CVA Analysis - Discover Natural Abstractions

Commonality Variability Analysis (CVA) is a systematic technique for discovering abstractions from requirements. Instead of choosing patterns first, you build a matrix showing what's COMMON (constant across use cases) vs what VARIES (differs between cases). Patterns emerge naturally from the matrix structure.

Core Insight: Rows (commonalities) map to Strategy pattern. Columns (variabilities) map to Abstract Factory pattern. The matrix reveals whether abstraction is needed at all.

From CLAUDE.md Design Philosophy: "Greatest vulnerability is wrong or missing abstraction." CVA prevents wrong abstractions by making pattern selection evidence-based, not intuition-based.

Triggers

Activate CVA when you encounter:

• discover abstractions for [domain]
• run CVA analysis on [requirements]
• commonality variability analysis
• prevent wrong abstraction
• what patterns emerge from [use cases]

Quick Reference

Phase	Purpose	Input	Output	Time
1. Identify Commonalities	Find what's constant across ALL use cases	Use cases/requirements	List of commonalities (matrix rows)	5-10 min
2. Identify Variabilities	Find what VARIES between use cases	Commonalities + use cases	List of variabilities (matrix columns)	5-10 min
3. Build Matrix	Visualize commonality × variability relationships	Rows + columns	CVA matrix (Markdown table)	5-15 min
4. Map to Patterns	Translate matrix structure to design patterns	Completed matrix	Pattern recommendations + rationale	5-10 min
5. Validate & Handoff	Validate choices, route to appropriate agents	Pattern recommendations	Validation report + ADR stub	5-10 min

Total Time: Quick (15 min), Standard (30 min), Deep (60 min)

When to Use

Use CVA when:

• Facing 2+ similar but varying requirements
• About to select a design pattern (validate with CVA first)
• Refactoring code with duplicated logic
• Need evidence for abstraction decisions (ADR material)
• Team debates whether to abstract (make implicit explicit)

Do NOT use CVA when:

• Only 1 use case exists (wait for 2nd per YAGNI)
• Pattern is obvious and uncontested (don't over-analyze)
• Time-critical decision (<15 min available, use intuition)
• Requirements are still highly uncertain (premature)

Process

Phase 1: Identify Commonalities

Purpose: Establish what is ALWAYS true across ALL use cases. This becomes the stable foundation.

Steps:

1. Start with first use case: What does it need? List all capabilities.
2. Add second use case: What do BOTH need? Extract shared capabilities.
3. For each additional use case: What do ALL cases need?
4. Document as matrix ROWS: Each row is a commonality present in every use case.

Example (Payment Processing):

Use Case 1: Credit card payment
Use Case 2: PayPal payment
Use Case 3: Bank transfer

Commonalities (ALL cases need):
- Validate payment amount
- Authorize transaction
- Record transaction
- Handle transaction errors

Verification:

• Each commonality appears in ALL use cases
• At least 2 commonalities identified (if only 1, reconsider scope)
• Commonalities are capabilities/behaviors, not implementation details

Failure Handling: If no commonalities exist, use cases may be unrelated. Analyze separately or reconsider grouping.

---

Phase 2: Identify Variabilities

Purpose: Discover what VARIES between use cases. This reveals extension points where Strategy or Abstract Factory patterns may apply.

Steps:

1. For each commonality, ask: "How does this vary across cases?"
2. List variations: Document different implementations for each commonality.
3. Document as matrix COLUMNS: Each column is a variation.
4. Optional: Add "Future Variations" column for anticipated changes.

Example (continuing Payment Processing):

Commonality: Validate payment amount

Variations:
- Credit Card: Check card limit, validate CVV
- PayPal: Check PayPal balance, validate account status
- Bank Transfer: Check account balance, validate routing number

Commonality: Authorize transaction

Variations:
- Credit Card: Contact card issuer API
- PayPal: OAuth flow with PayPal
- Bank Transfer: ACH authorization

Verification:

• Each variability differs in at least 2 use cases
• Variations are concrete implementations, not abstract
• If ZERO variability (all cells identical), abstraction may not be needed

Failure Handling: If all variability (no commonality), design may be too broad. Narrow scope or reconsider.

---

Phase 3: Build CVA Matrix

Purpose: Create visual artifact showing commonality × variability relationships. Makes implicit design explicit for team discussion.

Steps:

1. Create Markdown table: Commonalities as rows, variabilities as columns
2. Fill each cell: Concrete implementation for that commonality/variability pair
3. Highlight patterns: Where cells are identical (sharing opportunity), where they differ (extension point)
4. Optional: Export to Mermaid diagram using scripts/export-cva-matrix.py

Example Matrix:

Commonality	Credit Card	PayPal	Bank Transfer
Validate amount	Check card limit, CVV	Check PayPal balance, account	Check account balance, routing
Authorize	Card issuer API	PayPal OAuth	ACH authorization
Record	Log to CardTransactionDB	Log to PayPalTransactionDB	Log to BankTransactionDB
Handle errors	Card decline codes	PayPal error codes	ACH rejection codes

Interpretation:

• Row perspective (Strategy pattern): "Authorize" varies across payment methods → Strategy pattern for authorization
• Column perspective (Abstract Factory pattern): Each payment method has consistent set of operations → Abstract Factory for payment providers
• Cell perspective: "Log to *TransactionDB" shows common pattern with variation → Template Method or Strategy

Verification:

• Matrix is at least 2×2 (minimum for pattern discovery)
• All cells are filled (no empty cells)
• Patterns becoming visible (some cells identical, some divergent)

Failure Handling: If matrix shows no useful patterns, abstraction may not be beneficial. Document rationale for concrete implementation.

---

Phase 4: Map to Patterns

Purpose: Translate matrix structure to design patterns. Patterns EMERGE from analysis, not imposed.

Steps:

1. Read ROWS (commonalities):

   • If each row has different implementations across columns → Strategy pattern (vary algorithm for same operation)
   • Example: "Authorize" row has 3 implementations → IAuthorizationStrategy

2. Read COLUMNS (variabilities):

   • If each column represents a coherent family of implementations → Abstract Factory pattern (vary implementations across product families)
   • Example: "Credit Card" column has consistent set of card-specific operations → CreditCardPaymentFactory

3. Check for multidimensional variability:

   • If BOTH rows AND columns vary independently → Combination patterns or reconsider scope
   • Start with dominant axis (more variance), note multidimensional case in Extension Points

4. Document recommendations:

   • Which pattern(s) fit?
   • WHY do they fit? (cite matrix structure)
   • What are alternatives? (if any)

Example Output:

## Pattern Recommendations

### Primary: Abstract Factory Pattern

**Rationale**: Each payment method (Credit Card, PayPal, Bank Transfer) requires a coherent family of related operations. Matrix columns show consistent product families.

**Implementation**:
```csharp
public interface IPaymentFactory {
    IAmountValidator CreateValidator();
    ITransactionAuthorizer CreateAuthorizer();
    ITransactionRecorder CreateRecorder();
    IErrorHandler CreateErrorHandler();
}

public class CreditCardPaymentFactory : IPaymentFactory {
    // Concrete implementations from "Credit Card" column
}

Alternative: Strategy pattern per row (4 separate strategies). Rejected because operations are not independent - they share payment method context. Factory keeps cohesion.

**Create ADR stub** for architect agent:

```markdown
# ADR-XXX: Payment Processing Abstraction

## Context
CVA matrix revealed 3 payment methods with 4 common operations, each varying by method.

## Decision
Use Abstract Factory pattern with `IPaymentFactory` per method.

## Rationale
- Matrix columns show coherent product families
- Operations share payment method context (not independent)
- New payment methods extend without modifying existing

## Alternatives Considered
- Strategy per operation: Rejected (operations not independent, loses cohesion)
- No abstraction: Rejected (3 methods with clear variations justify abstraction per YAGNI threshold)

Verification:

• Recommended patterns align with matrix structure
• Rationale explains WHY patterns fit (cites matrix evidence)
• Edge cases addressed (single use case → don't abstract, all variability → reconsider)
• ADR stub created with decision rationale

Failure Handling: If no patterns fit cleanly, document rationale for concrete implementation. Abstraction may not be warranted yet.

---

Phase 5: Validation and Handoff

Purpose: Validate abstraction choices and route to appropriate agents for review.

Steps:

1. Run validation script:

   python3 .claude/skills/cva-analysis/scripts/validate-cva-matrix.py cva-matrix.md
   

   • Checks: ≥2 rows, ≥2 columns, all cells filled, patterns suggested
   • Exit code 0 = pass, 10 = validation failure, 1 = error

2. Route to decision-critic (if abstraction recommended):

   # Use decision-critic skill to validate abstraction choice
   /decision-critic "Validate Abstract Factory pattern for payment processing per CVA analysis"
   

3. Route to architect agent (for ADR creation):

   # Hand off ADR stub to architect agent
   Task(subagent_type="architect", prompt="Create ADR from CVA analysis stub")
   

4. Document reassessment triggers:

   ## Reassessment Triggers
   
   Re-run CVA when:
   - 3+ new payment methods added
   - Major architectural shift (e.g., microservices split)
   - Performance issues with current abstraction
   - Team feedback: abstraction is too complex or not pulling weight
   

Outputs:

• Validation report (pass/fail with specific issues)
• Handoff to decision-critic and/or architect agent
• Reassessment triggers documented

Verification:

• Validation script exits with code 0
• Decision-critic review completed (if abstraction recommended)
• Reassessment triggers are specific, not vague

Failure Handling: If validation fails, return to earlier phase based on issue:

• Missing commonalities → Phase 1
• Incomplete matrix → Phase 3
• Pattern mismatch → Phase 4

---

Anti-Patterns

Anti-Pattern	Why Bad	Example	Instead
Pattern-First Design	Violates CVA principle: patterns emerge, not imposed. Leads to wrong abstractions.	Decide "we need Strategy pattern" BEFORE running CVA	Run CVA first. Let matrix reveal pattern. If no pattern emerges, abstraction may not be needed.
Forcing Abstractions	Creates complexity without benefit. Worse than no abstraction per CLAUDE.md.	CVA shows no variability, but create Strategy anyway "for future"	When CVA shows no variability, explicitly document decision NOT to abstract. Revisit when variability emerges.
Skipping Matrix Visualization	Loses pedagogical and collaborative benefits. Matrix makes assumptions explicit.	Identify commonalities/variabilities mentally, skip matrix, jump to pattern	Always create visual matrix (Markdown table minimum). Use for team review, ADR rationale.
Analysis Paralysis	CVA is a tool, not an end. Over-analysis wastes time.	Spend 3 hours on CVA for 2 simple use cases	Use tiered approach: Quick (15 min) for simple, Standard (30 min) moderate, Deep (60 min) architectural. Set timer.
Ignoring Temporal Dimension	Today's constant may be tomorrow's variable.	CVA assumes "currency" constant (USD only). 6 months later, need multi-currency.	Include "Future Variations" column for anticipated changes. Balance YAGNI with known roadmap.
Using CVA for Single Use Case	CVA requires 2+ cases to identify commonality/variability.	Run CVA on payment before any other transaction exists	Wait for 2nd use case (YAGNI), OR document anticipated cases with stakeholder validation.

Integration with Other Skills

architect Agent

CVA produces ADR stub with pattern rationale. Architect agent creates formal ADR.

Handoff Protocol:

Task(subagent_type="architect", prompt="Create ADR from CVA analysis at cva-payment-processing.md")

decision-critic Skill

Validates abstraction choices discovered via CVA. Applies inversion thinking to pattern recommendation.

Usage:

/decision-critic "Validate Abstract Factory pattern for payment processing per CVA matrix"

independent-thinker Agent

Challenges whether abstraction is needed at all. Use when CVA is borderline (minimal variability).

Usage:

Task(subagent_type="independent-thinker", prompt="Challenge whether payment abstraction is justified given only 2 current methods")

Verification Checklist

After completing CVA:

• Matrix has ≥2 rows (commonalities) and ≥2 columns (variabilities)
• All matrix cells are filled with concrete implementations
• Recommended patterns align with matrix structure (rows → Strategy, columns → Factory)
• Rationale explains WHY patterns fit (cites matrix as evidence)
• Edge cases handled (single use case, all variability, no commonality)
• Validation script passes: python3 scripts/validate-cva-matrix.py [file]
• ADR stub created with decision rationale
• Reassessment triggers documented (when to re-run CVA)

Reassessment Triggers

Re-run CVA when:

1. 3+ new use cases added (may reveal new patterns or invalidate current abstraction)
2. Major architectural shift (e.g., monolith → microservices)
3. Performance issues with current abstraction (overhead not justified)
4. Team feedback: Abstraction is too complex, not pulling weight, or forcing designs
5. Quarterly review (align with retrospective cycle)

Tiered Depth Levels

Choose depth based on decision impact:

Tier	Time	Use Cases	When to Use
Quick	15 min	2-3	Simple decisions, clear patterns, low risk
Standard	30 min	3-5	Moderate complexity, some uncertainty, medium risk
Deep	60 min	6+	Architectural decisions, high uncertainty, high risk

Time-boxing: Set timer, commit to best-available analysis when time expires. CVA is a tool, not a perfectionist exercise.

.NET Examples

See references/matrix-building-examples.md for complete .NET examples:

• ASP.NET Middleware Pipeline (commonality: process request, variability: authentication, logging, compression)
• Dependency Injection Providers (commonality: resolve dependencies, variability: lifetime scope, registration patterns)
• Data Access Patterns (commonality: CRUD operations, variability: SQL, NoSQL, in-memory)

References

• Pattern Mapping Guide - How to read matrix and map to Strategy, Abstract Factory, combinations
• Matrix Building Examples - .NET examples with complete CVA workflows
• Multidimensional CVA - Advanced: handling multiple axes of variability
• Coplien Multi-Paradigm Design - Academic foundation and further reading

Scripts

validate-cva-matrix.py

Check CVA matrix completeness and suggest patterns.

Usage:

python3 .claude/skills/cva-analysis/scripts/validate-cva-matrix.py cva-matrix.md

Exit Codes:

• 0: Valid matrix, patterns suggested
• 10: Validation failure (missing rows/columns, empty cells)
• 1: Error (file not found, invalid format)

generate-cva-template.py

Create empty matrix template from user input.

Usage:

python3 .claude/skills/cva-analysis/scripts/generate-cva-template.py \
  --commonalities "Validate,Authorize,Record,Handle errors" \
  --variabilities "CreditCard,PayPal,BankTransfer" \
  --output cva-matrix.md

export-cva-matrix.py

Convert Markdown table to Mermaid diagram for presentations.

Usage:

python3 .claude/skills/cva-analysis/scripts/export-cva-matrix.py \
  --input cva-matrix.md \
  --format mermaid \
  --output cva-diagram.mmd

Extension Points

1. Pattern Mapping: Add new pattern types beyond Strategy/Abstract Factory

   • Edit references/pattern-mapping-guide.md
   • Add Builder, Bridge, Visitor patterns with matrix signatures

2. Matrix Formats: Add export formats beyond Markdown

   • Use scripts/export-cva-matrix.py --format <json|plantuml|csv>

3. Multidimensional Analysis: Handle complex cases with 3+ axes

   • See references/multidimensional-cva.md

4. AI Integration: Automated pattern suggestion

   • Route to decision-critic or independent-thinker for automated review