QE学习优化Skill "QELearningOptimization"

---

name: “QE学习优化” description: “AI驱动的QE代理的转移学习、指标优化和持续改进。” trust_tier: 3 validation: schema_path: schemas/output.json validator_path: scripts/validate-config.json eval_path: evals/qe-learning-optimization.yaml

---

QE学习优化

目的

指导v3学习优化能力的使用，包括代理之间的转移学习、超参数调优、A/B测试和持续性能改进。

激活

• 当优化代理性能时
• 当在代理之间转移知识时
• 当调整学习参数时
• 当运行A/B测试时
• 当分析学习指标时

快速开始

# 在代理之间转移知识
aqe learn transfer --from jest-generator --to vitest-generator

# 调优超参数
aqe learn tune --agent defect-predictor --metric accuracy

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

# 查看学习指标
aqe learn metrics --agent test-generator --period 30d

代理工作流

// 转移学习
Task("转移测试模式", `
  从Jest测试生成器转移到Vitest的学习模式：
  - 映射框架特定语法
  - 适应断言风格
  - 保留测试结构模式
  - 验证转移准确性
`, "qe-transfer-specialist")

// 指标优化
Task("优化预测准确性", `
  调优缺陷预测代理：
  - 分析当前性能指标
  - 运行贝叶斯超参数搜索
  - 在保留集上验证改进
  - 如果准确性提高>5%，则部署
`, "qe-metrics-optimizer")

学习操作

1. 转移学习

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. 超参数调优

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测试

await metricsOptimizer.abTest({
  hypothesis: 'ML模式匹配提高测试质量',
  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. 反馈循环

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
  }
});

学习指标仪表板

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;
  }[];
}

跨框架转移

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

持续改进

await learningOptimizer.continuousImprovement({
  agents: ['test-generator', 'coverage-analyzer', 'defect-predictor'],
  schedule: {
    metricCollection: 'hourly',
    tuning: 'weekly',
    majorUpdates: 'monthly'
  },
  thresholds: {
    degradationAlert: 5,  // 百分比
    improvementTarget: 2,  // 每周百分比
  },
  automation: {
    autoTune: true,
    autoRollback: true,
    requireApproval: ['major-changes']
  }
});

模式学习

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
  }
});

协调

主要代理: qe-transfer-specialist, qe-metrics-optimizer, qe-pattern-learner 协调员: qe-learning-coordinator 相关技能: qe-test-generation, qe-defect-intelligence