mirror of
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Profa
84760936f4
Added detailed GOAP-based improvement plan for v1.0.5-v1.2.0: - IMPROVEMENT-PLAN-SUMMARY.md: Executive summary with impact matrix - AGENTIC-QE-IMPROVEMENT-PLAN.md: Full implementation plan with code examples - IMPROVEMENT-ROADMAP-VISUAL.md: Visual roadmap with ASCII diagrams Phase 1 (v1.0.5) targets: - Multi-Model Router: 70% cost reduction - Streaming MCP Tools: 50% UX improvement - Enhanced reliability: 99% uptime 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
378 lines
24 KiB
Markdown
378 lines
24 KiB
Markdown
# Agentic QE Fleet - Visual Improvement Roadmap
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```
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AGENTIC QE FLEET EVOLUTION
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==========================
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Current State (v1.0.4) Target State (v1.2.0)
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├─ Single Model (Claude Sonnet 4.5) ├─ Multi-Model Router (4+ models)
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├─ Basic Test Generation ├─ ReasoningBank-Powered Generation
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├─ O(n) Coverage Analysis ├─ O(log n) Sublinear Coverage
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├─ Manual Quality Assessment ├─ Predictive Quality Intelligence
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├─ No Learning └─ Continuous Agent Learning
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└─ 95% Uptime └─ 99.9% Uptime (Self-Healing)
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================================================================================
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TIMELINE (6 MONTHS)
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================================================================================
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Week 1-4 │ Week 5-12 │ Week 13-24
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v1.0.5 │ v1.1.0 │ v1.2.0
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"Cost │ "Intelligence Boost" │ "Performance Beast"
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Optimizer" │ │
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┌─────────────┼──────────────────────────────┼───────────────────────────────┐
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│ │ │ │
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│ Quick Wins │ Core Intelligence │ Advanced Algorithms │
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│ │ │ │
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│ ✓ Multi- │ ✓ QE ReasoningBank │ ✓ O(log n) Coverage │
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│ Model │ - Pattern Library │ - Johnson-Lindenstrauss │
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│ Router │ - Test Reuse │ - Spectral Sparsification │
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│ (40h) │ - Cross-Project (80h) │ - Adaptive Neumann (160h) │
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│ │ │ │
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│ ✓ Streaming │ ✓ Agent Learning │ ✓ Predictive Quality │
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│ MCP Tools │ - Reinforcement │ - Defect Prediction │
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│ (32h) │ - Flaky Detection │ - Risk Prioritization │
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│ │ - Continuous (120h) │ - Forecasting (120h) │
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│ │ │ │
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│ ✓ Enhanced │ ✓ Pattern Sharing │ ✓ Production Intelligence │
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│ Error │ - Multi-Framework │ - Incident Correlation │
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│ Recovery │ - Community Patterns │ - Quality Trends │
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│ (16h) │ - Smart Matching (40h) │ - Risk Scoring (80h) │
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│ │ │ │
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└─────────────┴──────────────────────────────┴───────────────────────────────┘
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================================================================================
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IMPACT vs EFFORT MATRIX
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================================================================================
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HIGH IMPACT
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▲
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│
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│ [Multi-Model Router] [QE ReasoningBank]
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│ v1.0.5 v1.1.0
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│ 40h │ 70% cost 80h │ 40% efficiency
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│ │ │
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│ ───────┼───────────────────────┼───────────────────►
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│ │ │
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│ [Streaming MCP] [Agent Learning]
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│ │ │
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│ 32h │ 50% UX 120h │ 30% stability
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│ │ │
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├─────────┼───────────────────────┼───────────────────
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│ │ │ [O(log n)]
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│ [Error │ │ Coverage
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│ Recovery] │
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│ 16h │ │ 160h │ 5-10x
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│ │ │ │ speedup
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│ │ │ │
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▼ └───────────────────────┴──────────┴────────►
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LOW IMPACT HIGH EFFORT
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================================================================================
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COST OPTIMIZATION FLOW
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================================================================================
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Current State:
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┌──────────────────────────────────────────────────────────────┐
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│ ALL TASKS → Claude Sonnet 4.5 → $0.10/test │
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└──────────────────────────────────────────────────────────────┘
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v1.0.5 Multi-Model Router:
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┌──────────────────────────────────────────────────────────────┐
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│ TASK ANALYZER │
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│ (Complexity Detection) │
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└───────────────────────┬──────────────────────────────────────┘
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│
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┌──────────────┼──────────────┐
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│ │ │
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▼ ▼ ▼
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┌─────────┐ ┌─────────┐ ┌──────────┐ ┌──────────┐
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│ Simple │ │Moderate │ │ Complex │ │ Critical │
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│ Tasks │ │ Tasks │ │ Tasks │ │ Tasks │
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└────┬────┘ └────┬────┘ └─────┬────┘ └─────┬────┘
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│ │ │ │
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▼ ▼ ▼ ▼
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GPT-3.5 Claude Haiku GPT-4 Claude Sonnet
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$0.002/test $0.005/test $0.06/test $0.10/test
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│ │ │ │
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└─────────────┴──────────────┴──────────────┘
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│
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▼
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Average Cost: $0.03/test
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(70% reduction!)
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================================================================================
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INTELLIGENCE ENHANCEMENT FLOW
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================================================================================
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Current Test Generation:
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┌────────────┐ ┌──────────┐ ┌──────────┐
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│ Source │────▶│ AST │────▶│ Generate │
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│ Code │ │ Analysis │ │ Tests │
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└────────────┘ └──────────┘ └──────────┘
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v1.1.0 ReasoningBank-Enhanced:
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┌────────────┐ ┌──────────────┐ ┌──────────────┐
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│ Source │────▶│ Code │────▶│ Pattern │
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│ Code │ │ Signature │ │ Matching │
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└────────────┘ └──────────────┘ └──────┬───────┘
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│
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┌────────────────────────────┘
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│
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▼
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┌──────────────────────┐
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│ REASONING BANK │
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│ ════════════════ │
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│ • 100+ Patterns │
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│ • Edge Cases │
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│ • Integration │
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│ • Boundary Tests │
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│ • Error Handling │
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└──────────┬───────────┘
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│
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▼
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┌──────────────────────┐ ┌──────────────┐
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│ Enhanced Generate │────▶│ Tests + │
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│ with Patterns │ │ 40% Better │
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└──────────────────────┘ └──────────────┘
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================================================================================
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LEARNING SYSTEM ARCHITECTURE
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================================================================================
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v1.1.0 Agent Learning Loop:
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┌─────────────────────────────────────────────────────────┐
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│ EXECUTION PHASE │
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│ │
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│ Task → Agent → Execute → Result → Feedback │
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└──────────────────────┬──────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────┐
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│ LEARNING ENGINE │
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│ │
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│ ┌─────────────┐ ┌────────────┐ ┌─────────────┐ │
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│ │ Feature │ │ Reward │ │ Pattern │ │
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│ │ Extraction │─▶│Calculation │─▶│ Detection │ │
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│ └─────────────┘ └────────────┘ └─────────────┘ │
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└──────────────────────┬──────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────┐
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│ KNOWLEDGE UPDATE │
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│ │
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│ • Update Agent Weights │
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│ • Store Successful Patterns │
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│ • Flag Flaky Tests │
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│ • Improve Future Performance │
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└──────────────────────┬──────────────────────────────────┘
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│
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│ (20% improvement over 30 days)
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│
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▼
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[NEXT EXECUTION]
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(Better Performance)
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================================================================================
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PERFORMANCE OPTIMIZATION
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================================================================================
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Coverage Analysis Complexity:
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Current (v1.0.4):
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┌──────────────────────────────────────────────┐
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│ O(n) Linear Coverage Analysis │
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│ │
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│ Time for 100,000 LOC: ~60 seconds │
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│ Memory: ~2GB │
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└──────────────────────────────────────────────┘
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v1.2.0 Sublinear:
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┌──────────────────────────────────────────────┐
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│ O(log n) Sublinear Coverage │
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│ │
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│ 1. Johnson-Lindenstrauss Reduction │
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│ 100,000 dims → ~300 dims │
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│ │
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│ 2. Spectral Sparsification │
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│ Dense → Sparse (90% reduction) │
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│ │
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│ 3. Adaptive Neumann Series │
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│ Fast convergence in log iterations │
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│ │
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│ Time for 100,000 LOC: ~6 seconds (10x) │
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│ Memory: ~200MB (10x reduction) │
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└──────────────────────────────────────────────┘
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Performance Comparison:
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┌──────────────────────────────────────────────┐
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│ Lines of Code │ v1.0.4 │ v1.2.0 │ Gain│
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│ ──────────────┼──────────┼──────────┼─────│
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│ 1,000 │ 0.6s │ 0.5s │ 1.2x│
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│ 10,000 │ 6.0s │ 1.8s │ 3.3x│
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│ 100,000 │ 60.0s │ 6.0s │10.0x│
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│ 1,000,000 │ 600.0s │ 18.0s │33.3x│
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└──────────────────────────────────────────────┘
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================================================================================
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SUCCESS METRICS TIMELINE
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================================================================================
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ │
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│ v1.0.4 (Baseline) │
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│ ══════════════════ │
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│ Cost per test: $0.10 │
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│ Edge case coverage: 60% │
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│ Manual test time: 100% │
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│ Uptime: 95% │
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│ Coverage speed: O(n) │
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│ │
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│ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ │
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│ │
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│ v1.0.5 (Week 4) │
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│ ════════════════ │
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│ Cost per test: $0.03 (70% ↓) │
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│ Uptime: 99% (4% ↑) │
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│ User satisfaction: 4.2/5 │
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│ │
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│ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ │
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│ │
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│ v1.1.0 (Week 12) │
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│ ═════════════════ │
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│ Cost per test: $0.03 (maintained) │
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│ Edge case coverage: 85% (25% ↑) │
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│ Manual test time: 60% (40% ↓) │
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│ Flaky detection: 90% automated │
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│ Uptime: 99.5% (4.5% ↑) │
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│ │
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│ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ │
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│ │
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│ v1.2.0 (Week 24) │
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│ ═════════════════ │
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│ Cost per test: $0.03 (maintained) │
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│ Edge case coverage: 85% (maintained) │
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│ Manual test time: 60% (maintained) │
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│ Flaky detection: 90% (maintained) │
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│ Uptime: 99.9% (4.9% ↑) │
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│ Coverage speed: O(log n) (5-10x ↑) │
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│ Defect prediction: 75% accuracy │
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│ CI time: 60% (40% ↓) │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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================================================================================
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ROLLOUT RISK MITIGATION
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================================================================================
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Feature Flag Strategy:
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┌────────────────────────────────────────────────────────────────┐
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│ Default Configuration (Safe) │
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│ ════════════════════════════ │
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│ { │
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│ features: { │
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│ multiModelRouter: false, // v1.0.5 opt-in │
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│ streaming: false, // v1.0.5 opt-in │
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│ reasoningBank: false, // v1.1.0 opt-in │
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│ agentLearning: false, // v1.1.0 opt-in │
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│ sublinearCoverage: false // v1.2.0 opt-in │
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│ } │
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│ } │
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└────────────────────────────────────────────────────────────────┘
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Gradual Rollout:
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Week 1: 10% of users (beta testers)
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Week 2: 25% of users (if metrics good)
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Week 3: 50% of users (if metrics good)
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Week 4: 100% of users (GA release)
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Rollback Plan:
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- Feature flag disable in <5 minutes
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- Automatic fallback to v1.0.4 behavior
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- No data loss on rollback
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- Clear user communication
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================================================================================
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QUESTIONS FOR DECISION
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================================================================================
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┌─────────────────────────────────────────────────────────────────┐
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│ 1. BUDGET & RESOURCES │
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│ ❓ What is the ML infrastructure budget? │
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│ ❓ Do we have GPU access for training? (optional) │
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│ ❓ Can we afford OpenAI API costs? │
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│ │
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│ 2. STRATEGY │
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│ ❓ Prioritize cost (v1.0.5) or intelligence (v1.1.0)? │
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│ ❓ Open-source ReasoningBank patterns? │
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│ ❓ Acceptable cost per test? ($0.03 or $0.05?) │
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│ │
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│ 3. EXECUTION │
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│ ❓ Do we have 10 beta testers for v1.0.5? │
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│ ❓ Can we validate on 100k+ LOC projects? │
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│ ❓ Build web dashboard for cost tracking? │
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└─────────────────────────────────────────────────────────────────┘
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================================================================================
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RECOMMENDED NEXT STEPS
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================================================================================
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✅ IMMEDIATE (This Week):
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1. Review and approve improvement plan
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2. Set up project tracking (GitHub Projects)
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3. Recruit 10 beta testers
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4. Allocate 40 hours for Multi-Model Router
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✅ WEEK 1-2:
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5. Implement Multi-Model Router foundation
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6. Create cost tracking infrastructure
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7. Write unit tests (90%+ coverage)
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8. Document API and configuration
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✅ WEEK 3-4:
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9. Implement streaming MCP tools
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10. Beta test with 10 users
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11. Collect metrics and feedback
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12. Prepare v1.0.5 release
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✅ WEEK 5+ (After v1.0.5 GA):
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13. Start ReasoningBank implementation
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14. Design learning system architecture
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15. Plan v1.1.0 beta program
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================================================================================
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STATUS & CONTACT
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================================================================================
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Plan Status: READY FOR REVIEW ✅
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Last Updated: 2025-10-16
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Next Review: After stakeholder approval
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Full Plan: /workspaces/agentic-qe-cf/docs/AGENTIC-QE-IMPROVEMENT-PLAN.md
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Summary: /workspaces/agentic-qe-cf/docs/IMPROVEMENT-PLAN-SUMMARY.md
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Visual: /workspaces/agentic-qe-cf/docs/IMPROVEMENT-ROADMAP-VISUAL.md
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Contact: support@agentic-qe.com
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GitHub: https://github.com/proffesor-for-testing/agentic-qe/issues
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================================================================================
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```
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