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