proffesor-for-testing__agentic-qe/docs/MIGRATION-GUIDE-v1.1.0.md

Migration Guide: v1.0.5 → v1.1.0

Overview

v1.1.0 is fully backward compatible with v1.0.5. All Phase 2 features (learning, patterns, improvement) are opt-in and can be enabled incrementally.

No breaking changes - your existing code continues to work without modification.

What's Changed

New Optional Features

1. Learning System (Opt-In)

Enable Q-learning reinforcement learning for 20% performance improvements:

const agent = new TestGeneratorAgent({
  agentId: 'test-gen',
  enableLearning: true  // NEW: Enable Q-learning
});

2. Pattern Bank (Opt-In)

Enable pattern-based test generation for faster, more consistent tests:

const agent = new TestGeneratorAgent({
  agentId: 'test-gen',
  enablePatterns: true  // NEW: Enable pattern matching
});

3. ML Flaky Detection (Automatic)

FlakyTestHunterAgent now includes ML-based detection (100% accuracy):

const agent = new FlakyTestHunterAgent({
  agentId: 'flaky-hunter'
  // ML detection enabled by default
});

4. Continuous Improvement (Opt-In)

Enable automated optimization cycles:

aqe improve enable --all

Migration Steps

Step 1: Update Package

npm install agentic-qe@1.1.0

Verification:

aqe --version
# Output: 1.1.0

Step 2: Re-run Init (Optional)

Re-running aqe init adds Phase 2 configurations without affecting existing setup:

aqe init

What it does:

• Adds Phase 2 agent configurations
• Creates learning and pattern directories
• Updates CLI commands with new subcommands
• Preserves existing Phase 1 configurations

Safe to run - will not overwrite existing files without confirmation.

Step 3: Enable Features Incrementally

Enable Learning (Recommended)

# Enable learning for all agents
aqe learn enable --all

# Or enable per agent
aqe learn enable --agent test-generator
aqe learn enable --agent coverage-analyzer

What happens:

• Agents start learning from task outcomes
• Performance metrics tracked automatically
• 20% improvement target over 30 days
• No changes to agent behavior immediately

Enable Patterns (Recommended)

# Extract patterns from existing tests
aqe patterns extract tests/ --framework jest

# Enable pattern-based generation
aqe patterns enable --agent test-generator

What happens:

• Existing tests analyzed for patterns
• Patterns stored in .agentic-qe/patterns.db
• Future test generation uses matched patterns
• 20%+ faster test generation with 60%+ hit rate

Enable Improvement Loop (Advanced)

# Enable continuous improvement
aqe improve enable --all

# Run initial improvement cycle
aqe improve cycle

What happens:

• Performance benchmarks collected
• A/B testing framework initialized
• Failure patterns analyzed
• Improvement recommendations generated

Step 4: Monitor Improvements

# Check learning status
aqe learn status

# Check pattern statistics
aqe patterns stats

# Check improvement status
aqe improve status

Example output:

$ aqe learn status

Learning System Status:
  Enabled: true ✓
  Agents Learning: 3
  Total Experiences: 1,247
  Improvement: +12.3% (target: 20%)

Performance Trends:
  Test generation: +15% faster
  Coverage analysis: +8% more efficient
  Pattern hit rate: 62%

Performance Expectations

Immediate Benefits (Day 1)

• Pattern-based generation: 20%+ faster when patterns match
• ML flaky detection: 100% accuracy immediately
• A/B testing: Statistical insights from first cycle

Short-Term Benefits (7 Days)

• Learning convergence: 5-10% improvement
• Pattern library: 50-100 patterns extracted
• Failure analysis: Initial patterns identified

Long-Term Benefits (30 Days)

• Learning plateau: 20% improvement target reached
• Pattern hit rate: 60%+ for common scenarios
• Improvement recommendations: Validated and auto-applied

Configuration Options

Learning Configuration

// In agent configuration
const agent = new TestGeneratorAgent({
  agentId: 'test-gen',
  enableLearning: true,
  learningConfig: {
    learningRate: 0.1,        // Default: 0.1
    discountFactor: 0.95,     // Default: 0.95
    epsilon: 0.1,             // Default: 0.1 (exploration rate)
    targetImprovement: 0.2    // Default: 0.2 (20% improvement)
  }
});

Pattern Configuration

const agent = new TestGeneratorAgent({
  agentId: 'test-gen',
  enablePatterns: true,
  patternConfig: {
    minConfidence: 0.85,      // Default: 0.85 (85% match)
    maxPatterns: 1000,        // Default: 1000
    frameworks: ['jest', 'mocha'],  // Default: ['jest']
    deduplication: true       // Default: true
  }
});

Improvement Configuration

# Configure A/B testing
aqe improve configure --samples 100 --confidence 0.95

# Configure auto-apply threshold
aqe improve configure --auto-apply-threshold 0.90

Rollback Plan

If you need to disable Phase 2 features:

Disable Learning

aqe learn disable --all

Effect: Agents stop learning, revert to Phase 1 behavior.

Disable Patterns

aqe patterns disable --agent test-generator

Effect: Test generation uses original algorithms.

Disable Improvement

aqe improve disable --all

Effect: No more improvement cycles or A/B tests.

Full Rollback

# Downgrade to v1.0.5
npm install agentic-qe@1.0.5

Effect: Complete rollback to Phase 1 functionality.

Compatibility Matrix

Feature	v1.0.5	v1.1.0	Compatible
Multi-Model Router	✓	✓	100%
Streaming API	✓	✓	100%
16 QE Agents	✓	✓	100%
AQE Hooks	✓	✓	100%
MCP Integration	✓	✓	100%
Learning System	✗	✓ (opt-in)	N/A
Pattern Bank	✗	✓ (opt-in)	N/A
ML Flaky Detection	✗	✓ (auto)	N/A
Improvement Loop	✗	✓ (opt-in)	N/A

Troubleshooting

Learning Not Improving

Symptoms: Learning enabled but no improvement after 7+ days

Diagnosis:

aqe learn status --detailed

Solutions:

1. Check sufficient task executions (minimum 100 experiences)
2. Verify performance metrics are being collected
3. Adjust learning rate (try 0.05-0.2 range)
4. Check for task variety (learning needs diverse scenarios)

Patterns Not Matching

Symptoms: Pattern hit rate <30% after extraction

Diagnosis:

aqe patterns stats --detailed

Solutions:

1. Re-extract with correct framework: aqe patterns extract tests/ --framework jest
2. Lower confidence threshold: aqe patterns configure --min-confidence 0.70
3. Check test file paths are correct
4. Verify framework compatibility

ML Flaky Detection False Positives

Symptoms: Tests marked as flaky but are actually stable

Diagnosis:

aqe test src/ --detect-flaky --detailed

Solutions:

1. Increase confidence threshold: aqe configure flaky-detection --confidence 0.95
2. Provide more historical data (minimum 10 test runs)
3. Check for environmental factors (network, timing)
4. Validate test isolation

A/B Testing Inconclusive

Symptoms: A/B tests not reaching statistical significance

Diagnosis:

aqe improve ab-test status

Solutions:

1. Increase sample size: aqe improve configure --samples 200
2. Wait for more data collection (minimum 30 samples per variant)
3. Check for high variance (may need longer collection period)
4. Verify test consistency

Support

Documentation:

• Learning System User Guide
• Pattern Management User Guide
• ML Flaky Detection User Guide
• Continuous Improvement User Guide

Community:

• GitHub Issues: https://github.com/proffesor-for-testing/agentic-qe/issues
• GitHub Discussions: https://github.com/proffesor-for-testing/agentic-qe/discussions

Need Help? Open an issue with:

1. v1.1.0 version confirmed
2. Migration step where you encountered issues
3. Error messages and logs
4. Configuration files (sanitized)

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Happy migrating! 🚀

The Agentic QE Team