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Code generation aims to produce code that fulfills requirements written in natural language automatically.
Language models are few-shot learners
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Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering
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Codebleu: a method for automatic evaluation of code synthesis
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Program Synthesis with Large Language Models
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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
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Evaluating large language models trained on code
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Leveraging Automated Unit Tests for Unsupervised Code Translation
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Codet: Code generation with generated tests
Chen et al · 2022
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Incoder: A generative model for code infilling and synthesis
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Le et al · 2022
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Competition-level code generation with alphacode
Li et al · 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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Generating sequences by learning to self-correct
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Teaching large language models to self-debug
Chen et al · 2023
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Palm: Scaling language modeling with pathways
Chowdhery et al · 2023
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Self-collaboration code generation via chatgpt
Dong et al · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023a · 2023
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Self-edit: Fault-aware code editor for code generation
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Zheng et al · 2023
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Mapcoder: Multi-agent code generation for competitive problem solving
Md Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez. 2024 · 2024
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Deveval: A manually-annotated code generation benchmark aligned with real-world code repositories. In Findings of the Association for Computational Linguistics: ACL 2024 . 3603–3614
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Metagpt: Meta programming for multi-agent collaborative framework
Hong et al · 2023
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Codecot and beyond: Learning to program and test like a developer
Huang et al · 2023
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Self-planning Code Generation with Large Language Models
Jiang et al · 2023
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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
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Le et al · 2023
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Starcoder: may the source be with you!
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Structured chain-of-thought prompting for code generation
Li et al · 2023
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, et al · 2024
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Reflexion: Language agents with verbal reinforcement learning
Shinn et al · 2024
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Swe-agent: Agent-computer interfaces enable automated software engineering
John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024 · 2024
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Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, and Zhi Jin. 2024 · 2024
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DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 12055–12065
Jizheng Chen, Kounianhua Du, Xinyi Dai, Weiming Zhang, Xihuai Wang, Yasheng Wang, Ruiming Tang, Weinan Zhang, and Yong Yu. 2025 · 2025
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Codescore: Evaluating code generation by learning code execution
Yihong Dong, Jiazheng Ding, Xue Jiang, Ge Li, Zhuo Li, and Zhi Jin. 2025 · 2025
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Codetree: Agent-guided tree search for code generation with large language models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) . 3711–3726
Jierui Li, Hung Le, Yingbo Zhou, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. 2025a · 2025
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Structured chain-of-thought prompting for code generation
Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2025b · 2025
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Adacoder: An adaptive planning and multi-agent framework for function-level code generation
Yueheng Zhu, Chao Liu, Xuan He, Xiaoxue Ren, Zhongxin Liu, Ruwei Pan, and Hongyu Zhang. 2025 · 2025
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Toward Executable Repository-Level Code Generation via Environment Alignment
Ruwei Pan, Junlei Shen, Linhao Wu, Yueheng Zhu, Zixiong Yang, Yakun Zhang, Lu Zhang, and Hongyu Zhang. 2026a · 2026
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Persistent Cross-Attempt State Optimization for Repository-Level Code Generation
Ruwei Pan, Jiangshuai Wang, Qisheng Zhang, Yueheng Zhu, Linhao Wu, Zixiong Yang, Yakun Zhang, Lu Zhang, and Hongyu Zhang. 2026b · 2026
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Ruwei Pan, Yakun Zhang, Qingyuan Liang, Yueheng Zhu, Chao Liu, Lu Zhang, and Hongyu Zhang. 2026c · 2026
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