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.

翻译：大型语言模型（LLM）的进步推动了从代码生成辅助到自主编码代理的范式转变，催生了一种称为“氛围编码”的新型开发方法论。在该方法中，开发者通过观察结果而非逐行理解代码来验证人工智能生成的实现。尽管其具有变革潜力，这一新兴范式的有效性仍未得到充分探索，实证证据揭示了人机协作中意料之外的生产力损失和根本性挑战。为填补这一空白，本综述首次对基于大型语言模型的氛围编码进行了全面而系统的回顾，为这一变革性开发方法建立了理论基础和实践框架。通过对超过1000篇研究论文的系统分析，我们审视了整个氛围编码生态系统，考察了包括用于编码的LLM、基于LLM的编码代理、编码代理的开发环境以及反馈机制在内的关键基础设施组件。我们首先通过一个约束马尔可夫决策过程将氛围编码形式化，该过程捕捉了人类开发者、软件项目和编码代理之间的动态三元关系，从而将其确立为一门正式学科。基于这一理论基础，我们进一步将现有实践综合为五种不同的开发模型：无约束自动化、迭代式会话协作、规划驱动、测试驱动和上下文增强模型，从而提供了该领域的首个全面分类法。至关重要的是，我们的分析表明，成功的氛围编码不仅取决于代理的能力，更依赖于系统化的上下文工程、完善的开发环境以及人机协作的开发模型。