氛围建模的必要性：基于AI的可信软件开发中缺失的一环
The Case for Vibe Modeling: A Missing Step in AI-Based Trustworthy Software Development
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中文总结 AI 辅助
本文提出在AI辅助软件开发中引入氛围建模作为轻量中间抽象，通过学生调查验证其在提升LLM输出理解、降低验证工作量及增强信任方面的潜力，为可信AI软件工程研究提供方向。
中文摘要 AI 辅助
大型语言模型（LLMs）越来越多地被用于从自然语言提示生成软件制品，这虽能实现快速原型开发、降低软件创建门槛，但也带来了理解、验证、可追溯性及信任方面的挑战。本文认为，当前基于AI的开发实践过度聚焦于直接生成代码，却未充分关注能保留人类意图、支持系统行为推理的中间表示。我们主张将氛围建模（vibe modeling）作为自然语言交互与代码生成之间的轻量中间抽象。为探索其潜力，我们开展了一项学生调查研究，考察在多种AI辅助开发场景中，人们对LLM输出的理解、验证工作量、信任度及氛围建模感知有用性的看法。研究结果旨在为未来基于氛围建模的可信、可解释AI软件工程研究提供参考。
英文摘要
Large Language Models (LLMs) are increasingly used to generate software artifacts from natural language prompts. While this enables rapid prototyping and lowers the barrier to software creation, it also introduces challenges related to understanding, validation, traceability, and trust. In this paper, we argue that current AI-based development practices focus too heavily on the direct generation of code and insufficiently on intermediate representations that preserve human intent and support reasoning about system behavior. We argue for vibe modeling as a lightweight intermediate abstraction between natural language interaction and code generation. To explore its potential, we present a student survey study that examines perceptions of LLM output understanding, validation effort, trust and the perceived usefulness of vibe modeling across several AI-assisted development scenarios. Our results are intended to inform future studies for trustworthy and explainable AI-based software engineering via vibe modeling.
