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Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc.
Statistical phrase-based translation
Philipp Koehn, Franz J Och, and Daniel Marcu · 2003
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Locality-sensitive hashing scheme based on p-stable distributions
Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni · 2004
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Deckard: Scalable and accurate tree-based detection of code clones
Lingxiao Jiang, Ghassan Misherghi, Zhendong Su, and Stephane Glondu · 2007
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Lexical statistical machine translation for language migration
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N Nguyen · 2013
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Phrase-based statistical translation of programming languages
Svetoslav Karaivanov, Veselin Raychev, and Martin Vechev · 2014
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Towards a big data curated benchmark of inter-project code clones
Jeffrey Svajlenko, Judith F Islam, Iman Keivanloo, Chanchal K Roy, and Mohammad Mamun Mia · 2014
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Graph-based statistical language model for code
Anh Tuan Nguyen and Tien N Nguyen · 2015
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Divide-and-conquer approach for multi-phase statistical migration for source code (t)
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N Nguyen · 2015
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Deep learning code fragments for code clone detection
Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk · 2016
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Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2017
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Abstract syntax networks for code generation and semantic parsing
Maxim Rabinovich, Mitchell Stern, and Dan Klein · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code
Huihui Wei and Ming Li · 2017
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A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig · 2017
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2018
Cited alongside, same era.
code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav · 2018
Cited alongside, same era.
Generative code modeling with graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L Gaunt, and Oleksandr Polozov · 2018
Cited alongside, same era.
Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Tree-structured attention with hierarchical accumulation
Xuan-Phi Nguyen, Shafiq Joty, Steven Hoi, and Richard Socher · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
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Dialog-to-action: Conversational question answering over a large-scale knowledge base
Daya Guo, Duyu Tang, Nan Duan, M. Zhou, and Jian Yin · 2018
Cited alongside, same era.
Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Structural language models of code
Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav · 2019
Cited alongside, same era.
Coupling retrieval and meta-learning for context-dependent semantic parsing
Daya Guo, Duyu Tang, Nan Duan, M. Zhou, and Jian Yin · 2019
Cited alongside, same era.
Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2019
Cited alongside, same era.
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le · 2019
Later among the works it cites.
Exploring software naturalness throughneural language models
Luca Buratti, Saurabh Pujar, Mihaela Bornea, Scott McCarley, Yunhui Zheng, Gaetano Rossiello, Alessandro Morari, Jim Laredo, Veronika Thost, Yufan Zhuang, et al · 2020
Closest in time.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
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Scelmo: Source code embeddings from language models
Rafael-Michael Karampatsis and Charles Sutton · 2020
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Code prediction by feeding trees to transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra · 2020
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Intellicode compose: Code generation using transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2020
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Detecting code clones with graph neural networkand flow-augmented abstract syntax tree
Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin · 2020
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A novel neural source code representation based on abstract syntax tree
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong Liu · 2020
Closest in time.