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Current language models tailored for code tasks often adopt the pre-training-then-fine-tuning paradigm from natural language processing, modeling source code as plain text.
Control flow analysis
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Natural language models for predicting programming comments
Dana Movshovitz-Attias and William Cohen · 2013
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Lexical statistical machine translation for language migration
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien Nhut Nguyen · 2013
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Learning natural coding conventions
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2014
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Code completion with statistical language models
Veselin Raychev, Martin Vechev, and Eran Yahav · 2014
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A convolutional attention network for extreme summarization of source code
Miltiadis Allamanis, Hao Peng, and Charles Sutton · 2016
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Language to logical form with neural attention
Li Dong and Mirella Lapata · 2016
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Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim · 2016
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Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2016
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin · 2016
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Top-down tree long short-term memory networks
Xingxing Zhang, Liang Lu, and Mirella Lapata · 2016
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Convolutional neural networks over control flow graphs for software defect prediction
Anh Viet Phan, Minh Le Nguyen, and Lam Thu Bui · 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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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
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Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song · 2018
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Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
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Mapping language to code in programmatic context
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2018
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TRANX: A transition-based neural abstract syntax parser for semantic parsing and code generation
Pengcheng Yin and Graham Neubig · 2018
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Learning to mine aligned code and natural language pairs from stack overflow
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig · 2018
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Codesearchnet challenge: Evaluating the state of semantic code search
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Treebert: A tree-based pre-trained model for programming language
Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, and Lei Lyu · 2021
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Code prediction by feeding trees to transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra · 2021
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Dobf: A deobfuscation pre-training objective for programming languages
Marie-Anne Lachaux, Baptiste Roziere, Marc Szafraniec, and Guillaume Lample · 2021
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Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
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Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
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Novel positional encodings to enable tree-based transformers
Vighnesh Shiv and Chris Quirk · 2019
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A grammar-based structural CNN decoder for code generation
Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, and Lu Zhang · 2019
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Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu · 2019
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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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Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, et al · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy · 2020
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Integrating tree path in transformer for code representation
Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, and Zhi Jin · 2021
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Code completion by modeling flattened abstract syntax trees as graphs
Yanlin Wang and Hui Li · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
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Language-agnostic representation learning of source code from structure and context
Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, and Stephan Günnemann · 2021
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Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin · 2022
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The stack: 3 tb of permissively licensed source code
Denis Kocetkov, Raymond Li, LI Jia, Chenghao Mou, Yacine Jernite, Margaret Mitchell, Carlos Muñoz Ferrandis, Sean Hughes, et al · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven C.H. Hoi · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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Rethinking positional encoding in tree transformer for code representation
Han Peng, Ge Li, Yunfei Zhao, and Zhi Jin · 2022
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Ast-trans: Code summarization with efficient tree-structured attention
Ze Tang, Xiaoyu Shen, Chuanyi Li, Jidong Ge, Liguo Huang, Zhelin Zhu, and Bin Luo · 2022
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AVATAR: A parallel corpus for java-python program translation
Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, and Kai-Wei Chang · 2023
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Starcoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
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Learning program representations with a tree-structured transformer
Wenhan Wang, Kechi Zhang, Ge Li, Shangqing Liu, Anran Li, Zhi Jin, and Yang Liu · 2023
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Structcoder: Structure-aware transformer for code generation
Sindhu Tipirneni, Ming Zhu, and Chandan K. Reddy · 2024
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