# Requirements Traceability & Linking System - Research & Planning

## Executive Summary
Building a **spec-driven, graph-based requirements traceability system** for multi-language projects (TS/Py/Go/Rust) with smart contract support. System enables bidirectional linking: code ↔ user story ↔ test case ↔ wireframe ↔ epic ↔ technical specs.

## 1. Existing Solutions Analysis

### Enterprise Platforms
- **DOORS (IBM)**: Industry standard, complex, expensive, bidirectional traceability
- **Polarion (Siemens)**: Requirements management + ALM integration, strong versioning
- **Jira + Plugins**: Widespread, extensible via Zephyr/XStudio for test linking
- **Azure DevOps**: End-to-end traceability, integrated CI/CD

### Key Insights
- RTM (Requirements Traceability Matrix) is foundational but static
- Graph-based approaches (Neo4j) enable dynamic relationship queries
- Lightweight solutions (SQLite-based) preferred for local/embedded use
- Bidirectional linking critical for impact analysis

## 2. Spec-Driven Development Ecosystem

### GitHub Spec-Kit
- Hierarchical decomposition: Epic → Feature → Story → Task
- Markdown-based specs with AI integration
- Commands: `specify init`, `specify plan`, `specify expand`
- Strength: Simple, AI-friendly, version-controlled

### OpenSpec
- Lightweight spec framework for AI agents
- Supports change proposals and versioning
- Integrates with project architecture docs

## 3. Technology Stack Recommendations

### Backend Storage
- **Primary**: SQLite + graph schema (lightweight, portable, versioned)
- **Alternative**: Neo4j (complex queries, visualization-ready)
- **Versioning**: Temporal tables (bi-temporal: valid_from/valid_to)

### CLI Frameworks by Language
- **Python**: Typer + Rich (TUI) + Textual (full TUI)
- **Go**: Cobra + Bubble Tea (TUI framework)
- **Rust**: Clap + Ratatui (TUI)
- **TypeScript**: Commander + Ink (React-like TUI)

### Graph Representation
- Nodes: Epic, Feature, UserStory, Task, TestCase, CodeFile, Wireframe, TechnicalSpec
- Edges: decomposes_to, tests, implements, validates, references, blocks, depends_on
- Attributes: status, priority, owner, created_at, updated_at, version

## 4. Core Architecture

### Data Model
```
Requirement (base)
├── Epic (business/product level)
├── UserStory (user-facing)
├── TechnicalSpec (implementation)
├── TestCase (validation)
├── CodeArtifact (implementation)
└── UIArtifact (wireframe/design)

Traceability Links
├── Decomposition (parent → children)
├── Implementation (spec → code)
├── Validation (requirement → test)
└── Cross-domain (any → any)
```

### Versioning Strategy
- **Temporal versioning**: Track requirement evolution
- **Branching**: Support parallel requirement versions
- **Snapshots**: Capture product state at milestones

## 5. Integration Points

### Linter/Typechecker/Compiler
- Extract requirements from code comments (e.g., `@req:REQ-123`)
- Link test coverage to requirements
- Validate requirement completeness

### Smart Contracts
- Map contract functions to requirements
- Link test cases to contract methods
- Track audit trail via blockchain

### MCP (Model Context Protocol)
- Expose traceability as MCP tools for AI agents
- Enable AI-driven requirement decomposition
- Real-time requirement state queries

## 6. MVP Scope

### Phase 1: Core System
1. SQLite schema + graph model
2. Python CLI (Typer + Rich)
3. CRUD operations for requirements
4. Basic linking (decomposition, implementation)
5. Temporal versioning

### Phase 2: Integration
1. Code comment extraction
2. Test case linking
3. Export to RTM format
4. Visualization (graph queries)

### Phase 3: Multi-Language
1. Go/Rust/TS CLI wrappers
2. Language-specific extractors
3. Framework-specific adapters

## 7. Key Design Principles

- **Spec-first**: Requirements drive implementation
- **Graph-native**: Relationships as first-class citizens
- **Portable**: Works offline, version-controlled
- **Extensible**: Plugin architecture for domain-specific nodes
- **Traceable**: Full audit trail with temporal data
- **AI-ready**: MCP integration for agent automation

