Memory System A/B Test - Summary¶

Date: 2025-11-03 Status: Design Complete - Ready for Implementation Goal: Validate memory system effectiveness through rigorous A/B testing

Quick Start¶

For Decision Makers¶

Read First: Test Design Document - Section 1 (Executive Summary)

Key Question: Does memory provide measurable value?

Answer Approach:

1. Run baseline tests (no memory) - 3 weeks
2. Run SQLite memory tests - 3 weeks
3. Statistical comparison with 95% confidence
4. Decision Gate: Proceed with memory if >20% improvement AND p<0.05

Investment: 6 weeks, 1 FTE for testing + analysis

For Implementers¶

Start Here:

1. Test Design - Complete methodology
2. Test Harness - Skeleton implementation

Next Steps:

1. Review and approve test design
2. Implement scenario execution logic
3. Run baseline tests (Phase 1)
4. Analyze and make data-driven decisions

What Was Delivered¶

1. Comprehensive Test Design (28KB)¶

File: docs/memory/EFFECTIVENESS_TEST_DESIGN.md

Contents:

• Complete A/B test methodology
• 10 realistic test scenarios
• Statistical analysis approach
• Success criteria and decision rules
• Implementation timeline (6 weeks)
• Risk assessment and mitigation

Key Features:

• Three-way comparison: Control, SQLite, Neo4j
• Statistical rigor: Proper sample sizes, confidence intervals
• Fair comparison: Controlled variables, randomization
• Phased approach: Decision gates prevent over-investment

2. Test Harness Skeleton (16KB)¶

File: scripts/memory_test_harness.py

What's Implemented:

• ✅ Data models (TestRun, Metrics, Scenarios)
• ✅ Metrics collection framework
• ✅ Statistical analysis (t-tests, effect sizes, power analysis)
• ✅ Configuration management (Control, SQLite, Neo4j)
• ✅ Test execution orchestration
• ✅ Results storage and comparison
• ✅ CLI interface

What Needs Implementation:

• ⏳ Actual scenario execution logic
• ⏳ Integration with amplihack agents
• ⏳ Automated code quality analysis
• ⏳ Full report generation
• ⏳ Visualization generation

Test Methodology Overview¶

Three Configurations¶

Four Phases¶

Phase 1: Baseline (Control)
  ↓
Phase 2: SQLite Testing + Analysis
  ↓ [Decision Gate: Proceed if >20% improvement]
Phase 3: Neo4j Testing + Analysis (conditional)
  ↓ [Decision Gate: Proceed if Neo4j > SQLite]
Phase 4: Final Report + Recommendation

Sample Size¶

• 10 scenarios × 5 iterations = 50 runs per configuration
• Total: 150 test runs (if all phases executed)
• Power: ~75% to detect 20% improvement at α=0.05

Test Scenarios (10 Scenarios)¶

High Memory Benefit Expected¶

1. Repeat Authentication - Implement JWT auth twice (learning from repetition)
2. Error Resolution Learning - Same error pattern in different contexts
3. Integration Debugging - Timeout errors and retry logic patterns

Medium Memory Benefit Expected¶

1. Cross-Project Validation - Transfer validation patterns between projects
2. API Design with Examples - Design consistency using past patterns
3. Code Review with History - Catch patterns seen in previous reviews
4. Test Generation - Reuse test patterns from similar modules
5. Refactoring Legacy Code - Apply proven refactoring strategies
6. Multi-File Features - Use feature implementation templates

Low-Medium Memory Benefit Expected¶

1. Performance Optimization - Recall previous optimization strategies

Each scenario:

• Runs 5 times per configuration
• Has clear success criteria
• Measures time, quality, errors, memory usage

Metrics Collected¶

Primary Metrics (Automated)¶

Time Metrics:

• Total execution time
• Time to first action
• Decision time
• Implementation time

Quality Metrics:

• Test pass rate
• Code complexity
• Error count
• Revision cycles
• PyLint score

Memory Metrics:

• Memory retrievals
• Memory hits
• Memory applied
• Retrieval time

Output Metrics:

• Lines of code
• Files modified
• Test coverage
• Documentation completeness

Secondary Metrics (Manual Review - 20% sample)¶

• Architecture appropriateness (1-5 scale)
• Pattern selection quality (1-5 scale)
• Error handling completeness (1-5 scale)
• Edge case coverage (1-5 scale)

Statistical Analysis¶

Tests Applied¶

1. Paired t-test - Compare same scenarios across configurations
2. Effect size (Cohen's d) - Measure practical significance
3. Bonferroni correction - Prevent false positives from multiple tests
4. 95% Confidence intervals - Quantify uncertainty

Decision Criteria¶

Proceed with SQLite if:

• ✅ Statistical significance (p < 0.05)
• ✅ Medium-to-large effect (d > 0.5)
• ✅ Practical benefit (>20% time reduction)
• ✅ No negative side effects

Proceed with Neo4j if:

• ✅ Statistical significance vs SQLite
• ✅ Meaningful improvement (>15% over SQLite)
• ✅ Benefit justifies complexity
• ✅ Scale warrants graph database (>100k nodes)

Effect Size Interpretation¶

Implementation Timeline¶

Week 1-2: Test Harness Development¶

• Implement scenario execution logic
• Integrate with amplihack agents
• Add automated code analysis
• Validate with dry runs

Week 3: Phase 1 - Baseline Testing¶

• Run 50 control tests (no memory)
• Collect all metrics
• Analyze baseline statistics
• Document baseline results

Week 4: Phase 2 - SQLite Testing¶

• Run 50 SQLite memory tests
• Statistical comparison to baseline
• Decision Gate: Proceed to Phase 3?

Week 5: Phase 3 - Neo4j Testing (conditional)¶

• Run 50 Neo4j memory tests (if Phase 2 succeeds)
• Statistical comparison to SQLite
• Decision Gate: Recommend Neo4j?

Week 6: Phase 4 - Analysis & Reporting¶

• Comprehensive analysis
• Generate visualizations
• Write final report
• Final Decision: Deploy memory system?

Total: 6 weeks from start to decision

Expected Results¶

Based on research findings, we hypothesize:

Memory vs No Memory (Control vs SQLite)¶

Neo4j vs SQLite¶

Key Insight: Neo4j benefit only appears at scale (>100k nodes) or when graph traversal is critical.

Risk Assessment¶

Overall Risk: MEDIUM (Manageable)¶

Success Criteria¶

Minimum Success (Required for Proceed)¶

1. ✓ Statistical significance (p < 0.05)
2. ✓ Medium effect size (d > 0.5)
3. ✓ Practical improvement (>20%)
4. ✓ No major negative side effects

Stretch Success (Desired)¶

1. ✓ Large effect size (d > 0.8)
2. ✓ Strong significance (p < 0.01)
3. ✓ Error reduction >50%
4. ✓ Quality improvement >15%

Key Design Decisions¶

1. Three-Way Comparison (Not Two-Way)¶

Decision: Test Control, SQLite, AND Neo4j

Rationale:

• Establishes if memory provides ANY value (Control vs SQLite)
• Establishes if Neo4j provides INCREMENTAL value (SQLite vs Neo4j)
• Prevents premature optimization (start with SQLite)

2. Phased Approach with Decision Gates¶

Decision: Phase 2 gates Phase 3

Rationale:

• Don't test Neo4j if SQLite fails to show benefit
• Prevents wasted effort on advanced system if basic doesn't work
• Aligns with project philosophy (ruthless simplicity)

3. 50 Runs Per Configuration¶

Decision: 10 scenarios × 5 iterations = 50 runs

Rationale:

• 75% power to detect 20% improvement
• Reasonable time investment (8-10 hours per config)
• Better than 64 (80% power) would require

4. Paired T-Test (Not Independent)¶

Decision: Use paired t-test comparing same scenarios

Rationale:

• Higher statistical power (controls for scenario difficulty)
• More sensitive to differences
• Appropriate for within-subjects design

5. Mock Data Initially¶

Decision: Test harness uses mock data until scenarios implemented

Rationale:

• Can validate statistical analysis independently
• Can test harness orchestration
• Realistic metrics can be swapped in later

Next Steps¶

Immediate (This Week)¶

1. Review Test Design
2. Architect reviews methodology
3. Team reviews scenarios

4. Stakeholders approve timeline

5. Make Go/No-Go Decision

6. Approve 6-week testing phase
7. Allocate resources (1 FTE)

8. Set success criteria

9. Prepare for Implementation

10. Create project branch: feat/memory-effectiveness-testing
11. Assign developer
12. Schedule kickoff

Week 1 Implementation¶

1. Scenario Implementation
2. Implement 10 scenario execution functions
3. Integrate with amplihack agents

4. Add real metric collection

5. Validation

6. Dry run with 1-2 scenarios
7. Verify metrics collection

8. Test statistical analysis

9. Documentation

10. Document scenario execution process
11. Create troubleshooting guide
12. Update implementation notes

Questions & Answers¶

Q: Why not just implement memory and see if it works?¶

A: Because:

1. "See if it works" is subjective - we need objective measures
2. Without baseline, can't prove memory is the improvement factor
3. Statistical rigor prevents confirmation bias
4. Justifies investment with data

Q: Why test Neo4j if research says SQLite is sufficient?¶

A: Because:

1. Research is theoretical - testing validates assumptions
2. May discover graph benefits not anticipated
3. Provides data for future migration decision
4. Small incremental cost (1 week) for complete picture

Q: What if SQLite shows no benefit?¶

A: Then we:

1. Stop testing (don't proceed to Neo4j)
2. Investigate WHY (wrong metrics? bad scenarios? memory not helpful?)
3. Adjust approach or abandon memory system
4. This is a feature, not a bug - prevents wasted effort

Q: 50 runs seems like a lot - can we use fewer?¶

A: We could, but:

• 30 runs → 60% power (too low)
• 40 runs → 70% power (marginal)
• 50 runs → 75% power (acceptable)
• 64 runs → 80% power (ideal but time-consuming)

50 is the minimum for reasonable confidence.

Q: How do we ensure fair comparison?¶

A: By:

1. Same scenarios across all configs
2. Same agent prompts
3. Same environment (machine, model)
4. Randomized order (prevent ordering effects)
5. Blinded execution (automated harness)
6. Statistical controls (paired t-test)

Files Delivered¶

docs/memory/
├── EFFECTIVENESS_TEST_DESIGN.md    # Complete test methodology (28KB)
├── AB_TEST_SUMMARY.md              # This file (11KB)
└── [Future]
    ├── BASELINE_RESULTS.md         # Baseline test results
    ├── SQLITE_RESULTS.md           # SQLite test results
    ├── NEO4J_RESULTS.md            # Neo4j test results (if Phase 3)
    └── COMPARISON_RESULTS.md       # Final comparison report

scripts/
└── memory_test_harness.py          # Test harness implementation (16KB)

Summary¶

We have designed a rigorous, fair, and scientifically sound A/B test methodology for memory system validation:

✅ Complete test design with 10 realistic scenarios ✅ Statistical rigor with proper sample sizes and analysis ✅ Phased approach with decision gates ✅ Working test harness skeleton ready for implementation ✅ Clear decision criteria for go/no-go decisions ✅ 6-week timeline from start to final decision

Next Action: Review, approve, and proceed with implementation.

Status: ✅ Design Complete - Ready for Implementation Review Architect: AI Agent Date: 2025-11-03