Page 221 - 《软件学报》2021年第7期

陈翔 等:代码注释自动生成方法综述 2139

[58] Kang HJ, Bissyandé TF, Lo D. Assessing the generalizability of code2vec token embeddings. In: Proc. of the 34th IEEE/ACM Int’l
Conf. on Automated Software Engineering. 2019. 1–12.
[59] Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Proc. of the 2014 Conf. on Empirical
Methods in Natural Language Processing. 2014. 1532–1543.
[60] LeClair A, Jiang S, McMillan C. A neural model for generating natural language summaries of program subroutines. In: Proc. of
the 41st IEEE/ACM Int’l Conf. on Software Engineering. 2019. 795–806.
[61] Ahmad WU, Chakraborty S, Ray B, Chang KW. A transformer-based approach for source code summarization. arXiv Preprint
arXiv: 2005.00653, 2020.
[62] Loyola P, Marrese-Taylor E, Matsuo Y. A neural architecture for generating natural language descriptions from source code
changes. arXiv Preprint arXiv: 1704.04856, 2017.
[63] Jiang S, McMillan C. Towards automatic generation of short summaries of commits. In: Proc. of the 25th IEEE/ACM Int’l Conf. on
Program Comprehension. 2017. 320–323.
[64] Xu S, Yao Y, Xu F, Gu T, Tong H, Lu J. Commit message generation for source code changes. In: Proc. of the 27th Int’l Joint Conf.
on Artificial Intelligence. 2019. 3975–3981.
[65] Jiang S. Boosting neural commit message generation with code semantic analysis. In: Proc. of the 34th IEEE/ACM Int’l Conf. on
Automated Software Engineering. 2019. 1280–1282.
[66] Liu S, Gao C, Chen S, Nie LY, Liu Y. ATOM: Commit message generation based on abstract syntax tree and hybrid ranking. IEEE
Trans. on Software Engineering, 2020.
[67] Shido Y, Kobayashi Y, Yamamoto A, Miyamoto A, Matsumura T. Automatic source code summarization with extended tree-LSTM.
In: Proc. of the 2019 Int’l Joint Conf. on Neural Networks. 2019. 1–8.
[68] LeClair A, Haque S, Wu L, McMillan C. Improved code summarization via a graph neural network. In: Proc. of the Int’l Conf. on
Program Comprehension. 2020.
[69] Liu S, Chen Y, Xie X, Siow JK, Liu Y. Automatic code summarization via multi-dimensional semantic fusing in GNN. arXiv
Preprint arXiv: 2006.05405, 2020.
[70] Wan Y, Zhao Z, Yang M, Xu G, Ying H, Wu J, Yu PS. Improving automatic source code summarization via deep reinforcement
learning. In: Proc. of the 33rd ACM/IEEE Int’l Conf. on Automated Software Engineering. 2018. 397407.
[71] Wang W, Zhang Y, Sui Y, Wan Y, Zhao Z, WuJ, Xu G. Reinforcement-learning-guided source code summarization via hierarchical
attention. IEEE Trans. on Software Engineering, 2020.
[72] Chen Q, Zhou M. A neural framework for retrieval and summarization of source code. In: Proc. of the 33rd IEEE/ACM Int’l Conf.
on Automated Software Engineering. 2018. 826–831.
[73] Ye W, Xie R, Zhang J, Hu T, Wang X, Zhang S. Leveraging code generation to improve code retrieval and summarization via dual
learning. In: Proc. of the Web Conf. 2020. 2309–2319.
[74] Wang W, Zhang, Y, Zeng Z, Xu G. Trans. A transformer-based framework for unifying code summarization and code search. arXiv
Preprint arXiv: 2003.03238, 2020.
[75] Wei B, Li G, Xia X, Fu Z, Jin Z. Code generation as a dual task of code summarization. In: Advances in Neural Information
Processing Systems. 2019. 6563–6573.
[76] 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. 2018. 2269–2275.
[77] Zhang J, Wang X, Zhang H, Sun H, Liu X. Retrieval-based neural source code summarization. In: Proc. of the 42nd Int’l Conf. on
Software Engineering. 2020.
[78] Liu B, Wang T, Zhang X, Fan Q, Yin G, Deng J. A neural-network based code summarization approach by using source code and
its call dependencies. In: Proc. of the 11th Asia-Pacific Symp. on Internetware. 2019. 1–10.
[79] Zhou Y, Yan X, Yang W, Chen T, Huang Z. Augmenting Java method comments generation with context information based on
neural networks. Journal of Systems and Software, 2019,156:328–340.