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In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention.
Proceedings of the 34th Annual Meeting on Association for Computational Linguistics: Santa Cruz (California), June 24-27, 1996. Association for Computational Linguistics
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Unit Test Case Generation with Transformers and Focal Context
Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, and Neel Sundaresan. 2020 · 2009
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Mapping language to code in programmatic context
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Focus: A recommender system for mining api function calls and usage patterns. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 1050–1060
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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, et al · 2021
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Shubham Chandel, Colin B Clement, Guillermo Serrato, and Neel Sundaresan. 2022 · 2022
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Language models can teach themselves to program better
Patrick Haluptzok, Matthew Bowers, and Adam Tauman Kalai. 2022 · 2022
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Aixbench: A code generation benchmark dataset
Yiyang Hao, Ge Li, Yongqiang Liu, Xiaowei Miao, He Zong, Siyuan Jiang, Yang Liu, and He Wei. 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
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Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig. 2022b · 2022
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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
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MultiPL-E: a scalable and polyglot approach to benchmarking neural code generation
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Gemini: A multi-modal large language model
Google DeepMind. 2023 · 2023
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
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Codegen-test: An automatic code generation model integrating program test information. In 2023 2nd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE) . IEEE, 341–344
Maosheng Zhong, Zhixiang Wang, Gen Liu, Youde Chen, Huizhu Liu, and Ruping Wu. 2023 · 2023
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https://github.com/ComplexCodeEval/ComplexCodeEval
2024 · 2024
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GitHub Copilot
2024 · 2024
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Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering . 1–13
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CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang. 2023 · 2023
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Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation
Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2023 · 2023
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DS-1000: A natural and reliable benchmark for data science code generation. In International Conference on Machine Learning . PMLR, 18319–18345
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu. 2023 · 2023
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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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Wizardcoder: Empowering code large language models with evol-instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023 · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, 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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Magicoder: Source code is all you need
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang. 2023 · 2023
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Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Ge Li, Zhi Jin, Xiaoguang Mao, and Xiangke Liao. 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
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LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
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EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories
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Codereval: A benchmark of pragmatic code generation with generative pre-trained models. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering . 1–12
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