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赵衔麟 等: 面向代码注释生成任务的注释质量评价研究                                                      3767


                     Findings of the Association for Computational Linguistics. ACL, 2021. 2842–2851. [doi: 10.18653/v1/2021.findings-acl.251]
                 [73]  Wei BL, Li YM, Li G, Xia X, Jin Z. Retrieve and refine: Exemplar-based neural comment generation. In: Proc. of the 35th IEEE/ACM
                     Int’l Conf. on Automated Software Engineering. Melbourne: IEEE, 2020. 349–360.
                 [74]  Li JA, Li YM, Li G, Hu X, Xia X, Jin Z. EditSum: A retrieve-and-edit framework for source code summarization. In: Proc. of the 36th
                     IEEE/ACM Int’l Conf. on Automated Software Engineering. Melbourne: IEEE, 2021. 155–166. [doi: 10.1109/ASE51524.2021.9678724]
                 [75]  Shi ES, Wang YL, Du L, Zhang HY, Han S, Zhang DM, Sun HB. CAST: Enhancing code summarization with hierarchical splitting and
                     reconstruction of abstract syntax trees. In: Proc. of the 2021 Conf. on Empirical Methods in Natural Language Processing. Punta Cana:
                     ACL, 2021. 4053–4062. [doi: 10.18653/v1/2021.emnlp-main.332]
                 [76]  Wang WH, Zhang YQ, Sui YL, Wan Y, Zhao Z, Wu J, Yu PS, Xu GD. Reinforcement-learning-guided source code summarization using
                     hierarchical attention. IEEE Trans. on Software Engineering, 2022, 48(1): 102–119. [doi: 10.1109/TSE.2020.2979701]
                 [77]  Sridhara G, Hill E, Muppaneni D, Pollock L, Vijay-Shanker K. Towards automatically generating summary comments for Java methods.
                     In: Proc. of the 25th IEEE/ACM Int’l Conf. on Automated Software Engineering. Antwerp: ACM, 2010. 43–52. [doi: 10.1145/1858996.
                     1859006]
                 [78]  Tan L, Yuan D, Krishna G, Zhou YY. /*icomment: Bugs or bad comments?*/. In: Proc. of the 21st ACM SIGOPS Symp. on Operating
                     Systems Principles. Stevenson: ACM, 2007. 145–158. [doi: 10.1145/1294261.129427]
                 [79]  Tan SH, Marinov D, Tan L, Leavens GT. @tComment: Testing Javadoc comments to detect comment-code inconsistencies. In: Proc. of
                     the 5th IEEE Int’l Conf. on Software Testing, Verification and Validation. Montreal: IEEE, 2012. 260–269. [doi: 10.1109/ICST.2012.
                     106]
                 [80]  Blasi  A,  Gorla  A.  Replicomment:  Identifying  clones  in  code  comments.  In:  Proc.  of  the  26th  Conf.  on  Program  Comprehension.
                     Gothenburg: ACM, 2018. 320–323. [doi: 10.1145/3196321.3196360]
                 [81]  Corazza A, Maggio V, Scanniello G. On the coherence between comments and implementations in source code. In: Proc. of the 41st
                     Euromicro Conf. on Software Engineering and Advanced Applications. Madeira: IEEE, 2015. 76–83. [doi: 10.1109/SEAA.2015.20]
                 [82]  Corazza A, Maggio V, Scanniello G. Coherence of comments and method implementations: A dataset and an empirical investigation.
                     Software Quality Journal, 2018, 26(2): 751–777. [doi: 10.1007/s11219-016-9347-1]
                 [83]  McBurney PW, McMillan C. An empirical study of the textual similarity between source code and source code summaries. Empirical
                     Software Engineering, 2016, 21(1): 17–42. [doi: 10.1007/s10664-014-9344-6]
                 [84]  Iammarino M, Aversano L, Bernardi ML, Cimitile M. A topic modeling approach to evaluate the comments consistency to source code.
                     In: Proc. of the 2020 Int’l Joint Conf. on Neural Networks. Glasgow: IEEE, 2020. 1–8. [doi: 10.1109/IJCNN48605.2020.9207651]
                 [85]  Rabbi F, Haque N, Kadir E, Siddik S, Kabir A. An ensemble approach to detect code comment inconsistencies using topic modeling. In:
                     Proc. of the 32nd Int’l Conf. on Software Engineering and Knowledge Engineering. KSI Research Inc., 2020. 392–395.
                 [86]  Khamis N, Witte R, Rilling J. Automatic quality assessment of source code comments: The JavadocMiner. In: Proc. of the 15th Int’l
                     Conf. on Applications of Natural Language to Information Systems. Cardiff: Springer, 2010. 68–79. [doi: 10.1007/978-3-642-13881-2_7]
                 [87]  Sun XB, Geng Q, Lo D, Duan YC, Liu XY, Li B. Code comment quality analysis and improvement recommendation: An automated
                     approach.  Int’l  Journal  of  Software  Engineering  and  Knowledge  Engineering,  2016,  26(6):  981–1000.  [doi:  10.1142/S0218194
                     016500339]
                 [88]  Scalabrino S, Linares-Vásquez M, Poshyvanyk D, Oliveto R. Improving code readability models with textual features. In: Proc. of the
                     24th IEEE Int’l Conf. on Program Comprehension. Austin: IEEE, 2016. 1–10. [doi: 10.1109/ICPC.2016.7503707]
                 [89]  Scalabrino  S,  Linares-Vásquez  M,  Oliveto  R,  Poshyvanyk  D.  A  comprehensive  model  for  code  readability.  Journal  of  Software:
                     Evolution and Process, 2018, 30(6): e1958. [doi: 10.1002/smr.1958]
                 [90]  Aman H, Amasaki S, Yokogawa T, Kawahara M. A Doc2Vec-based assessment of comments and its application to change-prone method
                     analysis. In: Proc. of the 25th Asia-Pacific Software Engineering Conf. Nara: IEEE, 2018. 643–647. [doi: 10.1109/APSEC.2018.00082]
                 [91]  Sridhara G, Pollock L, Vijay-Shanker K. Generating parameter comments and integrating with method summaries. In: Proc. of the 19th
                     IEEE Int’l Conf. on Program Comprehension. Kingston: IEEE, 2011. 71–80. [doi: 10.1109/ICPC.2011.28]
                 [92]  Pawelka T, Juergens E. Is this code written in English? A study of the natural language of comments and identifiers in practice. In: Proc.
                     of  the  2015  IEEE  Int’l  Conf.  on  Software  Maintenance  and  Evolution.  Bremen:  IEEE,  2015.  401–410.  [doi:  10.1109/ICSM.2015.
                     7332491]
                 [93]  Hata H, Treude C, Kula RG, Ishio T. 9.6 million links in source code comments: Purpose, evolution, and decay. In: Proc. of the 41st
                     IEEE/ACM Int’l Conf. on Software Engineering. Montreal: IEEE, 2019. 1211–1221. [doi: 10.1109/ICSE.2019.00123]
                 [94]  Pan XL, Liu CX, Zou YZ, Xie T, Xie B. MESIA: Understanding and leveraging supplementary nature of method-level comments for
                     automatic comment generation. In: Proc. of the 32nd IEEE/ACM Int'l Conf. on Program Comprehension. Lisbon: IEEE, 2024. 74–86.
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