用传统工程规范约束生成式AI，防止代码漂移。
Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings
- 将BDD、C4模型等工程实践转化为AI开发的结构化约束
- 使用可机器读取的需求和架构文档，减少实现偏差
- 适合希望提升AI生成代码可维护性的团队
生成式AI正推动软件工程从手动编码转向代理驱动实现。尽管‘氛围编码’能快速原型开发，但常导致架构漂移、可追溯性差和可维护性降低。本文基于设计科学研究方法，提出Shift-Up框架，将可执行需求（BDD）、架构建模（C4）和架构决策记录（ADRs）等成熟实践重新诠释为面向生成式AI开发的结构化护航机制。初步探索性评估比较了无结构氛围编码、结构化提示工程与Shift-Up方法在开发一个网页应用中的表现。结果表明，嵌入机器可读的需求与架构产物能够稳定代理行为，降低实现漂移，并将人类精力转向更高层的设计与验证工作。研究提示，传统软件工程成果可在AI辅助开发中发挥有效控制作用。
原文摘要 · Abstract (English)
Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation. While vibe coding promises rapid prototyping, it often suffers from architectural drift, limited traceability, and reduced maintainability. Applying the design science research (DSR) methodology, this paper proposes Shift-Up, a framework that reinterprets established software engineering practices, like executable requirements (BDD), architectural modeling (C4), and architecture decision records (ADRs), as structural guardrails for GenAI-native development. Preliminary findings from our exploratory evaluation compare unstructured vibe coding, structured prompt engineering, and the Shift-Up approach in the development of a web application. These findings indicate that embedding machine-readable requirements and architectural artifacts stabilizes agent behavior, reduces implementation drift, and shifts human effort toward higher-level design and validation activities. The results suggest that traditional software engineering artifacts can serve as effective control mechanisms in AI-assisted development.
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