Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914498785312768 |
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| author | Lipsanen, Petrus Rannikko, Liisa Christophe, François Kalliokoski, Konsta Stirbu, Vlad Mikkonen, Tommi |
| author_facet | Lipsanen, Petrus Rannikko, Liisa Christophe, François Kalliokoski, Konsta Stirbu, Vlad Mikkonen, Tommi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings Lipsanen, Petrus Rannikko, Liisa Christophe, François Kalliokoski, Konsta Stirbu, Vlad Mikkonen, Tommi Software Engineering Artificial Intelligence 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. |
| title | Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2604.20436 |