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There has been a recent surge of interest in automating software engineering tasks using deep learning.
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Abstract Syntax Networks for Code Generation and Semantic Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 . 1139–1149
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A Syntactic Neural Model for General-Purpose Code Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 . 440–450
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Generative Code Modeling with Graphs. In 7th International Conference on Learning Representations, ICLR 2019
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CodeSearchNet challenge: Evaluating the state of semantic code search
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Roberta: A robustly optimized bert pretraining approach
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PyTorch: An Imperative Style, High-Performance Deep Learning Library. In NeurIPS 2019 . 8024–8035
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Language models are unsupervised multitask learners
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Xlnet: Generalized autoregressive pretraining for language understanding
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Structural language models of code. In International Conference on Machine Learning . PMLR, 245–256
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 . 1536–1547
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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An AST Structure Enhanced Decoder for Code Generation
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Exploring dynamic selection of branch expansion orders for code generation
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Code prediction by feeding trees to transformers. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 150–162
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Marie-Anne Lachaux, Baptiste Rozière, Marc Szafraniec, and Guillaume Lample. 2021 · 2021
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021
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Cited alongside, same era.
Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma. 2020 · 2020
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Unsupervised Translation of Programming Languages. In NeurIPS 2020
Baptiste Rozière, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020 · 2020
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Treegen: A tree-based transformer architecture for code generation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 8984–8991
Zeyu Sun, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, and Lu Zhang. 2020 · 2020
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Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, Online, 38–45
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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Unified Pre-training for Program Understanding and Generation. In Proceedings of NAACL-HLT 2021 . 2655–2668
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
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GraphCodeBERT: Pre-training Code Representations with Data Flow. In 9th International Conference on Learning Representations, ICLR 2021
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2021
Cited alongside, same era.
Measuring Coding Challenge Competence With APPS. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021 · 2021
Later among the works it cites.
Cotext: Multi-task learning with code-text transformer
Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, and Yanfang Ye. 2021 · 2021
Later among the works it cites.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
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Improving tree-structured decoder training for code generation via mutual learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 14121–14128
Binbin Xie, Jinsong Su, Yubin Ge, Xiang Li, Jianwei Cui, Junfeng Yao, and Bin Wang. 2021 · 2021
Later among the works it cites.
Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, and Stephan Günnemann. 2021 · 2021
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
Closest in time.
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 · 2022
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Multilingual code snippets training for program translation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 11783–11790
Ming Zhu, Karthik Suresh, and Chandan K Reddy. 2022 · 2022
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