/qe-defect-intelligence

name: "qe-defect-intelligence"
description: "Predicts defect-prone code using change frequency, complexity metrics, and historical bug patterns. Use when predicting defects before they escape, analyzing root causes of test failures, learning from past defect patterns, or implementing proactive quality management."
trust_tier: 3
validation:
  schema_path: schemas/output.json
  validator_path: scripts/validate-config.json
  eval_path: evals/qe-defect-intelligence.yaml

QE Defect Intelligence

Purpose

Guide the use of v3's defect intelligence capabilities including ML-based defect prediction, pattern recognition from historical data, and automated root cause analysis.

Activation

• When predicting defect-prone code
• When analyzing failure patterns
• When performing root cause analysis
• When learning from past defects
• When prioritizing testing based on risk

Quick Start

# Predict defects in changed code
aqe defect predict --changes HEAD~5..HEAD

# Analyze failure patterns
aqe defect patterns --period 90d --min-occurrences 3

# Root cause analysis
aqe defect rca --failure "test/auth.test.ts:45"

# Learn from resolved defects
aqe defect learn --source jira --status resolved

Agent Workflow

// Defect prediction
Task("Predict defect-prone code", `
  Analyze PR #456 changes and predict defect likelihood:
  - Historical defect correlation
  - Code complexity factors
  - Author experience with module
  - Test coverage gaps
  Flag high-risk changes requiring extra review.
`, "qe-defect-predictor")

// Root cause analysis
Task("Analyze test failure", `
  Investigate recurring failure in AuthService tests:
  - Collect failure history (last 30 days)
  - Identify common patterns
  - Trace to potential root causes
  - Suggest fixes using 5-whys analysis
`, "qe-root-cause-analyzer")

Prediction Models

1. Change-Based Prediction

await defectPredictor.predictFromChanges({
  changes: prChanges,
  factors: {
    codeChurn: { weight: 0.2 },
    complexity: { weight: 0.25 },
    authorExperience: { weight: 0.15 },
    fileHistory: { weight: 0.2 },
    testCoverage: { weight: 0.2 }
  },
  threshold: {
    high: 0.7,
    medium: 0.4,
    low: 0.2
  }
});

2. Pattern Learning

await patternLearner.learnPatterns({
  source: {
    defects: 'jira:project=MYAPP&type=bug',
    commits: 'git:last-6-months',
    tests: 'test-results:last-1000-runs'
  },
  patterns: [
    'code-smell-to-defect',
    'change-coupling',
    'test-gap-correlation',
    'complexity-defect-density'
  ],
  output: {
    rules: true,
    visualizations: true,
    recommendations: true
  }
});

3. Root Cause Analysis

await rootCauseAnalyzer.analyze({
  failure: testFailure,
  methods: [
    'five-whys',
    'fishbone-diagram',
    'fault-tree',
    'change-impact'
  ],
  context: {
    recentChanges: true,
    environmentDiff: true,
    dependencyChanges: true,
    similarFailures: true
  }
});

Defect Prediction Report

interface DefectPrediction {
  file: string;
  riskScore: number;  // 0-1
  riskLevel: 'critical' | 'high' | 'medium' | 'low';
  factors: {
    name: string;
    contribution: number;
    details: string;
  }[];
  historicalDefects: {
    count: number;
    recent: Defect[];
    patterns: string[];
  };
  recommendations: {
    action: string;
    priority: string;
    expectedRiskReduction: number;
  }[];
}

Pattern Categories

| Pattern | Detection | Prevention | |---------|-----------|------------| | Null pointer | Static analysis | Null checks, Optional | | Race condition | Concurrency analysis | Locks, atomic ops | | Memory leak | Heap analysis | Resource cleanup | | Off-by-one | Boundary analysis | Loop invariants | | Injection | Taint analysis | Input validation |

Root Cause Templates

root_cause_analysis:
  five_whys:
    max_depth: 5
    prompt_template: "Why did {effect} happen?"

  fishbone:
    categories:
      - people
      - process
      - tools
      - environment
      - materials
      - measurement

  fault_tree:
    top_event: "Test Failure"
    gate_types: [AND, OR, NOT]
    basic_events: true

Integration with Issue Tracking

await defectIntelligence.syncWithTracker({
  source: 'jira',
  project: 'MYAPP',
  sync: {
    defectData: 'bidirectional',
    predictions: 'create-tasks',
    patterns: 'update-labels'
  },
  automation: {
    flagHighRisk: true,
    suggestAssignee: true,
    linkRelated: true
  }
});

Coordination

**Primary Agents**: qe-defect-predictor, qe-pattern-learner, qe-root-cause-analyzer **Coordinator**: qe-defect-intelligence-coordinator **Related Skills**: qe-coverage-analysis, qe-quality-assessment