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Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry.
Juice: A large scale distantly supervised dataset for open domain context-based code generation
Rajas Agashe, Srinivasan Iyer, and Luke Zettlemoyer. 2019 · 1910
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A view of 20th and 21st century software engineering
Barry Boehm. 2006 · 2006
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Antonio Valerio Miceli Barone and Rico Sennrich. 2017 · 2017
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code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2018 · 2018
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Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang. 2019 · 2019
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A neural model for generating natural language summaries of program subroutines
Alexander LeClair, Siyuan Jiang, and Collin McMillan. 2019 · 2019
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Nl2type: inferring javascript function types from natural language information
Rabee Sohail Malik, Jibesh Patra, and Michael Pradel. 2019 · 2019
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On learning meaningful assert statements for unit test cases
Cody Watson, Michele Tufano, Kevin Moran, Gabriele Bavota, and Denys Poshyvanyk. 2020 · 2020
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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 · 2021
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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 · 2021
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Codesc: A large code-description parallel dataset
Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md Mahim Anjum Haque, Tahmid Hasan, Wasi Uddin Ahmad, Anindya Iqbal, and Rifat Shahriyar. 2021 · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. 2021 · 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 · 2021
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Don’t make your llm an evaluation benchmark cheater
Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han. 2023 · 2021
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Multi-lingual evaluation of code generation models
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, et al. 2022 · 2022
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Multipl-e: A scalable and extensible approach to benchmarking neural code generation
Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al. 2022 · 2022
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Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
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Cocomic: Code completion by jointly modeling in-file and cross-file context
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang. 2022 · 2022
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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
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Jigsaw: Large language models meet program synthesis
Naman Jain, Skanda Vaidyanath, Arun Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram Rajamani, and Rahul Sharma. 2022 · 2022
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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 · 2022
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Can we generate shellcodes via natural language? an empirical study
Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, and Samira Shaikh. 2022 · 2022
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Type4py: Practical deep similarity learning-based type inference for python
Amir M Mir, Evaldas Latoškinas, Sebastian Proksch, and Georgios Gousios. 2022 · 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 · 2022
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Asleep at the keyboard? assessing the security of github copilot’s code contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2022 · 2022
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Methods2test: A dataset of focal methods mapped to test cases
Michele Tufano, Shao Kun Deng, Neel Sundaresan, and Alexey Svyatkovskiy. 2022 · 2022
Cited alongside, same era.
Natural language to code generation in interactive data science notebooks
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, et al. 2022 · 2022
Cited alongside, same era.
Parsel: A unified natural language framework for algorithmic reasoning
Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D Goodman, and Nick Haber. 2022 · 2022
Cited alongside, same era.
Codegen-test: An automatic code generation model integrating program test information
Maosheng Zhong, Gen Liu, Hongwei Li, Jiangling Kuang, Jinshan Zeng, and Mingwen Wang. 2022 · 2022
Cited alongside, same era.
Sql-palm: Improved large language modeladaptation for text-to-sql
Ruoxi Sun, Sercan O Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, and Tomas Pfister. 2023 · 2023
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Llmseceval: A dataset of natural language prompts for security evaluations
Catherine Tony, Markus Mutas, Nicolás E Díaz Ferreyra, and Riccardo Scandariato. 2023 · 2023
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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 · 2023
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” according to…” prompting language models improves quoting from pre-training data
Orion Weller, Marc Marone, Nathaniel Weir, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme. 2023 · 2023
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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 · 2023
Cited alongside, same era.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
A Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Time travel in llms: Tracing data contamination in large language models
Shahriar Golchin and Mihai Surdeanu. 2023 · 2023
Cited alongside, same era.
Competition-level problems are effective llm evaluators
Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, et al. 2023 · 2023
Cited alongside, same era.
Llm-assisted code cleaning for training accurate code generators
Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E Gonzalez, Koushik Sen, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Swe-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023 · 2023
Cited alongside, same era.
Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, Weishi Wang, Md Rizwan Parvez, and Shafiq Joty. 2023 · 2023
Cited alongside, same era.
Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Shuiguang Deng, et al. 2023 · 2023
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Rethinking benchmark and contamination for language models with rephrased samples
Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023
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Large language models as analogical reasoners
Michihiro Yasunaga, Xinyun Chen, Yujia Li, Panupong Pasupat, Jure Leskovec, Percy Liang, Ed H Chi, and Denny Zhou. 2023 · 2023
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No more manual tests? evaluating and improving chatgpt for unit test generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, and Xin Peng. 2023 · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al. 2023 · 2023
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Deepseek llm: Scaling open-source language models with longtermism
Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al. 2024 · 2024
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Cruxeval: A benchmark for code reasoning, understanding and execution
Alex Gu, Baptiste Rozière, Hugh Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida I Wang. 2024 · 2024
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Deepseek-coder: When the large language model meets programming–the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y Wu, YK Li, et al. 2024 · 2024
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Codemind: A framework to challenge large language models for code reasoning
Changshu Liu, Shizhuo Dylan Zhang, and Reyhaneh Jabbarvand. 2024 · 2024
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Starcoder 2 and the stack v2: The next generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Eduardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. 2024 · 2024
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Repoagent: An llm-powered open-source framework for repository-level code documentation generation
Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, et al. 2024 · 2024
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Proving test set contamination for black-box language models
Yonatan Oren, Nicole Meister, Niladri Chatterji, Faisal Ladhak, and Tatsunori B Hashimoto. 2024 · 2024
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ToolLLM: Facilitating large language models to master 16000+ real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, dahai li, Zhiyuan Liu, and Maosong Sun. 2024 · 2024
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Quantifying contamination in evaluating code generation capabilities of language models
Martin Riddell, Ansong Ni, and Arman Cohan. 2024 · 2024
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Code generation with alphacodium: From prompt engineering to flow engineering
Tal Ridnik, Dedy Kredo, and Itamar Friedman. 2024 · 2024
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To the cutoff… and beyond? a longitudinal perspective on LLM data contamination
Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White, and Samuel Dooley. 2024 · 2024
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An empirical evaluation of using large language models for automated unit test generation
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2024 · 2024
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Nofuneval: Funny how code lms falter on requirements beyond functional correctness
Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi, Nagarajan Natarajan, and Aditya Kanade. 2024 · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team. 2024 · 2024
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Opencodeinterpreter: Integrating code generation with execution and refinement
Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue. 2024 · 2024
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Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
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