Our work provides the first comprehensive review of Vibe Coding with large language models. Through systematic analysis of 1000+ research papers, we survey the entire Vibe Coding ecosystem, examining critical infrastructure components including LLM architectures for code, coding agent capabilities, development environments, and feedback mechanisms.
The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous Coding Agents, enabling a novel development methodology termed "Vibe Coding" where developers validate AI-generated implementations through outcome observation rather than line-by-line code comprehension.
We introduce Vibe Coding as a formal discipline, formalizing it through a Constrained Markov Decision Process that captures the dynamic triadic relationship among human developers, software projects, and coding agents.
Building upon this formalization, we synthesize existing practices into five distinct development models: Unconstrained Automation, Iterative Conversational Collaboration, Planning-Driven, Test-Driven, and Context-Enhanced Models, providing the first comprehensive taxonomy in this domain.
- Awesome Vibe Coding
- 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, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, Oriol Vinyals,
- 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, Caiming Xiong,
- 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, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra,
- 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, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries,
- CodeT5+: Open code large language models for code understanding and generation, Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi,
- 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, Daxin Jiang,
- CodeGemma: Open code models based on Gemma, CodeGemma Team (Heri Zhao, Jeffrey Hui, Joshua Howland, Nam Nguyen, Siqi Zuo, Andrea Hu, Christopher A. Choquette-Choo, Jingyue Shen, Joe Kelley, Kshitij Bansal, Luke Vilnis, Mateo Wirth, Paul Michel, Peter Choy, Pratik Joshi, Ravin Kumar, Sarmad Hashmi, Shubham Agrawal, Zhitao Gong, Jane Fine, Tris Warkentin, Ale Jakse Hartman, Bin Ni, Kathy Korevec, Kelly Schaefer, Scott Huffman),
- Code Llama: Open foundation models for code, Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve,
- Magicoder: Empowering code generation with OSS-Instruct, Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, Lingming Zhang,
- 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, Y.K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang,
- 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, Edoardo 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, Han Hu, 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, Harm de Vries,
- DeepSeek-Coder-V2: Breaking the barrier of closed-source models in code intelligence, DeepSeek-AI (Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y. Wu, Yukun Li, Huazuo Gao, Shirong Ma, Wangding Zeng, Xiao Bi, Zihui Gu, Hanwei Xu, Damai Dai, Kai Dong, Liyue Zhang, Yishi Piao, Zhibin Gou, Zhenda Xie, Zhewen Hao, Bingxuan Wang, Junxiao Song, Deli Chen, Xin Xie, Kang Guan, Yuxiang You, Aixin Liu, Qiushi Du, Wenjun Gao, Xuan Lu, Qinyu Chen, Yaohui Wang, Chengqi Deng, Jiashi Li, Chenggang Zhao, Chong Ruan, Fuli Luo, Wenfeng Liang),
- Qwen2.5-Coder Technical Report, Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, Qiyao Peng, Yuqin Zhou, Zheng Zhao, Keming Lu, Xingzhang Ren, Yifu Chen, Junyang Lin,
- OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models, Siming Huang, Tianhao Cheng, Jason Klein Liu, Jiaran Hao, Liuyihan Song, Yang Xu, J. Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Zhaoxiang Zhang, Jie Fu, Qian Liu, Ge Zhang, Zili Wang, Yuan Qi, Yinghui Xu, Wei Chu,
- Codestral 25.01, Mistral AI,
- Code-R1: Reproducing R1 for Code with Reliable Rewards, Jiawei Liu, Lingming Zhang,
- Qwen3 Technical Report, Qwen Team,
- Kimi-Dev Technical Report, Kimi Team,
- CWM: An Open-Weights LLM for Research on Code Generation with World Models, Meta FAIR CodeGen Team,
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou
- AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation, Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, Jianguang Lou, Qingwei Lin, Ping Luo, Saravan Rajmohan
- ReAct: Synergizing Reasoning and Acting in Language Models, Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao
- Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints, Zhengdong Lu, Weikai Lu, Yiling Tao, Yun Dai, ZiXuan Chen, Huiping Zhuang, Cen Chen, Hao Peng, Ziqian Zeng
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face, Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang
- Large Language Models are Zero-Shot Reasoners, Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa
- Automatic Chain of Thought Prompting in Large Language Models, Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models, Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan
- HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking, Runquan Gui, Zhihai Wang, Jie Wang, Chi Ma, Huiling Zhen, Mingxuan Yuan, Jianye Hao, Defu Lian, Enhong Chen, Feng Wu
- CodePlan: Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning, Jiaxin Wen, Jian Guan, Hongning Wang, Wei Wu, Minlie Huang
- SQLucid: Grounding Natural Language Database Queries with Interactive Explanations, Yuan Tian, Jonathan K. Kummerfeld, Toby Jia-Jun Li, Tianyi Zhang
- Is a Question Decomposition Unit All We Need?, Pruthvi Patel, Swaroop Mishra, Mihir Parmar, Chitta Baral
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
- Multiple Memory Systems for Enhancing the Long-term Memory of Agent, Gaoke Zhang, Bo Wang, Yunlong Ma, Dongming Zhao, Zifei Yu
- A-MEM: Agentic Memory for LLM Agents, Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, Yongfeng Zhang
- MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents, Zeyu Zhang, Quanyu Dai, Xu Chen, Rui Li, Zhongyang Li, Zhenhua Dong
- TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation, Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu
- MemoryBank: Enhancing Large Language Models with Long-Term Memory, Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, Yanlin Wang
- Generative Agents: Interactive Simulacra of Human Behavior, Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein
- Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents, Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch
- RET-LLM: Towards a General Read-Write Memory for Large Language Models, Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, Hinrich Schütze
- Toolformer: Language Models Can Teach Themselves to Use Tools, Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom
- Large Knowledge Model: Perspectives and Challenges, Huajun Chen
- Small LLMs Are Weak Tool Learners: A Multi-LLM Agent, Weizhou Shen, Chenliang Li, Hongzhan Chen, Ming Yan, Xiaojun Quan, Hehong Chen, Ji Zhang, Fei Huang
- Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions, Xinyi Hou, Yanjie Zhao, Shenao Wang, Haoyu Wang
- MCP-Zero: Active Tool Discovery for Autonomous LLM Agents, Xiang Fei, Xiawu Zheng, Hao Feng
- Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents, Zhengliang Shi, Shen Gao, Lingyong Yan, Yue Feng, Xiuyi Chen, Zhumin Chen, Dawei Yin, Suzan Verberne, Zhaochun Ren
- OpenHands: An Open Platform for AI Software Developers as Generalist Agents, Xingyao Wang, Boxuan Li, Yufan Song, Frank F. Xu, Xiangru Tang, Mingchen Zhuge, Jiayi Pan, Yueqi Song, Bowen Li, Jaskirat Singh, Hoang H. Tran, Fuqiang Li, Ren Ma, Mingzhang Zheng, Bill Qian, Yanjun Shao, Niklas Muennighoff, Yizhe Zhang, Binyuan Hui, Junyang Lin, Robert Brennan, Hao Peng, Heng Ji, Graham Neubig
- CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges, Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, Zhi Jin
- Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection, Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
- ScaleMCP: Dynamic and Auto-Synchronizing Model Context Protocol Tools for LLM Agents, Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah, Pradeep Honaganahalli Basavaraju, James A. Burke
- Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation, Xinyi Ni, Haonan Jian, Qiuyang Wang, Vedanshi Chetan Shah, Pengyu Hong
- SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents, Jiaye Lin, Yifu Guo, Yuzhen Han, Sen Hu, Ziyi Ni, Licheng Wang, Mingguang Chen, Hongzhang Liu, Ronghao Chen, Yangfan He, Daxin Jiang, Binxing Jiao, Chen Hu, Huacan Wang
- AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation, Dong Huang, Jie M.Zhang, Michael Luck, Qingwen Bu, Yuhao Qing, Heming Cui
- Containerization in Cloud Computing: A Comprehensive Survey, Ferro, E., et al.,
- Virtual Environments for Code Generation: A Survey, Santoro, M., et al.,
- Kunerva: A Containerized Code Generation Platform, Lee, S., et al.,
- Fundamental Principles of Containerization, Lounissi, A., et al.,
- Container Security and Performance Analysis, Sultan, A., et al.,
- Cloud Container Orchestration: A Comprehensive Study, Pahl, C., et al.,
- Optimizing Container Performance in Distributed Systems, Romanov, A., et al.,
- CoCoS: A Container Orchestration System, Baresi, L., et al.,
- Performance Analysis of Containerized Applications, Amaral, M., et al.,
- Virtualization vs Containerization: A Comparative Study, Morabito, R., et al.,
- Containers in Cloud Computing: A Survey, Pahl, C., et al.,
- Confronting Reproducibility in AI Code Generation, Moulton, R., et al.,
- An introduction to Docker for reproducible research, Boettiger, C.,
- Virtual Environments for AI Development, Adamidi, A., et al.,
- Singularity: Scientific containers for mobility of compute, Kurtzer, G., et al.,
- Drive: A Docker-based Development Environment, Zhou, L., et al.,
- A Systematic Study of Container Security, Sarishma, K., et al.,
- Qualitative Analysis of Container Orchestration, Baresi, L., et al.,
- Towards Multi-language Container Support, Miller, J., et al.,
- Multi-platform Container Development, Cassano, F., et al.,
- Large-scale Container Orchestration for AI Systems, Su, Y., et al.,
- SWE: Secure Web Execution Environment, Jimenez, R., et al.,
- SandboxEval: Evaluating Sandbox Security, Rabin, M., et al.,
- AutoSafeCoder: Automated Safe Code Generation, Nunez, A., et al.,
- Secure Code Generation with LLMs, Gajbhiye, S., et al.,
- Securing AI-Generated Code, Bahadur, P., et al.,
- SourceFinder: Automated Source Code Analysis, Rokon, M., et al.,
- Your Code is Safe: LLM Security Framework, Liu, X., et al.,
- SALLM: Secure AI Language Model, Siddiq, M., et al.,
- NatiSand: Native Sandboxing for AI, Abbadini, G., et al.,
- PKRU: Memory Protection for Containers, Kirth, P., et al.,
- Virtuoso: WebAssembly for Secure Code Execution, Kanellopoulos, K., et al.,
- AgentSpec: Customizable Runtime Enforcement, Wang, L., et al.,
- Defining Cloud Computing: A Comprehensive Survey, Feilhauer, T., et al.,
- Cloud Computing: A Survey, Abolfazli, S., et al.,
- Cloud Computing: A Comprehensive Review, Bhm, C., et al.,
- Reasonable Scale: Large-scale AI Systems, Tagliabue, J., et al.,
- OpenAgent Safety: A Comprehensive Framework, Vijayvargiya, A., et al.,
- Tuning Large Language Models for Code Generation, Gong, H., et al.,
- ECCO: Efficient Code Compilation and Optimization, Waghjale, S., et al.,
- KubeIntellect: Kubernetes for AI Workloads, Ardebili, M., et al.,
- Attention is All You Need, Vaswani, A., et al.,
- BERT: Pre-training of Deep Bidirectional Transformers, Devlin, J., et al.,
- Language Models are Few-Shot Learners, Brown, T., et al.,
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, Raffel, C., et al.,
- Human-AI Collaboration in Software Development, Sergeyuk, A., et al.,
- IDE Integration for AI Code Generation, Sergeyuk, A., et al.,
- Towards Human-AI Collaborative Programming, Vaithilingam, P., et al.,
- Designing AI-Powered Development Tools, Robe, J., et al.,
- Programmer Experience with AI Code Generation, Ross, S., et al.,
- Significant Improvements in Developer Productivity, Weber, L., et al.,
- MultiMind: Multi-turn Reasoning for Code Generation, Donato, M., et al.,
- Using AI in Software Development: A Survey, Nam, J., et al.,
- Developer Experience with AI Tools, Pinto, G., et al.,
- GitHub Codespaces: Cloud-based Development, GitHub,
- JetBrains Gateway: Remote Development, JetBrains,
- Model Context Protocol: A Universal Interface, Hou, Y., et al.,
- A Survey of AI Development Protocols, Ray, S., et al.,
- Language Server Protocol: A Comprehensive Guide, Bork, F., et al.,
- Language Server Protocol in Practice, Gunasinghe, H., et al.,
- Deductive Debugging with DAP, Ernst, M., et al.,
- Cross-platform Debugging with DAP, Garcia, R., et al.,
- Glue: Connecting AI Tools with Development Environments, Li, W., et al.,
- Mind: AI-Powered Development Assistant, Koc, D., et al.,
- Git Integration for AI Development, Wu, X., et al.,
- MCP: Model Context Protocol, GitKraken,
- VS Agent: Visual Studio AI Integration, Microsoft,
- Microservices Architecture and CI/CD, Singh, R., et al.,
- Waterfall to Agile: A Historical Perspective, Dima, A., et al.,
- Agile software development ecosystems, Highsmith, J.,
- Extreme programming explained: embrace change, Beck, K.,
- Extended CI/CD with AI Integration, Ivanov, S., et al.,
- Continuous Integration in Cloud Environments, Bisicchia, P., et al.,
- ToSKer: TOSCA-based Cloud Orchestration, Brogi, A., et al.,
- TOSCA Data Management in Cloud, Dehury, C., et al.,
- Torch: TOSCA-based Cloud Migration, Tomarchio, O., et al.,
- FogArm: Autonomous Cloud Adaptation, Bisicchia, P., et al.,
- Architecting LLM-powered Systems, Joshi, N., et al.,
- Intelligent CI/CD with AI, Choi, H., et al.,
- LLM Operations: A Comprehensive Guide, Song, Y., et al.,
- AutoGen: Multi-Agent Conversation Framework, Wu, Q., et al.,
- AutoGen: A Comprehensive Survey, Zhu, L., et al.,
- AutoGen in Practice, Porsdam, H., et al.,
- CrewAI: Human-like Agent Collaboration, Taulli, T.,
- MetaGPT: Meta-Programming for GPT, Hong, W., et al.,
- MetaGPT: A Comprehensive Study, Zhou, S., et al.,
- Implementing LangGraph for Agent Collaboration, Chen, M., et al.,
- Code Generation with Multi-Agent Systems, Nasir, U., et al.,
- Multi-Agent Collaboration in Software Engineering, Williams, K., et al.,
- Edge Computing with Multi-Agent Systems, Zeng, Y., et al.,
- AI Collaboration in Distributed Systems, Luo, H., et al.,
- AI in Software Engineering: A Survey, Hughes, J., et al.,
- ConvCodeWorld: Conversational Code Generation in Virtual Worlds, Han, X., et al.,
- CriticLean: Critical Analysis for Code Generation, Peng, Y., et al.,
- Language Models are Few-Shot Learners, Brown, T., et al.,
- Self-Refine: Iterative Refinement with Self-Feedback, Madaan, A., et al.,
- Iterative Code Generation with Compiler Feedback, Bi, X., et al.,
- RepairAgent: Automated Code Repair with LLMs, Bouzenia, N., et al.,
- LLMLoop: Iterative Code Generation with Language Models, Ravi, S., et al.,
- FeedbackEval: Evaluating Feedback Mechanisms in Code Generation, Dai, W., et al.,
- CoderAgent: Tool-Integrated Code Generation, Zhan, H., et al.,
- RTLFixer: Automated RTL Error Repair, Tsai, C., et al.,
- A Review of Code Generation with LLMs, Li, Y., et al.,
- Evaluating Code Generation Systems, Siso, E., et al.,
- Compiler Feedback for Code Optimization, Grubisic, D., et al.,
- OpenEncoder: Optimized Code Generation, Huang, L., et al.,
- Augmenting Static Analysis with LLMs, Abtahi, F., et al.,
- Large Language Models for Code Analysis, Fang, Y., et al.,
- Combining Static Analysis with Machine Learning, Lacombe, G., et al.,
- Software Quality Assessment with Static Analysis, Srinivasarao, K., et al.,
- IRIS: Intelligent Static Analysis Framework, Li, M., et al.,
- LSAST: Large-Scale Static Analysis Tools, Keltek, A., et al.,
- CORE: Comprehensive Static Analysis, Wadhwa, S., et al.,
- An Effective Approach to Enhancing Compiler Error Messages, Becker, B.,
- SkipAnalyzer: Efficient Static Analysis, Mohajer, A., et al.,
- Isolating Code Issues with Static Analysis, Tu, H., et al.,
- Slither: Static Analysis for Ethereum Smart Contracts, Feist, J., et al.,
- QLPro: Advanced Static Analysis, Hu, X., et al.,
- LLM-Powered Static Analysis Systems, He, J., et al.,
- AutoSafeCoder: Multi-Agent Code Safety, Nunez, A., et al.,
- Training LLMs with Program Analysis Feedback, Yao, S., et al.,
- Helping Developers with Static Analysis, Dolcetti, G., et al.,
- Data-Driven Hardware Description Language Generation, Chang, M., et al.,
- Beyond Traditional HDL: HDLAgent, Kashanaki, H., et al.,
- CodeLUTra: Code Generation with LLMs, Tao, Y., et al.,
- Examining Code Generation Capabilities, Pearce, H., et al.,
- Test-Driven Code Generation, Mathews, J., et al.,
- VecTrans: Vectorized Code Translation, Zheng, L., et al.,
- CompilerGPT: LLM-Compiler Integration, Pirkelbauer, P., et al.,
- Redo: Iterative Code Refinement, Li, W., et al.,
- A Survey of Code Generation Feedback, Dong, Y., et al.,
- CodeRL: Reinforcement Learning for Code Generation, Le, H., et al.,
- A Survey of Reinforcement Learning for Code, Jiang, N., et al.,
- Execution-Based Code Generation, Shojaee, P., et al.,
- Coarse-Grained Feedback for Code Quality, Jain, N., et al.,
- Enhancing Code Generation with Feedback, Ashrafi, S., et al.,
- Cycle: Iterative Code Refinement, Ding, Y., et al.,
- AlphaTrans: Advanced Code Translation, Ibrahimzada, A., et al.,
- RLEF: Reinforcement Learning with Execution Feedback, Gehring, J., et al.,
- ACECoder: Advanced Code Generation, Zeng, Y., et al.,
- Viscoder: Visual Code Generation, Ni, A., et al.,
- InterCode: Interactive Code Generation, Yang, K., et al.,
- ART: Automated Program Repair Techniques, Ruiz, J., et al.,
- PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback, Yun Peng, Akhilesh Deepak Gotmare, Michael Lyu, Caiming Xiong, Silvio Savarese, Doyen Sahoo,
- You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects, Islem Bouzenia, Michael Pradel,
- LLM-based Unit Test Generation for Dynamically-Typed Programs, Runlin Liu, Zhe Zhang, Yunge Hu, Yuhang Lin, Xiang Gao, Hailong Sun,
- ProjectTest: A Project-level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms, Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing,
- Large Language Model Guided Self-Debugging Code Generation, Muntasir Adnan, Zhiwei Xu, Carlos C. N. Kuhn,
- Aster: Natural and multi-language unit
test generation with llms, Rangeet Pan, Myeongsoo Kim, Rahul Krishna, Raju Pavuluri, Saurabh Sinha,
- CodeT: Code generation with generated tests, Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen,
- Structured prompting and feedback-guided reasoning with LLMs for data interpretation, Amit Rath,
- From misuse to mastery: Enhancing code generation with knowledge-driven AI chaining, Xiaoxue Ren, Xinyuan Ye, Dehai Zhao, Zhenchang Xing, Xiaohu Yang,
- LLM-based unit test generation via property retrieval, Zhe Zhang, Xingyu Liu, Yuanzhang Lin, Xiang Gao, Hailong Sun, Yuan Yuan,
- Seeker: Enhancing exception handling in code with LLM-based multi-agent approach, Xuanming Zhang, Yuxuan Chen, Yuan Yuan, Minlie Huang,
- Automated unit test improvement using large language models at Meta , N. Alshahwan, Jubin Chheda, Anastasia Finogenova, Beliz Gokkaya, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, Eddy Wang,
- LLM as runtime error handler: A promising pathway to adaptive self-healing of software systems, Zhensu Sun, Haotian Zhu, Bowen Xu, Xiaoning Du, Li Li, David Lo,
- On the potential of agentic workflows for animal training plan generation, Jörg Schultz,
- CodeT: Code generation with generated tests, Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen,
- Structured prompting and feedback-guided reasoning with LLMs for data interpretation, Amit Rath,
- From misuse to mastery: Enhancing code generation with knowledge-driven AI chaining, Xiaoxue Ren, Xinyuan Ye, Dehai Zhao, Zhenchang Xing, Xiaohu Yang,
- LLM-based unit test generation via property retrieval, Zhe Zhang, Xingyu Liu, Yuanzhang Lin, Xiang Gao, Hailong Sun, Yuan Yuan,
- Seeker: Enhancing exception handling in code with LLM-based multi-agent approach, Xuanming Zhang, Yuxuan Chen, Yuan Yuan, Minlie Huang,
- Automated unit test improvement using large language models at Meta , N. Alshahwan, Jubin Chheda, Anastasia Finogenova, Beliz Gokkaya, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, Eddy Wang,
- LLM as runtime error handler: A promising pathway to adaptive self-healing of software systems, Zhensu Sun, Haotian Zhu, Bowen Xu, Xiaoning Du, Li Li, David Lo,
- On the potential of agentic workflows for animal training plan generation, Jörg Schultz,
- Grounded Copilot: How programmers interact with code-generating models, Shraddha Barke, Michael B. James, Nadia Polikarpova,
- Iterative resolution of prompt ambiguities using a progressive cutting-search approach, Fabrizio Marozzo,
- Deep Sets, M. Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alex Smola,
- MA-RLHF: Reinforcement learning from human feedback with macro actions, Yekun Chai, Haoran Sun, Huang Fang, Shuohuan Wang, Yu Sun, Hua Wu,
- ClarifyGPT: A framework for enhancing LLM-based code generation via requirements clarification, Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu, Binquan Zhang, ChenXue Wang, Shichao Liu, Qing Wang,
- PPO: Proximal Policy Optimization Algorithms, John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov,
- Post-training large language models via reinforcement learning from self-feedback, Carel van Niekerk, Renato Vukovic, Benjamin Matthias Ruppik, Hsien-chin Lin, Milica Gašić,
- History-aware cross-attention reinforcement: Self-supervised multi-turn and chain-of-thought fine-tuning with vLLM , Andrew Kiruluta, Andreas Lemos, Priscilla Burity,
- CRITIC: Large language models can self-correct with tool-interactive critiquing, Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, Weizhu Chen,
- BioAgents: Democratizing bioinformatics analysis with multi-agent systems, Nikita Mehandru, Amanda K. Hall, Olesya Melnichenko, Yulia Dubinina, Daniel Tsirulnikov, David Bamman, Ahmed Alaa, Scott Saponas, Venkat S. Malladi,
- Self-organized agents: A LLM multi-agent framework toward ultra large-scale code generation and optimization, Yoichi Ishibashi, Yoshimasa Nishimura,
- Multi-agent collaboration: Harnessing the power of intelligent LLM agents, Yashar Talebirad, Amirhossein Nadiri,
- LLM-Agent-UMF: LLM-based agent unified modeling framework for seamless integration of multi active/passive core-agents, Amine Ben Hassouna, Hana Chaari, Ines Belhaj,
- SWE-Search: Enhancing software agents with Monte Carlo Tree Search and iterative refinement, Antonis Antoniades, Albert Örwall, Kexun Zhang, Yuxi Xie, Anirudh Goyal, W. Wang,
- N-Critics: Self-refinement of large language models with ensemble of critics, Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan, Vineet Gundecha, Ashwin Ramesh Babu, Avisek Naug, Antonio Guillen-Perez, S. Sarkar,
- Self-review framework for enhancing instruction following capability of LLM, Sihyun Park,
- SAMULE: Self-learning agents enhanced by multi-level reflection, Yubin Ge, Salvatore Romeo, Jason Cai, Monica Sunkara, Yi Zhang,
- Reflexion: Language agents with verbal reinforcement learning, Noah Shinn, Federico Cassano, Beck Labash, A. Gopinath, Karthik Narasimhan, Shunyu Yao,
