qe-defect-intelligence

# 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 ```bash # 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 ```typescript // 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 ```typescript 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 ```typescript 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 ```typescript 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 ```typescript 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 ```yaml 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 ```typescript 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