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We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems.
2011
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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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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: Association for Computing Machinery, 2014, p. 419–428. [Online]. Available: https://doi.org/10.1145/2594291.2594321
2014
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M. Allamanis and C. Sutton, “Mining idioms from source code,” in Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering , 2014, pp. 472–483
2014
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O. Vinyals, M. Fortunato, and N. Jaitly, “Pointer networks,” arXiv preprint arXiv:1506.03134 , 2015
2015
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A. Hindle, E. T. Barr, M. Gabel, Z. Su, and P. Devanbu, “On the naturalness of software,” Communications of the ACM , vol. 59, no. 5, pp. 122–131, 2016
2016
Earlier work this paper cites.
C. Liu, X. Wang, R. Shin, J. E. Gonzalez, and D. Song, “Neural code completion,” 2016. [Online]. Available: https://openreview.net/forum?id=rJbPBt9lg
2016
Earlier work this paper cites.
P. Bielik, V. Raychev, and M. Vechev, “PHOG: probabilistic model for code,” in International Conference on Machine Learning , 2016, pp. 2933–2942
2016
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V. Raychev, P. Bielik, M. Vechev, and A. Krause, “Learning programs from noisy data,” in Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages , ser. POPL ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 761–774. [Online]. Available: https://doi.org/10.1145/2837614.2837671
2016
Earlier work this paper cites.
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
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“150k python dataset,” 2016. [Online]. Available: https://eth-sri.github.io/py150
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “”why should i trust you?”: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1135–1144. [Online]. Available: https://doi.org/10.1145/2939672.2939778
2016
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P. Bielik, V. Raychev, and M. Vechev, “Program synthesis for character level language modeling,” in International Conference on Learning Representations , 2016. [Online]. Available: https://openreview.net/forum?id=ry_sjFqgx
2016
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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 , 2017, pp. 763–773
2017
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“Pretrained probabilistic models for code,” 2017. [Online]. Available: https://github.com/eth-sri/ModelsPHOG
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. Red Hook, NY, USA: Curran Associates Inc., 2017, p. 6000–6010
2017
Earlier work this paper cites.
V. J. Hellendoorn and P. T. 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 , 2017
2017
Earlier work this paper cites.
S. Chakraborty, R. Tomsett, R. Raghavendra, D. Harborne, M. Alzantot, F. Cerutti, M. Srivastava, A. Preece, S. Julier, R. M. Rao et al. , “Interpretability of deep learning models: a survey of results,” in 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) . IEEE, 2017, pp. 1–6
2017
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2017
Earlier work this paper cites.
M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in Proceedings of the 34th International Conference on Machine Learning - Volume 70 , ser. ICML’17. JMLR.org, 2017, p. 3319–3328
2017
Earlier work this paper cites.
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
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J. Li, Y. Wang, M. R. Lyu, and I. King, “Code completion with neural attention and pointer networks,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , ser. IJCAI’18. AAAI Press, 2018, p. 4159–25
2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Akhtar and A. Mian, “Threat of adversarial attacks on deep learning in computer vision: A survey,” IEEE Access , vol. 6, pp. 14 410–14 430, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Ahmed, M. R. Samee, and R. E. Mercer, “You only need attention to traverse trees,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 316–322. [Online]. Available: https://www.aclweb.org/anthology/P19-1030/
2019
Later among the works it cites.
TabNine, “Autocompletion with deep learning,” TabNine Blog , Jul 2019. [Online]. Available: https://tabnine.com/blog/deep
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Yang and C. Xiang, “Improve language modelling for code completion through learning general token repetition of source code,” in 31st International Conference Software Engineering and Knowledge Engineering , 2019, pp. 667–777
2019
Later among the works it cites.
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Q.-S. Zhang and S.-C. Zhu, “Visual interpretability for deep learning: a survey,” Frontiers of Information Technology & Electronic Engineering , vol. 19, no. 1, pp. 27–39, 2018
2018
Cited alongside, same era.
M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=BJOFETxR-
2018
Cited alongside, same era.
A. Svyatkovskiy, Y. Zhao, S. Fu, and N. Sundaresan, “Pythia: AI-assisted code completion system,” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , Jul 2019. [Online]. Available: http://dx.doi.org/10.1145/3292500.3330699
2019
Cited alongside, same era.
U. Alon, O. Levy, and E. Yahav, “code2seq: Generating sequences from structured representations of code,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=H1gKYo09tX
2019
Cited alongside, same era.
L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou, and H.-W. Hon, “Unified language model pre-training for natural language understanding and generation,” in Advances in Neural Information Processing Systems , 2019, pp. 13 042–13 054. [Online]. Available: https://papers.nips.cc/paper/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” in OpenAI Blog , 2019. [Online]. Available: https://openai.com/blog/better-language-models/
2019
Cited alongside, same era.
U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “code2vec: Learning distributed representations of code,” Proceedings of the ACM on Programming Languages , vol. 3, no. POPL, pp. 1–29, 2019. [Online]. Available: https://doi.org/10.1145/3290353
2019
Cited alongside, same era.
2019
Cited alongside, same era.
P. Fernandes, M. Allamanis, and M. Brockschmidt, “Structured neural summarization,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=H1ersoRqtm
2019
Later among the works it cites.
M. Cvitkovic, B. Singh, and A. Anandkumar, “Open vocabulary learning on source code with a graph-structured cache,” in Proceedings of the 36th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. Long Beach, California, USA: PMLR, 09–15 Jun 2019, pp. 1475–1485. [Online]. Available: http://proceedings.mlr.press/v97/cvitkovic19b.html
2019
Later among the works it cites.
F. Liu, L. Zhang, and Z. Jin, “Modeling programs hierarchically with stack-augmented LSTM,” Journal of Systems and Software , p. 110547, 2020. [Online]. Available: https://doi.org/10.1016/j.jss.2020.110547
2020
Closest in time.
R.-M. Karampatsis, H. Babii, R. Robbes, C. Sutton, and A. Janes, “Big code != big vocabulary: Open-vocabulary models for source code,” in International Conference on Software Engineering (ICSE) , 2020
2020
Closest in time.
2020
Closest in time.
V. J. Hellendoorn, C. Sutton, R. Singh, and P. Maniatis, “Global relational models of source code,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=B1lnbRNtwr
2020
Closest in time.
W. E. Zhang, Q. Z. Sheng, A. Alhazmi, and C. Li, “Adversarial attacks on deep-learning models in natural language processing: A survey,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 11, no. 3, pp. 1–41, 2020
2020
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X. Huang, D. Kroening, W. Ruan, J. Sharp, Y. Sun, E. Thamo, M. Wu, and X. Yi, “A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability,” Computer Science Review , vol. 37, p. 100270, 2020
2020
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N. Yefet, U. Alon, and E. Yahav, “Adversarial examples for models of code,” Proceedings of the ACM on Programming Languages , vol. 4, no. OOPSLA, Nov. 2020. [Online]. Available: https://doi.org/10.1145/3428230
2020
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2020
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J. Jiarpakdee, C. Tantithamthavorn, H. K. Dam, and J. Grundy, “An empirical study of model-agnostic techniques for defect prediction models,” IEEE Transactions on Software Engineering , pp. 1–1, 2020
2020
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U. Alon, R. Sadaka, O. Levy, and E. Yahav, “Structural language models of code,” in Proceedings of the 37th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 245–256. [Online]. Available: http://proceedings.mlr.press/v119/alon20a.html
2020
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A. Svyatkovskiy, S. Lee, A. Hadjitofi, M. Riechert, J. Franco, and M. Allamanis, “Fast and memory-efficient neural code completion,” 2020
2020
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X.-P. Nguyen, S. Joty, S. Hoi, and R. Socher, “Tree-structured attention with hierarchical accumulation,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=HJxK5pEYvr
2020
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A. Svyatkovskiy, S. K. Deng, S. Fu, and N. Sundaresan, IntelliCode Compose: Code Generation Using Transformer . New York, NY, USA: Association for Computing Machinery, 2020, p. 1433–1443
2020
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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) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 2073–2083. [Online]. Available: https://www.aclweb.org/anthology/P16-1195
2083
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M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in Proceedings of The 33rd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, M. F. Balcan and K. Q. Weinberger, Eds., vol. 48. New York, New York, USA: PMLR, 20–22 Jun 2016, pp. 2091–2100. [Online]. Available: http://proceedings.mlr.press/v48/allamanis16.html
2091
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