在代码生成中迷失：重新构想软件模型在AI驱动软件工程中的作用
Lost in Code Generation: Reimagining the Role of Software Models in AI-driven Software Engineering
AI总结：
该研究针对AI生成软件存在的缺陷，提出从AI生成系统中恢复软件模型，区分智能体与人类反思循环，将软件模型作为连接现有软件工程与AI驱动开发的桥梁，拓展了软件模型的作用。
摘要：
生成式AI支持快速的“氛围编码”和智能体软件工程，自然语言提示可生成可用的软件系统，这降低了软件开发的门槛，但也模糊了原型与工程化软件之间的界限。看似完整的系统可能缺乏鲁棒性、安全性和可维护性。本文认为，这一转变赋予了软件模型新的作用：模型不再仅作为前期蓝图，还可从AI生成的系统中恢复，用于恢复理解并优化以指导后续迭代。本文区分了智能体循环（恢复的模型支持自动检查与修复）和人类反思循环（模型暴露待检查与修正的假设），确定了候选模型类型，并说明恢复的模型如何将隐含假设作为审查与验证的潜在约束，将软件模型定位为连接现有软件工程实践与AI驱动开发中基于模型推理的桥梁。
英文摘要：
Generative AI enables rapid "vibe coding" and agentic software engineering, where natural-language prompts yield working software systems. This lowers barriers to software creation, but it also collapses the boundary between prototypes and engineered software. Systems that appear complete may lack robustness, security, and maintainability. We argue that this shift motivates a renewed role for software models. Rather than serving only as upfront blueprints, models can be recovered from AI-generated systems, used to restore comprehension, and refined to guide subsequent evolution. We distinguish an agentic loop, in which recovered models support automated checking and repair, from a human reflective loop, in which models expose assumptions for inspection and revision. We identify candidate model types and illustrate how recovered models can expose implicit assumptions as possible constraints for review and validation. This paper positions software models as a bridge between current software engineering practice and model-based reasoning in AI-driven development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
