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.
Shift-Up：AI原生软件开发中软件工程护栏框架——初步发现 / Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings
该论文提出了一个名为Shift-Up的框架，通过将可执行需求、架构模型和架构决策记录等传统软件工程方法转化为结构化的“护栏”，来引导和约束AI（特别是生成式AI）在软件开发中的行为，从而避免代码质量下降、架构混乱等问题，让开发者能更多地专注于高层次的设计和验证工作。
源自 arXiv: 2604.20436
