RStack is being developed as both a product and a research-backed AI-SDLC operating model. This page links the research artifacts that explain what RStack implements today, what it claims, what remains a hypothesis, and how future productivity claims should be measured.
Research thesis
RStack improves the practical use of AI coding agents by wrapping them in a governed lifecycle:
clarify → plan → spec → approve → build → validate → release-readiness → learn
RStack does not claim that AI coding alone guarantees productivity. RStack claims that AI-assisted delivery becomes more reliable and measurable when work is constrained by lifecycle stages, approvals, typed handoffs, evidence, budget envelopes, and Business Hub observability.
Repo research artifacts
| Artifact | Purpose |
|---|
research/current-state-audit.md | Detailed audit of where RStack stood before the research roadmap implementation. |
research/bibliography.md | Standards, prior art, empirical research, and RStack primary-source references. |
research/methodology.md | How to study RStack and what metrics to collect. |
research/prior-art-ai-sdlc-framework.md | Comparison with ai-sdlc-framework/ai-sdlc and AI-SDLC reference architecture patterns. |
research/productivity-claims.md | Claims register separating implemented facts, external evidence, hypotheses, and unsupported claims. |
research/rstack-design-history.md | Primary-source narrative of current implementation and design history. |
research/paper-outline.md | Draft paper structure and thesis. |
Claims discipline
Every RStack paper or docs claim should have at least one of:
- measured evidence from RStack runs, tests, PRs, or CI,
- source evidence from standards, reports, or prior art,
- implementation evidence from code or docs,
- a clear hypothesis label and experiment plan.
Do not claim quantified productivity gains until RStack has measured comparison runs. The current research-safe claim is that RStack creates the lifecycle and evidence structure needed to measure and improve AI-assisted delivery.
Research-backed roadmap
The research program is tracked in GitHub issues:
Suggested paper measurement model
RStack should measure productivity through multiple lenses:
- delivery flow: time to plan, build, validate, and release-readiness,
- quality: builder/validator pass rates, retries, risks, tests run,
- governance: approvals, blocked gates, policy overrides,
- traceability: requirement-to-task-to-evidence completeness,
- cost: estimated vs actual usage when host/provider data is available,
- operator burden: number of human interventions per run.
See research/methodology.md for the full measurement plan. 
