agentic-quality-engineering

仓库: proffesor-for-testing/agentic-qe
安装量: 59
排名: #12567

安装

npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill agentic-quality-engineering

Agentic Quality Engineering

When implementing agentic QE or coordinating agents:

SPAWN appropriate agent(s) for the task using Task tool with agent type CONFIGURE agent coordination (hierarchical/mesh/sequential) EXECUTE with PACT principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus VALIDATE results through quality gates before deployment LEARN from outcomes - store patterns in aqe/learning/* namespace

Quick Agent Selection:

Test generation needed → qe-test-generator Coverage gaps → qe-coverage-analyzer Quality decision → qe-quality-gate Security scan → qe-security-scanner Performance test → qe-performance-tester Full pipeline → qe-fleet-commander

Critical Success Factors:

Agents amplify human expertise, not replace it Human-in-the-loop for critical decisions Measure: bugs caught, time saved, coverage improved Quick Reference Card When to Use Designing autonomous testing systems Scaling QE with intelligent agents Implementing multi-agent coordination Building CI/CD quality pipelines PACT Principles Principle Agent Behavior Human Role Proactive Analyze pre-merge, predict risk Set guardrails Autonomous Execute tests, fix flaky tests Review critical Collaborative Multi-agent coordination Provide context Targeted Risk-based prioritization Define risk areas 19-Agent Fleet Category Agents Primary Use Core Testing (5) test-generator, test-executor, coverage-analyzer, quality-gate, quality-analyzer Daily testing Performance/Security (2) performance-tester, security-scanner Non-functional Strategic (3) requirements-validator, production-intelligence, fleet-commander Planning Advanced (4) regression-risk-analyzer, test-data-architect, api-contract-validator, flaky-test-hunter Specialized Visual/Chaos (2) visual-tester, chaos-engineer Edge cases Deployment (1) deployment-readiness Release Analysis (1) code-complexity Maintainability Coordination Patterns Hierarchical: fleet-commander → [generators] → [executors] → quality-gate Mesh: test-gen ↔ coverage ↔ quality (peer decisions) Sequential: risk-analyzer → test-gen → executor → coverage → gate

Success Criteria

✅ 10x deployment frequency with same/better quality ✅ Coverage gaps detected in real-time ✅ Bugs caught pre-production ❌ Agents acting without human oversight on critical decisions ❌ Deploying all 19 agents at once (start with 1-2)

Core Concepts QE Evolution Stage Approach Limitation Traditional Manual everything Human bottleneck Automation Scripts + fixed scenarios Needs orchestration Agentic AI agents + human judgment Requires trust-building

Core Premise: Agents amplify human expertise for 10x scale.

Key Capabilities

  1. Intelligent Test Generation

// Agent analyzes code change, generates targeted tests const tests = await qeTestGenerator.generate(prDiff); // → Happy path, edge cases, error handling tests

  1. Pattern Detection - Scan logs, find anomalies, correlate errors

  2. Adaptive Strategy - Adjust test focus based on risk signals

  3. Root Cause Analysis - Link failures to code changes, suggest fixes

Agent Coordination Memory Namespaces aqe/test-plan/ - Test planning decisions aqe/coverage/ - Coverage analysis results aqe/quality/ - Quality metrics and gates aqe/learning/ - Patterns and Q-values aqe/coordination/* - Cross-agent state

Memory Operations (MCP Tools)

CRITICAL: Always use mcp__agentic-qe__memory_store with persist: true for learnings.

  1. Store data to persistent memory:

// Store test plan decisions (persisted to .agentic-qe/memory.db) mcp__agentic_qe__memory_store({ key: "aqe/test-plan/pr-123", namespace: "aqe/test-plan", value: { prNumber: 123, riskLevel: "medium", requiredCoverage: 85, testTypes: ["unit", "integration"], estimatedTime: 1800 }, persist: true, // ⚠️ REQUIRED for cross-session persistence ttl: 604800 // 7 days (0 = permanent) })

  1. Retrieve prior learnings before task:

// Query patterns before starting test generation const priorData = await mcp__agentic_qe__memory_retrieve({ key: "aqe/learning/patterns/test-generation/*", namespace: "aqe/learning", includeMetadata: true })

// Use patterns to guide current task if (priorData.success) { console.log(Loaded ${priorData.patterns.length} prior patterns); }

  1. Store coverage analysis results:

mcp__agentic_qe__memory_store({ key: "aqe/coverage/auth-module", namespace: "aqe/coverage", value: { moduleId: "auth-module", currentCoverage: 78, gaps: ["error-handling", "edge-cases"], suggestedTests: 12, priority: "high" }, persist: true, ttl: 1209600 // 14 days })

Three-Phase Memory Protocol

For coordinated multi-agent tasks, use the STATUS → PROGRESS → COMPLETE pattern:

// PHASE 1: STATUS - Task starting mcp__agentic_qe__memory_store({ key: "aqe/coordination/task-123/status", namespace: "aqe/coordination", value: { status: "running", agent: "qe-test-generator", startTime: Date.now() }, persist: true })

// PHASE 2: PROGRESS - Intermediate updates mcp__agentic_qe__memory_store({ key: "aqe/coordination/task-123/progress", namespace: "aqe/coordination", value: { progress: 50, action: "generating-unit-tests", testsGenerated: 25 }, persist: true })

// PHASE 3: COMPLETE - Task finished mcp__agentic_qe__memory_store({ key: "aqe/coordination/task-123/complete", namespace: "aqe/coordination", value: { status: "complete", result: "success", testsGenerated: 47, coverageAchieved: 92.3, duration: 15000 }, persist: true })

Blackboard Events Event Trigger Subscribers test:generated New tests created executor, coverage coverage:gap Gap detected test-generator quality:decision Gate evaluated fleet-commander security:finding Vulnerability found quality-gate Example: PR Quality Pipeline // 1. Risk analysis const risks = await Task("Analyze PR", prDiff, "qe-regression-risk-analyzer");

// 2. Generate tests for risks const tests = await Task("Generate tests", risks, "qe-test-generator");

// 3. Execute + analyze const results = await Task("Run tests", tests, "qe-test-executor"); const coverage = await Task("Check coverage", results, "qe-coverage-analyzer");

// 4. Quality decision const decision = await Task("Evaluate", {results, coverage}, "qe-quality-gate"); // → GO/NO-GO with rationale

Implementation Phases Phase Duration Goal Agent(s) Experiment Weeks 1-4 Validate one use case 1 agent Integrate Months 2-3 CI/CD pipeline 3-4 agents Scale Months 4-6 Multiple use cases 8+ agents Evolve Ongoing Continuous learning Full fleet Phase 1 Example

Week 1: Deploy single agent

aqe agent spawn qe-test-generator

Weeks 2-3: Generate tests for 10 PRs

Track: bugs found, test quality, review time

Week 4: Measure impact

aqe agent metrics qe-test-generator

→ Tests: 150, Bugs: 12, Time saved: 8h

Limitations & Strengths Agents Excel At Volume: Scan thousands of logs in seconds Patterns: Find correlations humans miss Tireless: 24/7 testing and monitoring Speed: Instant code change analysis Agents Need Humans For Business context and priorities Ethical judgment and trade-offs Creative exploration ("what if" scenarios) Domain expertise (healthcare, finance, legal) Best Practices Do Don't Start with one agent, one use case Deploy all 18 at once Build feedback loops early Deploy and forget Human reviews agent output Auto-merge without review Measure bugs caught, time saved Track vanity metrics (test count) Build trust gradually Give full autonomy immediately Trust Progression Month 1: Agent suggests → Human decides Month 2: Agent acts → Human reviews after Month 3: Agent autonomous on low-risk Month 4: Agent handles critical with oversight

Agent Coordination Hints coordination: topology: hierarchical commander: qe-fleet-commander memory_namespace: aqe/coordination blackboard_topic: qe-fleet

preload_skills: - agentic-quality-engineering # Always (this skill) - risk-based-testing # For prioritization - quality-metrics # For measurement

agent_assignments: qe-test-generator: [api-testing-patterns, tdd-london-chicago] qe-coverage-analyzer: [quality-metrics, risk-based-testing] qe-security-scanner: [security-testing, risk-based-testing] qe-performance-tester: [performance-testing]

Related Skills holistic-testing-pact - PACT principles deep dive risk-based-testing - Prioritize agent focus quality-metrics - Measure agent effectiveness api-testing-patterns, security-testing, performance-testing - Specialized testing Resources Agent definitions: .claude/agents/ CLI: aqe agent --help Fleet status: aqe fleet status

Success Metric: Deploy 10x more frequently with same or better quality through intelligent agent collaboration.

← 返回排行榜