Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings

• URL: http://arxiv.org/abs/2604.20436v1
• Date: Wed, 22 Apr 2026 10:55:57 GMT
• Title: Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings
• Abstract summary: Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation.<n>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.
• Score: 1.1523102135577732
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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