Research Program

Bibliography, methodology, claims discipline, and prior-art comparison for the RStack SDLC research paper.

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:

clarify → plan → spec → approve → build → validate → release-readiness → learn

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

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.

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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:

• #70 Decision Queue and Definition-of-Ready gate
• #71 RStack Spec v1alpha1
• #72 Cross-harness builder/validator review independence
• #73 RStack attestation envelopes
• #74 Traceability drift detection
• #75 Untrusted contributor PR gate
• #76 RFC / Architecture Decision Record process
• #77 Research bibliography and methodology appendix
• #78 Governance packs and profile-based enforcement levels

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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.