Page 341 - 《软件学报》2025年第8期
P. 341

3764                                                       软件学报  2025  年第  36  卷第  8  期


                     [doi: 10.1145/1085313.1085331]
                  [4]  Tan L, Yuan D, Zhou YY. Hotcomments: How to make program comments more useful? In: Proc. of the 11th USENIX Workshop on
                     Hot Topics in Operating Systems. San Diego: USENIX Association, 2007. 19.
                  [5]  Xia X, Bao LF, Lo D, Xing ZC, Hassan AE, Li SP. Measuring program comprehension: A large-scale field study with professionals.
                     IEEE Trans. on Software Engineering, 2018, 44(10): 951–976. [doi: 10.1109/TSE.2017.2734091]
                  [6]  Haiduc S, Aponte J, Moreno L, Marcus A. On the use of automated text summarization techniques for summarizing source code. In: Proc.
                     of the 17th Working Conf. on Reverse Engineering. Beverly: IEEE, 2010. 35–44. [doi: 10.1109/WCRE.2010.13]
                  [7]  Haiduc S, Aponte J, Marcus A. Supporting program comprehension with source code summarization. In: Proc. of the 32nd ACM/IEEE
                     Int’l Conf. on Software Engineering. Cape Town: IEEE, 2010. 223–226. [doi: 10.1145/1810295.1810335]
                  [8]  Bai  Y,  Zhang  LP,  Zhao  FR.  A  survey  on  research  of  code  comment.  In:  Proc.  of  the  3rd  Int’l  Conf.  on  Management  Engineering,
                     Software Engineering and Service Sciences. Wuhan: ACM, 2019. 45–51. [doi: 10.1145/3312662.3312710]
                  [9]  Song XT, Sun HL, Wang X, Yan JF. A survey of automatic generation of source code comments: Algorithms and techniques. IEEE
                     Access, 2019, 7: 111411–111428. [doi: 10.1109/ACCESS.2019.2931579]
                 [10]  Zhao  FR,  Zhao  JQ,  Bai  Y.  A  survey  of  automatic  generation  of  code  comments.  In:  Proc.  of  the  4th  Int’l  Conf.  on  Management
                     Engineering, Software Engineering and Service Sciences. Wuhan: ACM, 2020. 21–25. [doi: 10.1145/3380625.3380649]
                 [11]  Chen X, Yang G, Cui ZQ, Meng GZ, Wang Z. Survey of state-of-the-art automatic code comment generation. Ruan Jian Xue Bao/Journal
                     of Software, 2021, 32(7): 2118–2141 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6258.htm [doi: 10.13328/j.cnki.
                     jos.006258]
                 [12]  Iyer S, Konstas I, Cheung A, Zettlemoyer L. Summarizing source code using a neural attention model. In: Proc. of the 54th Annual
                     Meeting of the Association for Computational Linguistics. Berlin: ACL, 2016. 2073–2083. [doi: 10.18653/v1/P16-1195]
                 [13]  Hu X, Li G, Xia X, Lo D, Jin Z. Deep code comment generation. In: Proc. of the 26th Conf. on Program Comprehension. Gothenburg:
                     ACM, 2018. 200–210. [doi: 10.1145/3196321.319633]
                 [14]  Ahmad W, Chakraborty S, Ray B, Chang KW. A Transformer-based approach for source code summarization. In: Proc. of the 58th
                     Annual Meeting of the Association for Computational Linguistics. ACL, 2020. 4998–5007. [doi: 10.18653/v1/2020.acl-main.449]
                 [15]  Chen QY, Zhou MH. A neural framework for retrieval and summarization of source code. In: Proc. of the 33rd ACM/IEEE Int’l Conf. on
                     Automated Software Engineering. Montpellier: IEEE, 2018. 826–831. [doi: 10.1145/3238147.3240471]
                 [16]  Hu X, Li G, Xia X, Lo D, Jin Z. Deep code comment generation with hybrid lexical and syntactical information. Empirical Software
                     Engineering, 2020, 25(3): 2179–2217. [doi: 10.1007/s10664-019-09730-9]
                 [17]  LeClair A, Haque S, Wu LF, McMillan C. Improved code summarization via a graph neural network. In: Proc. of the 28th Int’l Conf. on
                     Program Comprehension. Seoul: ACM, 2020. 184–195. [doi: 10.1145/3387904.3389268]
                 [18]  Zhang J, Wang X, Zhang HY, Sun HL, Liu XD. Retrieval-based neural source code summarization. In: Proc. of the 42nd ACM/IEEE Int’l
                     Conf. on Software Engineering. Seoul: IEEE, 2020. 1385–1397. [doi: 10.1145/3377811.338038]
                 [19]  Gros D, Sezhiyan H, Devanbu P, Yu Z. Code to comment “translation”: Data, metrics, baselining & evaluation. In: Proc. of the 35th
                     IEEE/ACM Int’l Conf. on Automated Software Engineering. Melbourne: IEEE, 2020. 746–757.
                 [20]  Hu X, Xia X, Lo D, Wan ZY, Chen QY, Zimmermann T. Practitioners’ expectations on automated code comment generation. In: Proc. of
                     the 44th Int’l Conf. on Software Engineering. Pittsburgh: IEEE, 2022. 1693–1705. [doi: 10.1145/3510003.3510152]
                 [21]  Steidl D, Hummel B, Juergens E. Quality analysis of source code comments. In: Proc. of the 21st Int’l Conf. on Program Comprehension.
                     San Francisco: IEEE, 2013. 83–92. [doi: 10.1109/ICPC.2013.6613836]
                 [22]  Yu  H,  Li  B,  Wang  PX,  Jia  D,  Wang  YJ.  Source  code  comments  quality  assessment  method  based  on  aggregation  of  classification
                     algorithms. Journal of Computer Applications, 2016, 36(12): 3448–3453, 3467 (in Chinese with English abstract). [doi: 10.11772/j.issn.
                     1001-9081.2016.12.3448]
                 [23]  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. Philadelphia: ACL, 2002. 311–318. [doi: 10.3115/1073083.1073135]
                 [24]  Banerjee S, Lavie A. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proc. of
                     the 2005 ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. Ann Arbor:
                     ACL, 2005. 65–72.
                 [25]  Lin CY. ROUGE: A package for automatic evaluation of summaries. In: Proc. of the 2004 Text Summarization Branches Out. Barcelona:
                     ACL, 2004. 74–81.
                 [26]  Vedantam R, Lawrence Zitnick C, Parikh D. CIDEr: Consensus-based image description evaluation. In: Proc. of the 2015 IEEE Conf. on
                     Computer Vision and Pattern Recognition. Boston: IEEE, 2015. 4566–4575. [doi: 10.1109/CVPR.2015.7299087]
   336   337   338   339   340   341   342   343   344   345   346