2019, 14(6): 1064−1072.