qe-learning-optimization

name	qe-learning-optimization
description	Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.
trust_tier	3
validation	{"schema_path":"schemas/output.json","validator_path":"scripts/validate-config.json","eval_path":"evals/qe-learning-optimization.yaml"}

QE Learning Optimization

Purpose

Guide the use of v3's learning optimization capabilities including transfer learning between agents, hyperparameter tuning, A/B testing, and continuous performance improvement.

Activation

• When optimizing agent performance
• When transferring knowledge between agents
• When tuning learning parameters
• When running A/B tests
• When analyzing learning metrics

Quick Start

# Transfer knowledge between agents
aqe learn transfer --from jest-generator --to vitest-generator

# Tune hyperparameters
aqe learn tune --agent defect-predictor --metric accuracy

# Run A/B test
aqe learn ab-test --hypothesis "new-algorithm" --duration 7d

# View learning metrics
aqe learn metrics --agent test-generator --period 30d

Agent Workflow

// Transfer learning
Task("Transfer test patterns", `
  Transfer learned patterns from Jest test generator to Vitest:
  - Map framework-specific syntax
  - Adapt assertion styles
  - Preserve test structure patterns
  - Validate transfer accuracy
`, "qe-transfer-specialist")

// Metrics optimization
Task("Optimize prediction accuracy", `
  Tune defect-predictor agent:
  - Analyze current performance metrics
  - Run Bayesian hyperparameter search
  - Validate improvements on holdout set
  - Deploy if accuracy improves >5%
`, "qe-metrics-optimizer")

Learning Operations

1. Transfer Learning

await transferSpecialist.transfer({
  source: {
    agent: 'qe-jest-generator',
    knowledge: ['patterns', 'heuristics', 'optimizations']
  },
  target: {
    agent: 'qe-vitest-generator',
    adaptations: ['framework-syntax', 'api-differences']
  },
  strategy: 'fine-tuning',
  validation: {
    testSet: 'validation-samples',
    minAccuracy: 0.9
  }
});

2. Hyperparameter Tuning

await metricsOptimizer.tune({
  agent: 'defect-predictor',
  parameters: {
    learningRate: { min: 0.001, max: 0.1, type: 'log' },
    batchSize: { values: [16, 32, 64, 128] },
    patternThreshold: { min: 0.5, max: 0.95 }
  },
  optimization: {
    method: 'bayesian',
    objective: 'accuracy',
    trials: 50,
    parallelism: 4
  }
});

3. A/B Testing

await metricsOptimizer.abTest({
  hypothesis: 'ML pattern matching improves test quality',
  variants: {
    control: { algorithm: 'rule-based' },
    treatment: { algorithm: 'ml-enhanced' }
  },
  metrics: ['test-quality-score', 'generation-time'],
  traffic: {
    split: 50,
    minSampleSize: 1000
  },
  duration: '7d',
  significance: 0.05
});

4. Feedback Loop

await metricsOptimizer.feedbackLoop({
  agent: 'test-generator',
  feedback: {
    sources: ['user-corrections', 'test-results', 'code-reviews'],
    aggregation: 'weighted',
    frequency: 'real-time'
  },
  learning: {
    strategy: 'incremental',
    validationSplit: 0.2,
    earlyStoppingPatience: 5
  }
});

Learning Metrics Dashboard

interface LearningDashboard {
  agent: string;
  period: DateRange;
  performance: {
    current: MetricValues;
    trend: 'improving' | 'stable' | 'declining';
    percentile: number;
  };
  learning: {
    samplesProcessed: number;
    patternsLearned: number;
    improvementRate: number;
  };
  experiments: {
    active: Experiment[];
    completed: ExperimentResult[];
  };
  recommendations: {
    action: string;
    expectedImpact: number;
    confidence: number;
  }[];
}

Cross-Framework Transfer

transfer_mappings:
  jest_to_vitest:
    syntax:
      "describe": "describe"
      "it": "it"
      "expect": "expect"
      "jest.mock": "vi.mock"
      "jest.fn": "vi.fn"
    patterns:
      - mock-module
      - async-testing
      - snapshot-testing

  mocha_to_jest:
    syntax:
      "describe": "describe"
      "it": "it"
      "chai.expect": "expect"
      "sinon.stub": "jest.fn"
    adaptations:
      - assertion-style
      - hook-naming

Continuous Improvement

await learningOptimizer.continuousImprovement({
  agents: ['test-generator', 'coverage-analyzer', 'defect-predictor'],
  schedule: {
    metricCollection: 'hourly',
    tuning: 'weekly',
    majorUpdates: 'monthly'
  },
  thresholds: {
    degradationAlert: 5,  // percent
    improvementTarget: 2,  // percent per week
  },
  automation: {
    autoTune: true,
    autoRollback: true,
    requireApproval: ['major-changes']
  }
});

Pattern Learning

await patternLearner.learn({
  sources: {
    codeExamples: 'examples/**/*.ts',
    testExamples: 'tests/**/*.test.ts',
    userFeedback: 'feedback/*.json'
  },
  extraction: {
    syntacticPatterns: true,
    semanticPatterns: true,
    contextualPatterns: true
  },
  storage: {
    vectorDB: 'agentdb',
    versioning: true
  }
});

Coordination

Primary Agents: qe-transfer-specialist, qe-metrics-optimizer, qe-pattern-learner Coordinator: qe-learning-coordinator Related Skills: qe-test-generation, qe-defect-intelligence