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.
license: MIT
---

# 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

```bash
# 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

```typescript
// 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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```yaml
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

```typescript
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

```typescript
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