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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.

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Research thesis

RStack improves the practical use of AI coding agents by wrapping them in a governed lifecycle:
The careful version of the thesis is:
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.

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Repo research artifacts

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Claims discipline

Every RStack paper or docs claim should have at least one of:
  1. measured evidence from RStack runs, tests, PRs, or CI,
  2. source evidence from standards, reports, or prior art,
  3. implementation evidence from code or docs,
  4. 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.

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Research-backed roadmap

The research program is tracked in GitHub issues:

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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.