AI 中文总结
该研究对2022年至2025年10月的文献进行多视角综述，探讨软件开发中氛围编码实践。通过分析47篇文献发现其是迭代流程，开发者工作转变，短期生产力有提升，不同应用场景证据强弱有别，此为整合两类文献的首次综述并提出未来研究方向。
AI 中文摘要
氛围编码是一种软件开发实践，开发者用自然语言陈述意图，大语言模型生成代码。通常被视为一次性提示，但实际是意图驱动的迭代工作流程，结果取决于对生成代码的评估和管理。相关知识分散，现有综述未整合学术与实践证据。我们按既定指南对同行评审和灰色文献进行多视角综述，搜索2022年至2025年10月的文献。经筛选、可信度评估和滚雪球抽样，保留47篇文献并通过描述性映射和主题综合分析八个研究问题。氛围编码是迭代循环，开发者工作转向规范、监督和验证。47篇文献中有21篇（45%）报告了短期生产力和原型制作时间的收益，而关于可维护性、长期质量和保障有效性的证据有限。在原型制作和用户界面工作方面证据最强，在生产、数据密集型和安全关键型应用方面最弱，工具可见性不意味着有效性。这是首次按单一记录协议整合同行评审和灰色文献对氛围编码进行的综述。未来工作应评估保障有效性，研究会话级动态和长期可维护性，并在生产、数据密集型和安全关键型环境中测试氛围编码。
英文摘要
Vibe coding is a software development practice in which developers state intent in natural language and large language models generate code. It is often framed as one-shot prompting, but the evidence describes an intent-driven, iterative workflow whose outcomes depend on how generated code is evaluated and governed. Knowledge of how vibe coding is defined, practiced, and governed is scattered across academic and practitioner sources, and, to our knowledge, existing reviews have not yet integrated both evidence streams. We conducted a multivocal literature review of peer-reviewed and grey literature following established guidelines. Searches spanned 2022 to October 2025. After screening, credibility assessment, and snowballing, 47 sources were retained (28 peer-reviewed and 19 grey) and analyzed through descriptive mapping and thematic synthesis across eight research questions. Vibe coding is consistently described as an iterative generation-evaluation-revision loop rather than a one-shot activity, and developer work shifts from writing code towards specification, supervision, and validation. Short-term productivity and time-to-prototype gains are reported in 21 of 47 sources (45%), while evidence on maintainability, long-term quality, and safeguard effectiveness remains limited. Evidence is strongest for prototyping and user-interface work and weakest for production, data-intensive, and safety-critical use, and tool visibility does not imply effectiveness. This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol. Future work should evaluate safeguard effectiveness, study session-level dynamics and long-term maintainability, and test vibe coding in production, data-intensive, and safety-critical settings.
