A Survey of Vibe Coding with Large Language Models
Authors: name: Yuyao Ge; name: Lingrui Mei; name: Zenghao Duan; name: Tianhao Li; name: Yujia Zheng; name: Yiwei Wang; name: Lexin Wang; name: Jiayu Yao; name: Tianyu Liu; name: Yujun Cai; name: Baolong Bi; name: Fangda Guo; name: Jiafeng Guo; name: Shenghua Liu; name: Xueqi Cheng
Published: 2025-10-14 11:26:56

Abstract: 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. Despite its transformative potential, the
effectiveness of this emergent paradigm remains under-explored, with empirical
evidence revealing unexpected productivity losses and fundamental challenges in
human-AI collaboration. To address this gap, this survey provides the first
comprehensive and systematic review of Vibe Coding with large language models,
establishing both theoretical foundations and practical frameworks for this
transformative development approach. Drawing from systematic analysis of over
1000 research papers, we survey the entire vibe coding ecosystem, examining
critical infrastructure components including LLMs for coding, LLM-based coding
agent, development environment of coding agent, and feedback mechanisms. We
first introduce Vibe Coding as a formal discipline by 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 theoretical foundation, we then synthesize existing
practices into five distinct development models: Unconstrained Automation,
Iterative Conversational Collaboration, Planning-Driven, Test-Driven, and
Context-Enhanced Models, thus providing the first comprehensive taxonomy in
this domain. Critically, our analysis reveals that successful Vibe Coding
depends not merely on agent capabilities but on systematic context engineering,
well-established development environments, and human-agent collaborative
development models.