AI-powered defect prediction, pattern learning, and root cause analysis for proactive quality management.
Guide the use of v3's defect intelligence capabilities including ML-based defect prediction, pattern recognition from historical data, and automated root cause analysis.
# 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
// 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")
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
}
});
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
}
});
await rootCauseAnalyzer.analyze({
failure: testFailure,
methods: [
'five-whys',
'fishbone-diagram',
'fault-tree',
'change-impact'
],
context: {
recentChanges: true,
environmentDiff: true,
dependencyChanges: true,
similarFailures: true
}
});
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 | 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_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
await defectIntelligence.syncWithTracker({
source: 'jira',
project: 'MYAPP',
sync: {
defectData: 'bidirectional',
predictions: 'create-tasks',
patterns: 'update-labels'
},
automation: {
flagHighRisk: true,
suggestAssignee: true,
linkRelated: true
}
});
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