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                [30]    Yashu L, Zhihai W, Yueran H, Hanbing Y. A method of extracting malware features based on probabilistic topic model. Journal of
                     Computer  Research and Development, 2019,56(11):2339 (in  Chinese with English  abstract). [doi: 10.7544/issn1000-1239.2019.
                     20190393]
                [31]    Gao Y, Liu H, Fan XZ, Niu ZD. Method name recommendation based on source code depository and feature matching. Ruan Jian
                     Xue Bao/Journal of Software, 2015,26(12):3062–3074 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4817.
                     htm [doi: 10.13328/j.cnki.jos.004817]
                [32]    Broder AZ. On the resemblance and containment of documents. In: Proc. of the Compression and Complexity of SEQUENCES
                     1997 (Cat. No. 97TB100171). IEEE, 1997. 21–29. [doi: 10.1109/SEQUEN.1997.666900]
                [33]    Huang Y, Liu ZY, Chen XP, Xiong  YF, Luo XN.  Auxiliary method  for code commit comprehension based  on core-class
                     identification. Ruan Jian Xue Bao/Journal of Software, 2017,28(6):1418–1434 (in Chinese with English abstract). http://www.jos.
                     org.cn/1000-9825/5225.htm [doi: 10.13328/j.cnki.jos.005225]
                [34]    Huang Y,  Jia  N, Zhou Q, Chen XP, Xiong YF, Luo XN. Method combining  structural and  semantic features  to support code
                     commenting decision. Ruan Jian Xue Bao/Journal of Software, 2018,29(8):2226–2242 (in Chinese with English abstract). http://
                     www.jos.org.cn/1000-9825/5528.htm [doi: 10.13328/j.cnki.jos.005528]
                [35]    Lin ZQ, Zou YZ, Zhao JF, Cao YK, Xie B. Software text semantic search approach based on code structure knowledge. Ruan Jian
                     Xue Bao/Journal of Software, 2019,30(12):3714–3729 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5609.
                     htm [doi: 10.13328/j.cnki.jos.005609]
                [36]    Mou L, Li G, Zhang L, Wang T, Jin Z. Convolutional neural networks over tree structures for programming language processing. In:
                     Proc. of the 30th AAAI Conf. on Artificial Intelligence. 2016. 1287–1293. [doi: 10.5555/3015812.3016002]
                [37]    Cao Y, Zou Y, Luo Y, Xie B, Zhao J. Toward accurate link between code and software documentation. Science China Information
                     Sciences, 2018,61(5):050105. [doi: 10.1007/s11432-017-9402-3]
                [38]    Chen C, Peng X, Sun J, Xing Z, Wang X, Zhao Y, Zhang H, Zhao W. Generative API usage code recommendation with parameter
                     concretization. Science China Information Sciences, 2019,62(9):192103. [doi: 10.1007/s11432-018-9821-9]
                [39]    Tai  KS, Socher  R,  Manning  CD. Improved semantic  representations from  tree-structured long short-term  memory networks. In:
                     Proc.  of the  53rd Annual Meeting  of the Association  for Computational Linguistics and the  7th Int’l Joint Conf.  on Natural
                     Language Processing (Volume 1: Long Papers). 2015. 1556–1566. [doi: 10.3115/v1/P15-1150]
                [40]    Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. In: Proc. of the 3rd Int’l Conf.
                     on Learning Representations, ICLR 2015. 2015.
                [41]    Vinyals O, Fortunato M, Jaitly N. Pointer networks. In: Proc. of the 28th Int’l Conf. on Neural Information Processing Systems—
                     Volume 2. 2015. 2692–2700. [doi: 10.5555/2969442.2969540]
                [42]    Kingma  DP,  Ba  J. ADAM:  A  method for stochastic optimization. In: Proc. of the 3rd Int’l  Conf. on Learning Representations,
                     ICLR 2015. 2015.
                [43]    Loshchilov I, Hutter F. Decoupled weight decay regularization. In: Proc. of the Int’l Conf. on Learning Representations. 2018.
                [44]    Papineni  K, Roukos S, Ward  T,  et al. BLEU: A method  for automatic evaluation  of machine  translation.  In: Proc.  of  the  40th
                     Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2002. 311–318. [doi: 10.
                     3115/1073083.1073135]
                [45]    Denkowski M, Lavie A. Meteor universal: Language specific translation evaluation for any target language. In: Proc. of the 9th
                     Workshop on Statistical Machine Translation. 2014. 376–380. [doi: 10.3115/v1/W14-3348]
                [46]    Tu Z, Lu Z, Liu Y, Liu X, Li H. Modeling coverage for neural machine translation. In: Proc. of the 54th Annual Meeting of the
                     Association for Computational Linguistics (Volume 1: Long Papers). 2016. 76–85. [doi: 10.18653/v1/P16-1008]
                [47]    Mi H, Sankaran B, Wang Z, Ittycheriah A. Coverage embedding models for neural machine translation. In: Proc. of the 2016 Conf.
                     on Empirical Methods in Natural Language Processing. 2016. 955–960. [doi: 10.18653/v1/D16-1096]
                [48]    Gong C, He D, Tan X, Qin T, Wang L, Liu T. Y. Frage: Frequency-agnostic word representation. In: Proc. of the 32nd Int’l Conf.
                     on Neural Information Processing Systems. 2018. 1341–1352. [doi: 10.5555/3326943.3327066]
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