---
title: "Research Program"
description: "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.

## Research thesis

RStack improves the practical use of AI coding agents by wrapping them in a governed lifecycle:

```text
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.

## Repo research artifacts

| Artifact | Purpose |
|---|---|
| [`research/current-state-audit.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/current-state-audit.md) | Detailed audit of where RStack stood before the research roadmap implementation. |
| [`research/bibliography.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/bibliography.md) | Standards, prior art, empirical research, and RStack primary-source references. |
| [`research/methodology.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/methodology.md) | How to study RStack and what metrics to collect. |
| [`research/prior-art-ai-sdlc-framework.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/prior-art-ai-sdlc-framework.md) | Comparison with `ai-sdlc-framework/ai-sdlc` and AI-SDLC reference architecture patterns. |
| [`research/productivity-claims.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/productivity-claims.md) | Claims register separating implemented facts, external evidence, hypotheses, and unsupported claims. |
| [`research/rstack-design-history.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/rstack-design-history.md) | Primary-source narrative of current implementation and design history. |
| [`research/paper-outline.md`](https://github.com/richard-devbot/SDLC-rstack/blob/main/research/paper-outline.md) | Draft paper structure and thesis. |

## 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.

<Warning>
  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.
</Warning>

## Research-backed roadmap

The research program is tracked in GitHub issues:

- [#70 Decision Queue and Definition-of-Ready gate](https://github.com/richard-devbot/SDLC-rstack/issues/70)
- [#71 RStack Spec v1alpha1](https://github.com/richard-devbot/SDLC-rstack/issues/71)
- [#72 Cross-harness builder/validator review independence](https://github.com/richard-devbot/SDLC-rstack/issues/72)
- [#73 RStack attestation envelopes](https://github.com/richard-devbot/SDLC-rstack/issues/73)
- [#74 Traceability drift detection](https://github.com/richard-devbot/SDLC-rstack/issues/74)
- [#75 Untrusted contributor PR gate](https://github.com/richard-devbot/SDLC-rstack/issues/75)
- [#76 RFC / Architecture Decision Record process](https://github.com/richard-devbot/SDLC-rstack/issues/76)
- [#77 Research bibliography and methodology appendix](https://github.com/richard-devbot/SDLC-rstack/issues/77)
- [#78 Governance packs and profile-based enforcement levels](https://github.com/richard-devbot/SDLC-rstack/issues/78)

## 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.