# Epistemic Architecture Assessment

**Applying Empirica's Epistemic Framework to Code Architecture Decisions**

Date: 2025-12-28
Status: Design Proposal
Confidence: 0.85 | Uncertainty: 0.20

---

## Vision

**If AI agents benefit from epistemic self-assessment, why not code architecture?**

Empirica helps AI know what it knows. This same framework can help **maintainers** assess:
- Component coupling and stability
- Evolution likelihood and independence
- Modularity decisions (bundle vs separate)
- Integration strategies
- Breaking change risk
- Maintenance overhead

**Result:** Evidence-based architecture decisions, not gut feelings.

---

## Motivation

### The Problem

Architecture decisions are often made with **high uncertainty** but **low epistemic transparency**:

❌ "Should we extract this as a library?" → *Unknown coupling, guessed evolution*
❌ "Can we break this API?" → *Unclear stability contracts, unknown downstream impact*
❌ "Should we bundle or separate?" → *Intuition-based, no quantified tradeoffs*

### The Empirica Way

✅ **Measure coupling** (static analysis + runtime dependencies)
✅ **Estimate stability** (change frequency, semantic versioning, public API surface)
✅ **Predict evolution** (feature requests, roadmap, independent value)
✅ **Quantify uncertainty** (evidence quality, prediction confidence)
✅ **Track decisions** (record assessments, measure accuracy over time)

---

## Design: `empirica assess-component`

### Command Interface

```bash
# Assess a component (directory, module, plugin, service)
empirica assess-component --component plugins/claude-code-integration

# Compare two components
empirica assess-component --compare empirica-mcp,plugins/claude-code-integration

# Assess with specific question
empirica assess-component --component empirica/core/canonical \
  --question "Should we extract canonical to separate repo?"

# Output formats
empirica assess-component --component X --output json
empirica assess-component --component X --output markdown > assessment.md
empirica assess-component --component X --output interactive  # TUI
```

### Assessment Dimensions

#### 1. **Coupling Analysis**

**What it measures:**
- Import dependency graph (who imports this component?)
- Interface stability (public API surface area)
- Data contracts (schema dependencies)
- File system conventions (path dependencies)

**Metrics:**
- **Coupling score:** 0.0 (independent) → 1.0 (tightly coupled)
- **Dependency count:** Number of components that import this
- **API surface:** Public functions/classes exposed
- **Breaking change risk:** Estimated impact of API changes

**How:**
```python
from empirica.architecture.assessment import CouplingAnalyzer

analyzer = CouplingAnalyzer(component_path="plugins/claude-code-integration")
coupling = analyzer.analyze()

print(coupling.score)  # 0.70 (High)
print(coupling.dependencies)  # ['empirica.cli.project_commands', 'empirica.utils.session_resolver']
print(coupling.break_risk)  # 'MEDIUM'
```

#### 2. **Stability Estimation**

**What it measures:**
- Change frequency (git history analysis)
- Semantic versioning adherence
- Public API churn rate
- Test coverage and failure rate

**Metrics:**
- **Stability score:** 0.0 (volatile) → 1.0 (stable)
- **Change velocity:** Commits per month
- **API churn:** % of public API changed in last 6 months
- **Test confidence:** Coverage × success rate

**How:**
```python
from empirica.architecture.assessment import StabilityEstimator

estimator = StabilityEstimator(component_path="empirica/cli/project_commands.py")
stability = estimator.estimate()

print(stability.score)  # 0.85 (High)
print(stability.change_velocity)  # 2.5 commits/month
print(stability.api_churn)  # 0.15 (15% changed in 6mo)
```

#### 3. **Evolution Likelihood**

**What it predicts:**
- Independent evolution potential (can it evolve without core changes?)
- Feature request velocity (GitHub issues, roadmap)
- Cross-component coordination (does it need other components to improve?)

**Metrics:**
- **Independence score:** 0.0 (always coupled) → 1.0 (fully independent)
- **Evolution velocity:** Feature requests per month
- **Coordination overhead:** % of features requiring multi-component changes

**How:**
```python
from empirica.architecture.assessment import EvolutionPredictor

predictor = EvolutionPredictor(component_path="plugins/claude-code-integration")
evolution = predictor.predict()

print(evolution.independence)  # 0.20 (Low - requires core changes)
print(evolution.scenarios)  # [{'name': 'Richer snapshots', 'requires_core': True, 'probability': 0.35}, ...]
```

#### 4. **Maintenance Burden**

**What it estimates:**
- Version coordination overhead (if separated)
- Testing complexity (multi-version testing)
- Documentation maintenance
- User support burden (version mismatch issues)

**Metrics:**
- **Bundled cost:** Hours/month if bundled with core
- **Separated cost:** Hours/month if in separate repo
- **Cost delta:** Savings or overhead from separation

**How:**
```python
from empirica.architecture.assessment import MaintenanceBurdenEstimator

estimator = MaintenanceBurdenEstimator(component_path="plugins/claude-code-integration")
burden = estimator.estimate()

print(burden.bundled_cost)  # 1-2 hours/month
print(burden.separated_cost)  # 4-6 hours/month
print(burden.delta)  # +3 hours/month (separation overhead)
```

---

## Assessment Schema

### Input

```json
{
  "component": {
    "path": "plugins/claude-code-integration",
    "type": "plugin",
    "scope": "module"
  },
  "question": "Should this be in a separate repository?",
  "context": {
    "similar_components": ["empirica-mcp"],
    "constraints": ["must support Claude Code hook system"],
    "goals": ["maximize maintainability", "minimize user confusion"]
  }
}
```

### Output

```json
{
  "assessment_id": "uuid",
  "component": "plugins/claude-code-integration",
  "timestamp": "2025-12-28T15:00:00Z",

  "metrics": {
    "coupling": {
      "score": 0.70,
      "level": "HIGH",
      "dependencies": [
        {"component": "empirica.cli.project_commands", "type": "CLI subprocess"},
        {"component": "empirica.utils.session_resolver", "type": "Python import"}
      ],
      "break_risk": "MEDIUM",
      "confidence": 0.90
    },

    "stability": {
      "score": 0.85,
      "level": "HIGH",
      "change_velocity": 2.5,
      "api_churn": 0.10,
      "confidence": 0.80
    },

    "evolution": {
      "independence": 0.20,
      "level": "LOW",
      "scenarios": [
        {
          "name": "Richer snapshot data",
          "probability": 0.35,
          "requires_core": true,
          "impact": "HIGH"
        },
        {
          "name": "New hook types",
          "probability": 0.15,
          "requires_core": false,
          "impact": "LOW"
        }
      ],
      "confidence": 0.70
    },

    "maintenance": {
      "bundled_cost": 1.5,
      "separated_cost": 5.0,
      "delta": 3.5,
      "unit": "hours/month",
      "confidence": 0.80
    }
  },

  "recommendation": {
    "decision": "BUNDLE",
    "rationale": "High coupling (0.70) + low evolution independence (0.20) + maintenance savings (3.5h/month)",
    "alternatives": [
      {
        "option": "Separate repository",
        "pros": ["Clear separation", "Independent releases"],
        "cons": ["Version coordination overhead", "Testing complexity", "User confusion"],
        "score": 0.35
      },
      {
        "option": "Bundle with core",
        "pros": ["No version coordination", "Atomic updates", "User simplicity"],
        "cons": ["Larger core repo (minimal)"],
        "score": 0.85
      }
    ],
    "confidence": 0.80,
    "uncertainty": 0.25
  },

  "epistemic_state": {
    "evidence_quality": {
      "coupling_analysis": "HIGH (direct code inspection)",
      "stability_estimates": "MEDIUM (inferred from patterns)",
      "evolution_scenarios": "MEDIUM (speculative reasoning)",
      "maintenance_burden": "MEDIUM (experience-based)"
    },
    "uncertainty_sources": [
      "Future Empirica architecture changes (0.35 uncertainty)",
      "Claude Code hook evolution (0.30 uncertainty)",
      "Maintenance team capacity (0.40 uncertainty)"
    ],
    "what_would_change_my_mind": [
      "Empirica adds plugin versioning system",
      "Bootstrap schema formalized with versioning",
      "Multiple plugins emerge with similar patterns"
    ]
  },

  "comparison": {
    "similar_component": "empirica-mcp",
    "key_differences": {
      "coupling": "empirica-mcp: 0.30 (LOW) vs this: 0.70 (HIGH)",
      "evolution": "empirica-mcp: 0.70 (HIGH) vs this: 0.20 (LOW)",
      "audience": "empirica-mcp: broad MCP clients vs this: Empirica users only"
    },
    "separation_justification": {
      "empirica-mcp": "CORRECT (low coupling, high independence)",
      "this_component": "INCORRECT (high coupling, low independence)"
    }
  }
}
```

---

## Implementation Phases

### Phase 1: Core Assessment Framework (Week 1-2)

**Deliverables:**
- `empirica/architecture/assessment/` package
- `CouplingAnalyzer` class
- `StabilityEstimator` class
- `EvolutionPredictor` class
- `MaintenanceBurdenEstimator` class
- CLI command: `empirica assess-component`

**Files:**
```
empirica/architecture/
├── assessment/
│   ├── __init__.py
│   ├── coupling.py           # CouplingAnalyzer
│   ├── stability.py          # StabilityEstimator
│   ├── evolution.py          # EvolutionPredictor
│   ├── maintenance.py        # MaintenanceBurdenEstimator
│   ├── schema.py             # Assessment data models
│   └── recommender.py        # Decision recommendation engine
└── ...
```

### Phase 2: Git History Analysis (Week 3)

**Deliverables:**
- Git commit frequency analysis
- API churn detection (diff analysis)
- Change velocity metrics
- Test coverage integration

### Phase 3: Dependency Graph Analysis (Week 4)

**Deliverables:**
- Python import graph builder
- Cross-module dependency visualization
- Public API surface detection
- Breaking change impact prediction

### Phase 4: TUI Dashboard (Week 5-6)

**Deliverables:**
- Interactive assessment UI (Rich TUI)
- Component comparison view
- Decision recommendation matrix
- Historical assessment tracking

### Phase 5: Assessment Tracking (Week 7)

**Deliverables:**
- Save assessments to database
- Track decision accuracy over time
- "Was this recommendation correct?" follow-up
- Calibration improvement (similar to CASCADE)

---

## Usage Examples

### Example 1: Plugin Separation Decision

```bash
# Assess whether to separate a plugin
empirica assess-component --component plugins/claude-code-integration

# Output (simplified):
┌─ Assessment: plugins/claude-code-integration ─────────────────────────┐
│                                                                        │
│ 📊 Metrics                                                             │
│   Coupling:     ████████████░░░░░░ 0.70 (HIGH)                        │
│   Stability:    ██████████████░░░░ 0.85 (HIGH)                        │
│   Evolution:    ████░░░░░░░░░░░░░░ 0.20 (LOW)                         │
│   Maintenance:  +3.5 hours/month if separated                         │
│                                                                        │
│ ✅ Recommendation: BUNDLE with core                                   │
│                                                                        │
│ Rationale:                                                             │
│   • High coupling (0.70) requires synchronized updates                │
│   • Low evolution independence (0.20) - most improvements need core   │
│   • Maintenance overhead: 3.5 hours/month if separated                │
│   • User simplicity: install empirica → get plugin                    │
│                                                                        │
│ Confidence: 0.80 | Uncertainty: 0.25                                   │
│                                                                        │
│ Compare to: empirica-mcp                                               │
│   empirica-mcp CORRECTLY separated (low coupling, high independence)  │
│   This component should NOT separate (opposite profile)               │
│                                                                        │
└────────────────────────────────────────────────────────────────────────┘
```

### Example 2: API Breaking Change Decision

```bash
# Assess impact of breaking an API
empirica assess-component --component empirica/cli/project_commands.py \
  --question "Can we rename --trigger flag to --hook-type?"

# Output:
┌─ Breaking Change Impact Analysis ─────────────────────────────────────┐
│                                                                        │
│ 🔴 HIGH RISK CHANGE                                                   │
│                                                                        │
│ Downstream Dependencies:                                               │
│   • plugins/claude-code-integration (CRITICAL)                        │
│   • User automation scripts (UNKNOWN COUNT)                           │
│   • Documentation (3 files)                                           │
│                                                                        │
│ Estimated Impact:                                                      │
│   • Plugin breakage: 100% (requires immediate update)                 │
│   • User scripts: Unknown (no telemetry)                              │
│   • Migration effort: 4-6 hours                                       │
│                                                                        │
│ ✅ Recommendation: DON'T BREAK (or provide deprecation path)          │
│                                                                        │
│ Alternative:                                                           │
│   • Add --hook-type as alias                                          │
│   • Deprecate --trigger with warning                                  │
│   • Remove in v2.0 (major version bump)                               │
│                                                                        │
│ Confidence: 0.75 | Uncertainty: 0.30 (unknown downstream usage)       │
│                                                                        │
└────────────────────────────────────────────────────────────────────────┘
```

### Example 3: Feature Request Evaluation

```bash
# Assess where a feature should go
empirica assess-component --component empirica/core/canonical \
  --question "Should vector embedding be in core or plugin?"

# Output:
┌─ Feature Placement Assessment ────────────────────────────────────────┐
│                                                                        │
│ Feature: Vector embedding for semantic search                         │
│                                                                        │
│ Option 1: Core (empirica/core/canonical)                              │
│   Coupling:     ████░░░░░░░░░░░░░░ 0.25 (LOW)                         │
│   Pros:         • Already in vector search (Qdrant integration)       │
│   Cons:         • Increases core dependencies                         │
│   Score:        0.40                                                   │
│                                                                        │
│ Option 2: Plugin (empirica/plugins/vector-search)                     │
│   Coupling:     ██░░░░░░░░░░░░░░░░ 0.10 (VERY LOW)                    │
│   Pros:         • Optional dependency                                  │
│                 • Independent evolution                                │
│   Cons:         • Installation friction                               │
│   Score:        0.75                                                   │
│                                                                        │
│ ✅ Recommendation: PLUGIN                                             │
│                                                                        │
│ Rationale:                                                             │
│   • Low coupling allows separation                                    │
│   • Not all users need vector search                                  │
│   • Already factored in empirica[vector] optional dependency          │
│                                                                        │
│ Confidence: 0.85 | Uncertainty: 0.20                                   │
│                                                                        │
└────────────────────────────────────────────────────────────────────────┘
```

---

## Integration with Empirica Workflow

### 1. Architecture Decision Records (ADRs)

```bash
# Generate ADR from assessment
empirica assess-component --component X --output adr > docs/adr/0042-component-x-bundling.md

# ADR includes:
# - Context (what decision we're making)
# - Assessment metrics (coupling, stability, evolution)
# - Options considered (bundle vs separate)
# - Decision and rationale
# - Consequences (what changes, what risks)
# - Confidence and uncertainty
```

### 2. Pre-Commit Hooks

```bash
# In .git/hooks/pre-commit
empirica assess-component --component $CHANGED_COMPONENT --validate-stability

# Warns if:
# - Breaking change detected (API signature change)
# - High coupling increased (new dependencies added)
# - Stability decreased (test coverage dropped)
```

### 3. CI/CD Integration

```yaml
# In .github/workflows/assess-architecture.yml
- name: Assess Architecture Changes
  run: |
    empirica assess-component --component ${{ matrix.component }} \
      --compare-to main --output github-comment
```

### 4. Documentation Generation

```bash
# Auto-generate architecture docs
empirica assess-component --all --output docs > docs/architecture/COMPONENT_ASSESSMENT.md

# Includes:
# - Component dependency graph
# - Coupling matrix
# - Evolution roadmap
# - Maintenance burden breakdown
```

---

## Benefits

### For Maintainers

✅ **Evidence-based decisions** - Replace gut feelings with quantified metrics
✅ **Risk awareness** - Know breaking change impact before making it
✅ **Maintenance planning** - Predict effort for separation vs bundling
✅ **Historical tracking** - Learn from past decisions, improve calibration

### For Contributors

✅ **Clear architecture** - Understand component relationships
✅ **Contribution guidance** - Know where new features should go
✅ **Breaking change awareness** - Understand API stability contracts

### For Users

✅ **Predictable stability** - Know what changes to expect
✅ **Clear upgrade paths** - Understand version compatibility
✅ **Better documentation** - Auto-generated architecture guides

---

## Meta: Epistemic Assessment of This Proposal

**Confidence:** 0.85 (High)
**Uncertainty:** 0.20 (Low-Medium)

**Why confident:**
- ✅ Empirica already has epistemic framework (reuse existing concepts)
- ✅ Similar tools exist (dependency analyzers, coupling metrics)
- ✅ Clear value proposition (evidence-based architecture decisions)
- ✅ Concrete implementation path (phased approach)

**Why uncertain:**
- ⚠️ Complexity of accurate coupling measurement (dynamic imports, reflection)
- ⚠️ Evolution prediction accuracy (requires historical data)
- ⚠️ Maintenance burden estimation (subjective, experience-dependent)
- ⚠️ Adoption by maintainers (new tool, learning curve)

**What would increase confidence:**
- Prototype coupling analyzer (prove it's feasible)
- Validate with historical decisions (did past assessments predict reality?)
- User feedback (do maintainers find this useful?)

---

## Next Steps

### Immediate (This Week)
1. Validate concept with maintainers
2. Prototype coupling analyzer on empirica-integration plugin
3. Compare predictions to actual decision (bundle vs separate)

### Short-Term (1-2 Weeks)
1. Implement core assessment framework
2. CLI command: `empirica assess-component`
3. Test on 3-5 real components
4. Measure prediction accuracy

### Long-Term (1 Month+)
1. TUI dashboard for interactive assessment
2. Historical tracking and calibration
3. CI/CD integration
4. Auto-generated architecture docs

---

## Conclusion

**Empirica's epistemic framework shouldn't just apply to AI work—it should apply to Empirica itself.**

By turning the lens inward, we can:
- Make better architecture decisions
- Reduce technical debt
- Improve maintainability
- Build trust with users (transparent, evidence-based decisions)

**The assessment you just witnessed isn't a one-off analysis—it's a template for a systematic capability.**

Let's build it. 🚀
