The science behind SHIFT
SHIFT is grounded in peer-reviewed research on how AI changes the way engineers think, learn, and build. Papers from the founding organizations are collected here as they are published.
CMM-AISE: A Cognitive Maturity Model for AI-Assisted Software Engineering
Argues that AI-assisted engineering creates a tension between cognitive scaffolding, which strengthens reasoning, and cognitive substitution, which bypasses it — and proposes a maturity model spanning capability dimensions, maturity levels, and stage-dependent risk across the SDLC.
Read the paperRe-Imagining Software Visualization for Spec-Driven Development
Specifications are now first-class artifacts, yet they are still handled as flat text — the same comprehension problem that motivated code visualization two decades ago. Proposes a five-level framework for visualizing SDD projects, from the structure of a single spec through to traceability into generated code and its coevolution over time.
Read the paperSpec-Graph Meta-Framework for AI-Assisted Software Development: An Industry Case
An industry case for representing system specifications as a typed graph, so that AI agents consume structured, traceable context instead of loose prose. Presented as a talk at the SHIFT Forum 2026. The paper is under review and will be posted here once a decision is in.