Fetching the paper…
Reading the bibliography…
Code comment has been an important part of computer programs, greatly facilitating the understanding and maintenance of source code.
M. Röscheisen, M. Baldonado, K. Chang, L. Gravano, S. Ketchpel, and A. Paepcke, “The stanford infobus and its service layers: Augmenting the internet with higher-level information management protocols,” in Digital Libraries in Computer Science: The MeDoc Approach . Springer, 1998, pp. 213–230
1998
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
S. Haiduc, J. Aponte, L. Moreno, and A. Marcus, “On the use of automated text summarization techniques for summarizing source code,” 2010
2010
Earlier work this paper cites.
S. Haiduc, J. Aponte, and A. Marcus, “Supporting program comprehension with source code summarization,” in 2010 acm/ieee 32nd international conference on software engineering , vol. 2. IEEE, 2010, pp. 223–226
2010
Earlier work this paper cites.
G. Sridhara, E. Hill, D. Muppaneni, L. Pollock, and K. Vijay-Shanker, “Towards automatically generating summary comments for java methods,” in Proceedings of the IEEE/ACM international conference on Automated software engineering , 2010, pp. 43–52
2010
Earlier work this paper cites.
G. Sridhara, L. Pollock, and K. Vijay-Shanker, “Automatically detecting and describing high level actions within methods,” in 2011 33rd International Conference on Software Engineering (ICSE) . IEEE, 2011, pp. 101–110
2011
Earlier work this paper cites.
B. P. Eddy, J. A. Robinson, N. A. Kraft, and J. C. Carver, “Evaluating source code summarization techniques: Replication and expansion,” in 2013 21st International Conference on Program Comprehension (ICPC) . IEEE, 2013, pp. 13–22
2013
Earlier work this paper cites.
L. Moreno, J. Aponte, G. Sridhara, A. Marcus, L. Pollock, and K. Vijay-Shanker, “Automatic generation of natural language summaries for java classes,” in 2013 21st International Conference on Program Comprehension (ICPC) . IEEE, 2013, pp. 23–32
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Denkowski and A. Lavie, “Meteor universal: Language specific translation evaluation for any target language,” in Proceedings of the ninth workshop on statistical machine translation , 2014, pp. 376–380
2014
Earlier work this paper cites.
B. Chen and C. Cherry, “A systematic comparison of smoothing techniques for sentence-level bleu,” in Proceedings of the Ninth Workshop on Statistical Machine Translation , 2014, pp. 362–367
2014
Earlier work this paper cites.
E. Wong, T. Liu, and L. Tan, “Clocom: Mining existing source code for automatic comment generation,” in 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER) . IEEE, 2015, pp. 380–389
2015
Earlier work this paper cites.
P. W. McBurney and C. McMillan, “Automatic source code summarization of context for java methods,” IEEE Transactions on Software Engineering , vol. 42, no. 2, pp. 103–119, 2015
2015
Earlier work this paper cites.
P. Rodeghero, C. Liu, P. W. McBurney, and C. McMillan, “An eye-tracking study of java programmers and application to source code summarization,” IEEE Transactions on Software Engineering , vol. 41, no. 11, pp. 1038–1054, 2015
2015
Earlier work this paper cites.
D. Tapscott and A. Tapscott, Blockchain revolution: how the technology behind bitcoin is changing money, business, and the world . Penguin, 2016
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
A. Savelyev, “Contract law 2.0:‘smart’contracts as the beginning of the end of classic contract law,” Information & Communications Technology Law , vol. 26, no. 2, pp. 116–134, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
T. Sun and W. Yu, “A formal verification framework for security issues of blockchain smart contracts,” Electronics , vol. 9, no. 2, p. 255, 2020
2020
Later among the works it cites.
Z. Yang, J. Keung, M. Zhang, Y. Xiao, Y. Huang, and T. Hui, “Smart contracts vulnerability auditing with multi-semantics,” in 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) . IEEE, 2020, pp. 892–901
2020
Later among the works it cites.
N. He, L. Wu, H. Wang, Y. Guo, and X. Jiang, “Characterizing code clones in the ethereum smart contract ecosystem,” in International Conference on Financial Cryptography and Data Security . Springer, 2020, pp. 654–675
2020
Later among the works it cites.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation with hybrid lexical and syntactical information,” Empirical Software Engineering , vol. 25, no. 3, pp. 2179–2217, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Lu, Z. Zhao, G. Li, and Z. Jin, “Learning to generate comments for api-based code snippets,” in Software Engineering and Methodology for Emerging Domains . Springer, 2017, pp. 3–14
2017
Cited alongside, same era.
Y. Liang and K. Zhu, “Automatic generation of text descriptive comments for code blocks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Cited alongside, same era.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation,” in 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) . IEEE, 2018, pp. 200–20 010
2018
Cited alongside, same era.
Y. Wan, Z. Zhao, M. Yang, G. Xu, H. Ying, J. Wu, and P. S. Yu, “Improving automatic source code summarization via deep reinforcement learning,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , 2018, pp. 397–407
2018
Cited alongside, same era.
X. HU, G. LI, X. XIA, D. LO, S. LU, and Z. JIN, “Summarizing source code with transferred api knowledge.(2018),” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelli-gence (IJCAI 2018), Stockholm, Sweden, 2018 July 13 , vol. 19, pp. 2269–2275
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. LeClair, S. Jiang, and C. McMillan, “A neural model for generating natural language summaries of program subroutines,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 795–806
2019
Cited alongside, same era.
U. Alon, S. Brody, O. Levy, and E. Yahav, “code2seq: Generating sequences from structured representations of code,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=H1gKYo09tX
2019
Cited alongside, same era.
A. LeClair, S. Haque, L. Wu, and C. McMillan, “Improved code summarization via a graph neural network,” in Proceedings of the 28th International Conference on Program Comprehension , ser. ICPC ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 184–195. [Online]. Available: https://doi.org/10.1145/3387904.3389268
2020
Later among the works it cites.
W. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “A transformer-based approach for source code summarization,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 4998–5007. [Online]. Available: https://www.aclweb.org/anthology/2020.acl-main.449
2020
Later among the works it cites.
J. Zhang, X. Wang, H. Zhang, H. Sun, and X. Liu, “Retrieval-based neural source code summarization,” in 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) . IEEE, 2020, pp. 1385–1397
2020
Later among the works it cites.
Y. Zhuang, Z. Liu, P. Qian, Q. Liu, X. Wang, and Q. He, “Smart contract vulnerability detection using graph neural network,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 , C. Bessiere, Ed. International Joint Conferences on Artificial Intelligence Organization, 7 2020, pp. 3283–3290, main track. [Online]. Available: https://doi.org/10.24963/ijcai.2020/454
2020
Later among the works it cites.
W. Wang, Y. Zhang, Y. Sui, Y. Wan, Z. Zhao, J. Wu, P. Yu, and G. Xu, “Reinforcement-learning-guided source code summarization via hierarchical attention,” IEEE Transactions on Software Engineering , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
“Ethereum (eth) blockchain explorer,” https://etherscan.io/ , 01 2021, (Accessed on 01/26/2021)
2021
Closest in time.
“Smart contract code summarization dataset — zenodo,” https://zenodo.org/record/4587089#.YEog9-gzYuV , (Accessed on 03/11/2021)
2021
Closest in time.
“yz1019117968/icpc-21-mmtrans: Mmtrans for smart contract code summarization,” https://github.com/yz1019117968/ICPC-21-MMTrans , (Accessed on 03/11/2021)
2021
Closest in time.
“Solidity — solidity 0.6.0 documentation,” https://docs.soliditylang.org/en/v0.6.0/index.html , 01 2021, (Accessed on 01/09/2021)
2021
Closest in time.
“federicobond/solidity-parser-antlr: A solidity parser for js built on top of a robust antlr4 grammar,” https://github.com/federicobond/solidity-parser-antlr , 01 2021, (Accessed on 01/26/2021)
2021
Closest in time.
S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Summarizing source code using a neural attention model,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2016, pp. 2073–2083
2083
Closest in time.