publication . Preprint . Conference object . 2020

Typilus: Neural Type Hints

Miltiadis Allamanis; Earl T. Barr; Soline Ducousso; Zheng Gao;
Open Access English
  • Published: 06 Jun 2020
Abstract
Comment: Accepted to PLDI 2020
Persistent Identifiers
Subjects
free text keywords: Computer Science - Programming Languages, Computer Science - Machine Learning, Statistics - Machine Learning, Artificial intelligence, business.industry, business, Discrete space, Identifier, Vocabulary, media_common.quotation_subject, media_common, Similarity learning, Type inference, Structured prediction, Computer science, Deep learning, Theoretical computer science, Python (programming language), computer.programming_language, computer
60 references, page 1 of 4

[1] Miltiadis Allamanis. 2019. The Adverse Efects of Code Duplication in Machine Learning Models of Code. In SPLASH Onward! [OpenAIRE]

[2] Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton. 2014. Learning Natural Coding Conventions. In Proceedings of the International Symposium on Foundations of Software Engineering (FSE).

[3] Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton. 2015. Suggesting accurate method and class names. In Proceedings of the Joint Meeting of the European Software Engineering Conference and the Symposium on the Foundations of Software Engineering (ESEC/FSE). [OpenAIRE]

[4] Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR) 51, 4 (2018), 81.

[5] Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to Represent Programs with Graphs. In Proceedings of the International Conference on Learning Representations (ICLR).

[6] Miltiadis Allamanis and Charles Sutton. 2013. Mining source code repositories at massive scale using language modeling. In Proceedings of the Working Conference on Mining Software Repositories (MSR). IEEE Press, 207-216.

[7] Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, and Yi Wei. 2015. Bimodal modelling of source code and natural language. In Proceedings of the International Conference on Machine Learning (ICML).

[8] Uri Alon, Omer Levy, and Eran Yahav. 2010. code2seq: Generating Sequences from Structured Representations of Code. In Proceedings of the International Conference on Learning Representations (ICLR).

[9] Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. code2vec: Learning distributed representations of code. Proceedings of the ACM on Programming Languages 3, POPL (2019), 40.

[10] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR).

[11] Antonio Valerio Miceli Barone and Rico Sennrich. 2017. A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Vol. 2. 314-319.

[12] Rohan Bavishi, Michael Pradel, and Koushik Sen. 2018. Context2Name: A deep learning-based approach to infer natural variable names from usage contexts. arXiv preprint arXiv:1809.05193 (2018).

[13] Pavol Bielik, Veselin Raychev, and Martin Vechev. 2016. PHOG: Probabilistic Model for Code. In Proceedings of the International Conference on Machine Learning (ICML). 2933-2942.

[14] Gilad Bracha. 2004. Pluggable type systems. In OOPSLA workshop on revival of dynamic languages, Vol. 4.

[15] Marc Brockschmidt, Miltiadis Allamanis, Alexander L Gaunt, and Oleksandr Polozov. 2019. Generative code modeling with graphs. In International Conference in Learning Representations.

60 references, page 1 of 4
Abstract
Comment: Accepted to PLDI 2020
Persistent Identifiers
Subjects
free text keywords: Computer Science - Programming Languages, Computer Science - Machine Learning, Statistics - Machine Learning, Artificial intelligence, business.industry, business, Discrete space, Identifier, Vocabulary, media_common.quotation_subject, media_common, Similarity learning, Type inference, Structured prediction, Computer science, Deep learning, Theoretical computer science, Python (programming language), computer.programming_language, computer
60 references, page 1 of 4

[1] Miltiadis Allamanis. 2019. The Adverse Efects of Code Duplication in Machine Learning Models of Code. In SPLASH Onward! [OpenAIRE]

[2] Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton. 2014. Learning Natural Coding Conventions. In Proceedings of the International Symposium on Foundations of Software Engineering (FSE).

[3] Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton. 2015. Suggesting accurate method and class names. In Proceedings of the Joint Meeting of the European Software Engineering Conference and the Symposium on the Foundations of Software Engineering (ESEC/FSE). [OpenAIRE]

[4] Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR) 51, 4 (2018), 81.

[5] Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to Represent Programs with Graphs. In Proceedings of the International Conference on Learning Representations (ICLR).

[6] Miltiadis Allamanis and Charles Sutton. 2013. Mining source code repositories at massive scale using language modeling. In Proceedings of the Working Conference on Mining Software Repositories (MSR). IEEE Press, 207-216.

[7] Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, and Yi Wei. 2015. Bimodal modelling of source code and natural language. In Proceedings of the International Conference on Machine Learning (ICML).

[8] Uri Alon, Omer Levy, and Eran Yahav. 2010. code2seq: Generating Sequences from Structured Representations of Code. In Proceedings of the International Conference on Learning Representations (ICLR).

[9] Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. code2vec: Learning distributed representations of code. Proceedings of the ACM on Programming Languages 3, POPL (2019), 40.

[10] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR).

[11] Antonio Valerio Miceli Barone and Rico Sennrich. 2017. A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Vol. 2. 314-319.

[12] Rohan Bavishi, Michael Pradel, and Koushik Sen. 2018. Context2Name: A deep learning-based approach to infer natural variable names from usage contexts. arXiv preprint arXiv:1809.05193 (2018).

[13] Pavol Bielik, Veselin Raychev, and Martin Vechev. 2016. PHOG: Probabilistic Model for Code. In Proceedings of the International Conference on Machine Learning (ICML). 2933-2942.

[14] Gilad Bracha. 2004. Pluggable type systems. In OOPSLA workshop on revival of dynamic languages, Vol. 4.

[15] Marc Brockschmidt, Miltiadis Allamanis, Alexander L Gaunt, and Oleksandr Polozov. 2019. Generative code modeling with graphs. In International Conference in Learning Representations.

60 references, page 1 of 4
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