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A source code summary of a subroutine is a brief description of that subroutine.
Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences , vol. 55, no. 1, pp. 119–139, 1997. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S002200009791504X
1997
Earlier work this paper cites.
D. Kramer, “Api documentation from source code comments: a case study of javadoc,” in Proceedings of the 17th annual international conference on Computer documentation . ACM, 1999, pp. 147–153
1999
Earlier work this paper cites.
A. Forward and T. C. Lethbridge, “The relevance of software documentation, tools and technologies: a survey,” in Proceedings of the 2002 ACM symposium on Document engineering . ACM, 2002, pp. 26–33
2002
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 on association for computational linguistics . Association for Computational Linguistics, 2002, pp. 311–318
2002
Earlier work this paper cites.
S. Wang and X. Yao, “Diversity analysis on imbalanced data sets by using ensemble models,” in 2009 IEEE symposium on computational intelligence and data mining . IEEE, 2009, pp. 324–331
2009
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,” in 2010 17th Working Conference on Reverse Engineering . IEEE, 2010, pp. 35–44
2010
Earlier work this paper cites.
L. Shi, H. Zhong, T. Xie, and M. Li, “An empirical study on evolution of api documentation,” in International Conference on Fundamental Approaches To Software Engineering . Springer, 2011, pp. 416–431
2011
Earlier work this paper cites.
S.-L. Hsieh, S.-H. Hsieh, P.-H. Cheng, C.-H. Chen, K.-P. Hsu, I.-S. Lee, Z. Wang, and F. Lai, “Design ensemble machine learning model for breast cancer diagnosis,” Journal of medical systems , vol. 36, no. 5, pp. 2841–2847, 2012
2012
Earlier work this paper cites.
H. Zhong and Z. Su, “Detecting api documentation errors,” in Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications , 2013, pp. 803–816
2013
Earlier work this paper cites.
M. Asad, “Optimized stock market prediction using ensemble learning,” in 2015 9th International Conference on Application of Information and Communication Technologies (AICT) , 2015, pp. 263–268
2015
Earlier work this paper cites.
E. Garmash and C. Monz, “Ensemble learning for multi-source neural machine translation,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers . Osaka, Japan: The COLING 2016 Organizing Committee, Dec. 2016, pp. 1409–1418. [Online]. Available: https://www.aclweb.org/anthology/C16-1133
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Loyola, E. Marrese-Taylor, and Y. Matsuo, “A neural architecture for generating natural language descriptions from source code changes,” in ACL , 2017
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
R. Sennrich, O. Firat, K. Cho, A. Birch, B. Haddow, J. Hitschler, M. Junczys-Dowmunt, S. Läubli, A. V. Miceli Barone, J. Mokry, and M. Nadejde, “Nematus: a toolkit for neural machine translation,” in Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics . Valencia, Spain: Association for Computational Linguistics, April 2017, pp. 65–68. [Online]. Available: http://aclweb.org/anthology/E17-3017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton, “A survey of machine learning for big code and naturalness,” ACM Computing Surveys (CSUR) , vol. 51, no. 4, pp. 1–37, 2018
2018
Cited alongside, same era.
M. Z. Hossain, F. Sohel, M. F. Shiratuddin, and H. Laga, “A comprehensive survey of deep learning for image captioning,” ACM Computing Surveys (CsUR) , vol. 51, no. 6, pp. 1–36, 2019
2019
Later among the works it cites.
S. Borovkova and I. Tsiamas, “An ensemble of lstm neural networks for high-frequency stock market classification,” Journal of Forecasting , vol. 38, no. 6, pp. 600–619, 2019
2019
Later among the works it cites.
S. Al-Dahidi, O. Ayadi, M. Alrbai, and J. Adeeb, “Ensemble approach of optimized artificial neural networks for solar photovoltaic power prediction,” IEEE Access , vol. 7, pp. 81 741–81 758, 2019
2019
Later among the works it cites.
U. Alon, S. Brody, O. Levy, and E. Yahav, “code2seq: Generating sequences from structured representations of code,” International Conference on Learning Representations , 2019
2019
Later among the works it cites.
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2018
Cited alongside, same era.
K. Xu, L. Wu, Z. Wang, Y. Feng, M. Witbrock, and V. Sheinin, “Graph2seq: Graph to sequence learning with attention-based neural networks,” Conference on Empirical Methods in Natural Language Processing , 2018
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,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence . AAAI Press, 2018, pp. 2269–2275
2018
Cited alongside, same era.
O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , vol. 8, no. 4, p. e1249, 2018
2018
Cited alongside, same era.
S. Rasp and S. Lerch, “Neural networks for postprocessing ensemble weather forecasts,” Monthly Weather Review , vol. 146, no. 11, pp. 3885–3900, 2018
2018
Cited alongside, same era.
Y. Liang and K. Q. Zhu, “Automatic generation of text descriptive comments for code blocks,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. LeClair and C. McMillan, “Recommendations for datasets for source code summarization,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 3931–3937
2019
Cited alongside, same era.
M. Allamanis, “The adverse effects of code duplication in machine learning models of code,” in Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software , 2019, pp. 143–153
2019
Cited alongside, same era.
F. Zhao, J. Zhao, and Y. Bai, “A survey of automatic generation of code comments,” in Proceedings of the 2020 4th International Conference on Management Engineering, Software Engineering and Service Sciences , 2020, pp. 21–25
2020
Later among the works it cites.
S. Haque, A. LeClair, L. Wu, and C. McMillan, “Improved automatic summarization of subroutines via attention to file context,” International Conference on Mining Software Repositories , 2020
2020
Later among the works it cites.
A. LeClair, S. Haque, L. Wu, and C. McMillan, “Improved code summarization via a graph neural network,” in 28th ACM/IEEE International Conference on Program Comprehension (ICPC’20) , 2020
2020
Later among the works it cites.
R. Dabre, C. Chu, and A. Kunchukuttan, “A survey of multilingual neural machine translation,” ACM Computing Surveys (CSUR) , vol. 53, no. 5, pp. 1–38, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Liu, Y. Chen, X. Xie, J. K. Siow, and Y. Liu, “Retrieval-augmented generation for code summarization via hybrid {gnn},” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=zv-typ1gPxA
2021
Closest in time.
D. Zügner, T. Kirschstein, M. Catasta, J. Leskovec, and S. Günnemann, “Language-agnostic representation learning of source code from structure and context,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=Xh5eMZVONGF
2021
Closest in time.
A. Bansal, S. Haque, and M. C., “Project-level encoding for neural source code summarization of subroutines,” in 29th IEEE/ACM International Conference on Program Comprehension (ICPC’21) , 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.