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Code completion is a popular software development tool integrated into all major IDEs.
1903
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2013
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T. T. Nguyen, A. T. Nguyen, H. A. Nguyen, and T. N. Nguyen, “A statistical semantic language model for source code,” in Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering , ser. ESEC/FSE 2013. New York, NY, USA: Association for Computing Machinery, 2013, p. 532–542. [Online]. Available: https://doi.org/10.1145/2491411.2491458
2013
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V. Raychev, M. Vechev, and E. Yahav, “Code completion with statistical language models,” in Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation , ser. PLDI ’14. New York, NY, USA: ACM, 2014, pp. 419–428. [Online]. Available: http://doi.acm.org/10.1145/2594291.2594321
2014
J. Li, Y. Wang, M. R. Lyu, and I. King, “Code completion with neural attention and pointer networks,” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence , Jul 2018. [Online]. Available: http://dx.doi.org/10.24963/ijcai.2018/578
2018
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M. Brockschmidt, M. Allamanis, A. L. Gaunt, and O. Polozov, “Generative code modeling with graphs,” 2019
2019
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V. J. Hellendoorn, S. Proksch, H. C. Gall, and A. Bacchelli, “When code completion fails: A case study on real-world completions,” ser. ICSE ’19. IEEE Press, 2019, p. 960–970. [Online]. Available: https://doi.org/10.1109/ICSE.2019.00101
2019
Later among the works it cites.
M. Allamanis, “The adverse effects of code duplication in machine learning models of code,” 2019
2019
Later among the works it cites.
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R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” 2015
2015
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V. Raychev, P. Bielik, and M. Vechev, “Probabilistic model for code with decision trees,” in Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications , ser. OOPSLA 2016. New York, NY, USA: Association for Computing Machinery, 2016, p. 731–747. [Online]. Available: https://doi.org/10.1145/2983990.2984041
2016
Cited alongside, same era.
P. Bielik, V. Raychev, and M. Vechev, “Phog: Probabilistic model for code,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 , ser. ICML’16. JMLR.org, 2016, p. 2933–2942
2016
Cited alongside, same era.
V. J. Hellendoorn and P. Devanbu, “Are deep neural networks the best choice for modeling source code?” in Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering , ser. ESEC/FSE 2017. New York, NY, USA: Association for Computing Machinery, 2017, p. 763–773. [Online]. Available: https://doi.org/10.1145/3106237.3106290
2017
Cited alongside, same era.
2020
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S. Kim, J. Zhao, Y. Tian, and S. Chandra, “Code prediction by feeding trees to transformers,” 2020
2020
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2020
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