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Code generation systems have been extensively developed in recent years to generate source code based on natural language instructions.
Latent Predictor Networks for Code Generation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 599–609
Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, Fumin Wang, and Andrew Senior. 2016 · 2016
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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 (Volume 1: Long Papers) . 1139–1149
Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017 · 2017
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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 (Volume 1: Long Papers) . 440–450
Pengcheng Yin and Graham Neubig. 2017 · 2017
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Metamorphic Testing for Machine Translations: MT4MT. In ASWEC . IEEE Computer Society, 96–100
Zhi Quan Zhou and Liqun Sun. 2018 · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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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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Measuring Coding Challenge Competence With APPS
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
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A Search-Based Testing Framework for Deep Neural Networks of Source Code Embedding. In ICST . IEEE, 36–46
Maryam Vahdat Pour, Zhuo Li, Lei Ma, and Hadi Hemmati. 2021 · 2021
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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In EMNLP (1) . Association for Computational Linguistics, 8696–8708
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
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GitHub Copilot Blog
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Cited alongside, same era.
Top languages used in 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 Chu Hong Hoi. 2022 · 2022
Cited alongside, same era.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
Cited alongside, same era.
Natural Test Generation for Precise Testing of Question Answering Software. In ASE . ACM, 71:1–71:12
Qingchao Shen, Junjie Chen, Jie M. Zhang, Haoyu Wang, Shuang Liu, and Menghan Tian. 2022 · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code. In MAPS@PLDI . ACM, 1–10
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
OpenAI ChatGPT
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InCoder: A Generative Model for Code Infilling and Synthesis. In ICLR . OpenReview.net
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023
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CCTEST: Testing and Repairing Code Completion Systems. In ICSE . IEEE, 1238–1250
Zongjie Li, Chaozheng Wang, Zhibo Liu, Haoxuan Wang, Dong Chen, Shuai Wang, and Cuiyun Gao. 2023 · 2023
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Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023 · 2023
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Cited alongside, same era.
Natural Attack for Pre-trained Models of Code. In ICSE . ACM, 1482–1493
Zhou Yang, Jieke Shi, Junda He, and David Lo. 2022 · 2022
Cited alongside, same era.
CERT: Continual Pre-training on Sketches for Library-oriented Code Generation. In The 2022 International Joint Conference on Artificial Intelligence
Daoguang Zan, Bei Chen, Dejian Yang, Zeqi Lin, Minsu Kim, Bei Guan, Yongji Wang, Weizhu Chen, and Jian-Guang Lou. 2022 · 2022
Cited alongside, same era.
Adversarial Robustness of Deep Code Comment Generation
Yu Zhou, Xiaoqing Zhang, Juanjuan Shen, Tingting Han, Taolue Chen, and Harald C. Gall. 2022 · 2022
Cited alongside, same era.
GitHub Copilot
Accessed: 2023a
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The language percentage distribution in GitHub
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Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021b
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On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot. In ICSE . IEEE, 2149–2160
Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, and Gabriele Bavota. 2023 · 2023
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CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. In ICLR . OpenReview.net
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023 · 2023
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Planning with large language models for code generation
Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B Tenenbaum, and Chuang Gan. 2023 · 2023
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