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P. 342
赵衔麟 等: 面向代码注释生成任务的注释质量评价研究 3765
[27] Stapleton S, Gambhir Y, LeClair A, Eberhart Z, Weimer W, Leach K, Huang Y. A human study of comprehension and code
summarization. In: Proc. of the 28th Int’l Conf. on Program Comprehension. Seoul: ACM, 2020. 2–13. [doi: 10.1145/3387904.3389258]
[28] Roy D, Fakhoury S, Arnaoudova V. Reassessing automatic evaluation metrics for code summarization tasks. In: Proc. of the 29th ACM
Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Athens: ACM, 2021.
1105–1116. [doi: 10.1145/3468264.3468588]
[29] Haque S, Eberhart Z, Bansal A, McMillan C. Semantic similarity metrics for evaluating source code summarization. In: Proc. of the 30th
IEEE/ACM Int’l Conf. on Program Comprehension. ACM, 2022. 36–47. [doi: 10.1145/3524610.3527909]
[30] Shi L, Mu FW, Chen X, Wang S, Wang JJ, Yang Y, Li G, Xia X, Wang Q. Are we building on the rock? On the importance of data
preprocessing for code summarization. In: Proc. of the 30th ACM Joint European Software Engineering Conf. and Symp. on the
Foundations of Software Engineering. Singapore: ACM, 2022. 107–119. [doi: 10.1145/3540250.3549145]
[31] Wang C, He H, Pal U, Marinov D, Zhou MH. Suboptimal comments in Java projects: From independent comment changes to
commenting practices. ACM Trans. on Software Engineering and Methodology, 2023, 32(2): 45. [doi: 10.1145/3546949]
[32] Mahmud J, Faisal F, Arnob RI, Anastasopoulos A, Moran K. Code to comment translation: A comparative study on model effectiveness
& errors. In: Proc. of the 1st Workshop on Natural Language Processing for Programming. ACL, 2021. 1–16. [doi: 10.18653/v1/2021.
nlp4prog-1.1]
[33] Song XT, Sun HL. Survey on neural network-based automatic source code summarization technologies. Ruan Jian Xue Bao/Journal of
Software, 2022, 33(1): 55–77 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6337.htm [doi: 10.13328/j.cnki.jos.
006337]
[34] Rani P, Blasi A, Stulova N, Panichella S, Gorla A, Nierstrasz O. A decade of code comment quality assessment: A systematic literature
review. Journal of Systems and Software, 2023, 195: 111515. [doi: 10.1016/j.jss.2022.111515]
[35] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization
Administration of the People’s Republic of China. GB/T 19000-2016 Quality management systems —Fundamentals and vocabulary.
Beijing: Standards Press of China, 2016 (in Chinese).
[36] Badihi S, Heydarnoori A. Generating code summaries using the power of the crowd. arXiv:1612.03618, 2016.
[37] Shi ES, Wang YL, Du L, Chen JJ, Han S, Zhang HY, Zhang DM, Sun HB. On the evaluation of neural code summarization. In: Proc. of
the 44th Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 1597–1608. [doi: 10.1145/3510003.3510060]
[38] Sun WS, Fang CR, You YD, Miao Y, Liu Y, Li YK, Deng GL, Huang SH, Chen YC, Zhang QJ, Qian HW, Liu Y, Chen ZY. Automatic
code summarization via ChatGPT: How far are we? arXiv:2305.12865, 2023.
[39] Nie PY, Zhang JY, Li JJ, Mooney R, Gligoric M. Impact of evaluation methodologies on code summarization. In: Proc. of the 60th
Annual Meeting of the Association for Computational Linguistics. Dublin: ACL, 2022. 4936–4960. [doi: 10.18653/v1/2022.acl-long.339]
[40] Hu X, Li G, Xia X, Lo D, Lu S, Jin Z. Summarizing source code with transferred API knowledge. In: Proc. of the 27th Int’l Joint Conf.
on Artificial Intelligence. Stockholm: AAAI Press, 2018. 2269–2275.
[41] LeClair A, Jiang SY, McMillan C. A neural model for generating natural language summaries of program subroutines. In: Proc. of the
41st Int’l Conf. on Software Engineering. Montreal: IEEE, 2019. 795–806. [doi: 10.1109/ICSE.2019.00087]
[42] Husain H, Wu HH, Gazit T, Allamanis M, Brockschmidt M. CodeSearchNet challenge: Evaluating the state of semantic code search.
arXiv:1909.09436, 2019.
[43] Wang Y, Wang WS, Joty S, Hoi SCH. CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and
generation. In: Proc. of the 2021 Conf. on Empirical Methods in Natural Language Processing. Punta Cana: ACL, 2021. 8696–8708. [doi:
10.18653/v1/2021.emnlp-main.685]
[44] Liu SQ, Chen Y, Xie XF, Siow JK, Liu Y. Retrieval-augmented generation for code summarization via hybrid GNN. In: Proc. of the 9th
Int’l Conf. on Learning Representations. OpenReview.net, 2021.
[45] Wong E, Liu TY, Tan L. CloCom: Mining existing source code for automatic comment generation. In: Proc. of the 22nd IEEE Int’l Conf.
on Software Analysis, Evolution, and Reengineering. Montreal: IEEE, 2015. 380–389. [doi: 10.1109/SANER.2015.7081848]
[46] Zhou Y, Zhang XQ, Shen JJ, Han TT, Chen TL, Gall H. Adversarial robustness of deep code comment generation. ACM Trans. on
Software Engineering and Methodology, 2022, 31(4): 60. [doi: 10.1145/3501256]
[47] Li Z, Wu YH, Peng B, Chen X, Sun ZY, Liu Y, Yu DL. SeCNN: A semantic CNN parser for code comment generation. Journal of
Systems and Software, 2021, 181: 111036. [doi: 10.1016/j.jss.2021.111036]
[48] Mu FW, Chen X, Shi L, Wang S, Wang Q. Developer-intent driven code comment generation. In: Proc. of the 45th Int’l Conf. on
Software Engineering. Melbourne: IEEE, 2023. 768–780. [doi: 10.1109/ICSE48619.2023.00073]
[49] Wang ZN, Yu XH, Feng YS, Zhao DY. An intra-class relation guided approach for code comment generation. In: Proc. of the 2023

