.claude/skills/qe-defect-intelligence/SKILL.md
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.
npx skillsauth add proffesor-for-testing/agentic-qe qe-defect-intelligenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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
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