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2140                                     Journal of Software  软件学报 Vol.32, No.7,  July 2021

                [80]    Haque S, LeClair A, Wu L, McMillan C. Improved automatic summarization of subroutines via attention to file context. In: Proc.
                     of the 17th Int’l Conf. on Mining Software Repositories. 2020.
                [81]    Barone AVM, Sennrich R. A parallel corpus of Python functions and documentation strings for automated code documentation and
                     code generation. In: Proc. of the 8th Int’l Joint Conf. on Natural Language Processing. 2017. 314–319.
                [82]    LeClair A, McMillan C. Recommendations for datasets for source code summarization. In: Proc. of the 2019 Conf. of the North
                     American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019. 3931–3937.
                [83]    Yao Z, Weld DS, Chen WP, Sun H. Staqc: A systematically mined question-code dataset from stack overflow. In: Proc. of the 2018
                     World Wide Web Conf. 2018. 1693–1703.
                [84]    Eberhart Z, LeClair A, McMillan C. Automatically extracting subroutine summary descriptions from unstructured comments. In:
                     Proc. of the 27th IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering. 2020. 35–46.
                [85]    Khamis N, Witte R, Rilling J. Automatic quality assessment of source code comments: The JavadocMiner. In: Proc. of the Int’l
                     Conf. on Application of Natural Language to Information Systems. 2010. 68–79.
                [86]    Steidl D,  Hummel  B, Juergens  E.  Quality  analysis of source  code  comments.  In: Proc. of the 21st Int’l  Conf. on Program
                     Comprehension. 2013. 83–92.
                [87]    Nenkova A, Passonneau RJ. Evaluating content selection in summarization: The Pyramid method. In: Proc. of the Human Language
                     Technology Conf. of the North American. 2004. 145–152.
                [88]    Papineni K, Roukos S, Ward T, Zhu WJ. BLEU: A method for automatic evaluation of machine translation. In: Proc. of the 40th
                     Annual Meeting of the Association for Computational Linguistics. 2002. 311–318.
                [89]    Banerjee S, Lavie A. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proc.
                     of  the ACL WORKSHop  on  Intrinsic and Extrinsic Evaluation  Measures for Machine Translation and/or  Summarization.  2005.
                     65–72.
                [90]    Lin CY. Rouge: A  package  for automatic  evaluation of  summaries.  In: Proc.  of  the Text Summarization  Branches Out.  2004.
                     74–81.
                [91]    Vedantam R, Lawrence Zitnick C, Parikh D. Cider: Consensus-based image description evaluation. In: Proc. of the IEEE Conf. on
                     Computer Vision and Pattern Recognition. 2015. 4566–4575.
                [92]    Kamimura M, Murphy GC. Towards generating human-oriented summaries of unit test cases. In: Proc. of the 21st Int’l Conf. on
                     Program Comprehension. 2013. 215–218.
                [93]    Li B, Vendome C, Linares-Vásquez M, Poshyvanyk D, Kraft NA. Automatically documenting unit test cases. In: Proc. of the 2016
                     IEEE Int’l Conf. on Software Testing, Verification and Validation. 2016. 341–352.
                [94]    Moreno L, Bavota G, Di Penta M, Oliveto R, Marcus A, Canfora G. Automatic generation of release notes. In: Proc. of the 22nd
                     ACM SIGSOFT Int’l Symp. on Foundations of Software Engineering. 2014. 484–495.
                [95]    Gao C, Zeng J, Xia X, Lo D, Lyu MR, King I. Automating app review response generation. In: Proc. of the 34th IEEE/ACM Int’l
                     Conf. on Automated Software Engineering. 2019. 163–175.
                [96]    Allamanis M,  Barr ET,  Bird  C, Sutton C. Suggesting  accurate  method  and class names. In: Proc. of  the 10th Joint Meeting on
                     Foundations of Software Engineering. 2015. 38–49.
                [97]    Jiang  L,  Liu  H, Jiang H. Machine learning based recommendation of  method names:  How far  are  we. In: Proc. of the 34th
                     IEEE/ACM Int’l Conf. on Automated Software Engineering. 2019. 602–614.
                [98]    Oda Y, Fudaba H, Neubig G, Hata H, Sakti S, Toda T, Nakamura S. Learning to generate pseudo-code from source code using
                     statistical machine translation. In: Proc. of the 30th IEEE/ACM Int’l Conf. on Automated Software Engineering. 2015. 574–584.
                [99]    Movshovitz-Attias D, Cohen W. Natural language models  for  predicting programming comments.  In:  Proc.  of  the  51st  Annual
                     Meeting of the Association for Computational Linguistics. 2013. 35–40.
                [100]    Ciurumelea A, Proksch S, Gall HC. Suggesting comment completions for Python using neural language models. In: Proc. of the
                     27th IEEE Int’l Conf. on Software Analysis, Evolution and Reengineering. 2020. 456–467.
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