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Code completion aims to enhance programming productivity by predicting potential code based on the current programming context.
A markovian decision process
Richard Bellman · 1957
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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A context sensitive code completion system for the c and c++ programming languages., 2005
Martin Stubenschrott · 2005
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Learning from examples to improve code completion systems
Marcel Bruch, Martin Monperrus, and Mira Mezini · 2009
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Mining source code repositories at massive scale using language modeling
Miltiadis Allamanis and Charles Sutton · 2013
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Towards improving statistical modeling of software engineering data: think locally, act globally!
Nicolas Bettenburg, Meiyappan Nagappan, and Ahmed E Hassan · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
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Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin Vechev · 2016
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An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio · 2016
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Neural code completion
Chang Liu, Xin Wang, Richard Shin, Joseph E Gonzalez, and Dawn Song · 2016
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Learning what and where to draw
Scott E Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, and Honglak Lee · 2016
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Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2018
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Improving automatic source code summarization via deep reinforcement learning
Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, and Philip S Yu · 2018
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Pythia: Ai-assisted code completion system
Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, and Neel Sundaresan · 2019
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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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Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
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Towards full-line code completion with neural language models
Wenhan Wang, Sijie Shen, Ge Li, and Zhi Jin · 2020
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Multi-task learning based pre-trained language model for code completion
Fang Liu, Ge Li, Yunfei Zhao, and Zhi Jin · 2020
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Codebleu: a method for automatic evaluation of code synthesis
Reacc: A retrieval-augmented code completion framework
Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy · 2022
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Codefill: Multi-token code completion by jointly learning from structure and naming sequences
Maliheh Izadi, Roberta Gismondi, and Georgios Gousios · 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
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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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Cctest: Testing and repairing code completion systems
Zongjie Li, Chaozheng Wang, Zhibo Liu, Haoxuan Wang, Shuai Wang, and Cuiyun Gao · 2022
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Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma · 2020
Cited alongside, same era.
Intellicode compose: Code generation using transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2020
Cited alongside, same era.
Reinforcement-learning-guided source code summarization using hierarchical attention
Wenhua Wang, Yuqun Zhang, Yulei Sui, Yao Wan, Zhou Zhao, Jian Wu, S Yu Philip, and Guandong Xu · 2020
Cited alongside, same era.
Code prediction by feeding trees to transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra · 2021
Cited alongside, same era.
Code completion by modeling flattened abstract syntax trees as graphs
Yanlin Wang and Hui Li · 2021
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Introducing github copilot: your ai pair programmer
Nat Friedman · 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 Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Cited alongside, same era.
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, et al · 2021
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Ai code generator—amazon codewhisperer, 2023
C Amazon · 2023
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Do not give away my secrets: Uncovering the privacy issue of neural code completion tools
Yizhan Huang, Yichen Li, Weibin Wu, Jianping Zhang, and Michael R Lyu · 2023
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https://www.darkreading.com/vulnerabilities-threats/samsung-engineers-sensitive-data-chatgpt-warnings-ai-use-workplace , 2023
samsung-engineers-sensitive-data-chatgpt-warnings-ai-use-workplace · 2023
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Execution-based code generation using deep reinforcement learning
Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K Reddy · 2023
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Don’t complete it! preventing unhelpful code completion for productive and sustainable neural code completion systems
Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, and Li Li · 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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Codet5+: Open code large language models for code understanding and generation
Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, and Steven C. H. Hoi · 2023
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Out of the bleu: how should we assess quality of the code generation models?
Mikhail Evtikhiev, Egor Bogomolov, Yaroslav Sokolov, and Timofey Bryksin · 2023
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Gpt-4 technical report
OpenAI OpenAI · 2023
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Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Stepcoder: Improve code generation with reinforcement learning from compiler feedback
Shihan Dou, Yan Liu, Haoxiang Jia, Limao Xiong, Enyu Zhou, Junjie Shan, Caishuang Huang, Wei Shen, Xiaoran Fan, Zhiheng Xi, et al · 2024
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