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

arXiv: https://arxiv.org/abs/2604.20436 · HTML: https://mlsanity.com/paper/2604.20436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [A Survey of Vibe Coding with Large Language Models](https://mlsanity.com/paper/2510.12399/) (2025)
- [Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering](https://mlsanity.com/paper/2506.17937/) (2025)

## Abstract (arXiv)

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.