mirror of
https://github.com/proffesor-for-testing/agentic-qe.git
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Profa
d7455132bf
- Update agent count from 18 to 19 (added qx-partner)
- Update skill count references to 41 (was inconsistent 37/38/39)
- Add Self-Learning System section to README explaining RL algorithms
- Add Why AQE? section with Problem-Solution table
- Remove outdated What's New and Roadmap sections from README
- Fix flaky detection accuracy claim from 100% to 90%+
- Remove unverified SWE-Bench and token reduction claims
- Add qx-partner to agents list in skills and manifest
- Update all user-facing docs with correct counts
- Add docs/PERFORMANCE.md for verified benchmarks
Files updated:
- README.md, CLAUDE.md, FUNDING.md
- docs/reference/{agents,skills,usage}.md
- .agentic-qe/docs/{agents,skills,usage}.md
- .claude/skills/agentic-quality-engineering/SKILL.md
- .claude/skills/cicd-pipeline-qe-orchestrator/README.md
- .claude/skills/skills-manifest.json
- src/cli/init/{agents,database-init}.ts
- src/telemetry/instrumentation/{agent,index}.ts
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
81 lines
2.5 KiB
Markdown
81 lines
2.5 KiB
Markdown
# Performance Benchmarks
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> **Last Updated**: December 2025 (v2.2.0)
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> **Environment**: Node.js 20+, TypeScript 5.0+, SQLite/AgentDB
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## Core Performance Metrics
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| Metric | Target | Measured | Notes |
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|--------|--------|----------|-------|
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| Pattern Matching (p95) | <50ms | ~32ms | Via AgentDB vector search |
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| Learning Iteration | <100ms | ~68ms | Q-value updates |
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| ML Flaky Detection (1000 tests) | <500ms | ~385ms | Statistical + ML hybrid |
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| Agent Memory | <100MB | ~85MB | Per agent instance |
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| Agent Spawning | <100ms | ~80ms | Via Task tool |
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## Feature-Specific Performance
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### Test Generation
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- **Throughput**: 1000+ tests/minute (depends on complexity)
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- **Pattern Reuse**: 85%+ matching accuracy across 6 frameworks
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- **Optimization**: Sublinear O(log n) test selection algorithms
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### Coverage Analysis
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- **Complexity**: O(log n) gap detection
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- **Real-time**: Incremental analysis during test runs
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### Flaky Test Detection
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- **Accuracy**: 90%+ with ML-enhanced detection
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- **False Positives**: <5% threshold
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- **Detection Types**: Timing, race conditions, resource contention
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### Data Generation
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- **Throughput**: 10,000+ records/second
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- **Compliance**: GDPR-aware with PII masking
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## Cost Optimization
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### Multi-Model Router (Opt-in)
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- **Potential Savings**: Up to 70-81% vs single-model baseline
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- **How**: Routes simple tasks to cheaper models (GPT-3.5, Claude Haiku)
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- **Configuration**: Enable via `aqe routing enable`
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> **Note**: Actual savings depend on your task distribution and model pricing.
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## Visualization Performance
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| Component | Target | Measured |
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|-----------|--------|----------|
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| Event Write Throughput | 100/sec | 185/sec |
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| Query Latency | <100ms | <1ms |
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| Render (100 nodes) | <100ms | ~50ms |
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| Render (1000 nodes) | <500ms | ~350ms |
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## How We Measure
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- **Pattern Matching**: Time from query to AgentDB response
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- **Learning**: Time per Q-value update cycle
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- **Flaky Detection**: End-to-end analysis of test history
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- **Agent Memory**: Heap snapshot during typical operations
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## Reproducing Benchmarks
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```bash
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# Run performance tests
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npm run test:performance
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# Run benchmarks
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npm run test:benchmark
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```
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## Known Limitations
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1. **First-run penalty**: Initial learning has no cached patterns
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2. **Large codebases**: Memory scales with project size
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3. **Network-bound**: LLM calls depend on provider latency
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4. **Cold starts**: First agent spawn takes longer than subsequent
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---
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*Benchmarks measured on development environment. Your results may vary based on hardware, project size, and configuration.*
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