Fetching the paper…
Reading the bibliography…
Code intelligence plays a key role in transforming modern software engineering.
Very-large scale code clone analysis and visualization of open source programs using distributed ccfinder: D-ccfinder
Simone Livieri, Yoshiki Higo, Makoto Matushita, and Katsuro Inoue · 2007
Earlier work this paper cites.
Rapid multi-purpose, multi-commit code analysis
Carol V Alexandru and Harald C Gall · 2015
Earlier work this paper cites.
Characterizing and detecting anti-patterns in the logging code
Boyuan Chen and Zhen Ming Jiang · 2017
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
Earlier work this paper cites.
Towards zero knowledge learning for cross language api mappings
Nghi Bui · 2019
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
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, and Ming Zhou · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
Earlier work this paper cites.
Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
Earlier work this paper cites.
Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi 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 Rush · 2020
Earlier work this paper cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi · 2021
Earlier work this paper cites.
Graphcodebert: Pre-training code representations with data flow
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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, 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
Earlier work this paper cites.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2021
Cited alongside, same era.
Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, and Burkhard Rost · 2021
Cited alongside, same era.
Multilingual training for software engineering
Toufique Ahmed and Premkumar Devanbu · 2022
Later among the works it cites.
Spt-code: sequence-to-sequence pre-training for learning source code representations
Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, and Bin Luo · 2022
Later among the works it cites.
Detect-localize-repair: A unified framework for learning to debug with codet5
Nghi DQ Bui, Yue Wang, and Steven Hoi · 2022
Later among the works it cites.
Hierarchynet: Learning to summarize source code with heterogeneous representations
Minh Huynh Nguyen, Nghi DQ Bui, Truong Son Hy, Long Tran-Thanh, and Tien N Nguyen · 2022
Later among the works it cites.
CODE-MVP: Learning to represent source code from multiple views with contrastive pre-training
Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, and Jin Liu · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, and Tie-Yan Liu · 2021
Cited alongside, same era.
Unified Pre-training for Program Understanding and Generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
Cited alongside, same era.
Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations
Nghi DQ Bui, Yijun Yu, and Lingxiao Jiang · 2021
Cited alongside, same era.
Infercode: Self-supervised learning of code representations by predicting subtrees
Nghi DQ Bui, Yijun Yu, and Lingxiao Jiang · 2021
Cited alongside, same era.
Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
Cited alongside, same era.
Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin · 2022
Cited alongside, same era.
CodeRL: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Hoi · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn · 2022
Cited alongside, same era.
Spt-code: Sequence-to-sequence pre-training for learning source code representations
Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, and Bin Luo · 2022
Later among the works it cites.
Natgen: generative pre-training by “naturalizing” source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Prem Devanbu, and Baishakhi Ray · 2022
Later among the works it cites.
Naturalcc: an open-source toolkit for code intelligence
Yao Wan, Yang He, Zhangqian Bi, Jianguo Zhang, Yulei Sui, Hongyu Zhang, Kazuma Hashimoto, Hai Jin, Guandong Xu, Caiming Xiong, et al · 2022
Later among the works it cites.
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 · 2023
Closest in time.
OpenAI · 2023
Closest in time.
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
Closest in time.
Codet5+: Open code large language models for code understanding and generation
Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi DQ Bui, Junnan Li, and Steven CH Hoi · 2023
Closest in time.
Santacoder: don’t reach for the stars!
Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, et al · 2023
Closest in time.
The vault: A comprehensive multilingual dataset for advancing code understanding and generation
Dung Nguyen, Le Nam, Anh Dau, Anh Nguyen, Khanh Nghiem, Jin Guo, and Nghi Bui · 2023
Closest in time.
Adaptive budget allocation for parameter-efficient fine-tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao · 2023
Closest in time.
Repocoder: Repository-level code completion through iterative retrieval and generation
Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen · 2023
Closest in time.
Better language models of code through self-improvement
Hung Q To, Nghi DQ Bui, Jin Guo, and Tien N Nguyen · 2023
Closest in time.
Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x, 2023
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, and Jie Tang · 2023
Closest in time.