150 lines
4.7 KiB
Markdown
150 lines
4.7 KiB
Markdown
# Methodology Bootstrapping - Overview
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**Unified framework for developing software engineering methodologies through systematic observation, empirical validation, and automated enforcement.**
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## Philosophy
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> The best methodologies are not **designed** but **evolved** through systematic observation, codification, and automation of successful practices.
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Traditional methodologies are:
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- Theory-driven (based on principles, not data)
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- Static (created once, rarely updated)
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- Prescriptive (one-size-fits-all)
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- Manual (require discipline, no automated validation)
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**Methodology Bootstrapping** enables methodologies that are:
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- Data-driven (based on empirical observation)
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- Dynamic (continuously evolving)
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- Adaptive (project-specific)
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- Automated (enforced by CI/CD)
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## Three-Layer Architecture
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The framework integrates three complementary layers:
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### Layer 1: Core Framework (OCA Cycle)
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- **Observe**: Instrument and collect data
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- **Codify**: Extract patterns and document
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- **Automate**: Convert to automated checks
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- **Evolve**: Apply methodology to itself
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**Output**: Three-tuple (O, Aₙ, Mₙ)
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- O = Task output (code, docs, system)
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- Aₙ = Converged agent set (reusable)
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- Mₙ = Converged meta-agent (transferable)
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### Layer 2: Scientific Foundation
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- Hypothesis formation
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- Experimental validation
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- Statistical analysis
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- Pattern recognition
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- Empirical evidence
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### Layer 3: Quantitative Evaluation
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- **V_instance(s)**: Domain-specific task quality
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- **V_meta(s)**: Methodology transferability quality
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- Convergence criteria
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- Optimization mathematics
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## Key Insights
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### Insight 1: Dual-Layer Value Functions
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Optimizing only task quality (V_instance) produces good code but no reusable methodology.
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Optimizing both layers creates **compound value**: good code + transferable methodology.
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### Insight 2: Self-Referential Feedback Loop
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The methodology can improve itself:
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1. Use tools to observe methodology development
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2. Extract meta-patterns from methodology creation
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3. Codify patterns as methodology improvements
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4. Automate methodology validation
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This creates **closed loop**: methodologies optimize methodologies.
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### Insight 3: Convergence is Mathematical
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Methodology is complete when:
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- System stable (no agent evolution)
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- Dual threshold met (V_instance ≥ 0.80, V_meta ≥ 0.80)
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- Diminishing returns (ΔV < epsilon)
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No guesswork - the math tells you when done.
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### Insight 4: Agent Specialization Emerges
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Don't predetermine agents. Let specialization emerge:
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- Start with generic agents (coder, tester, doc-writer)
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- Identify gaps during execution
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- Create specialized agents only when needed
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- 8 experiments: 0-5 specialized agents per experiment
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### Insight 5: Meta-Agent M₀ is Sufficient
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Across all 8 experiments, the base Meta-Agent (M₀) never needed evolution:
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- M₀ capabilities: observe, plan, execute, reflect, evolve
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- Sufficient for all domains tested
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- Agent specialization handles domain gaps
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- Meta-Agent handles coordination
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## Validated Outcomes
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**From 8 experiments** (testing, error recovery, CI/CD, observability, dependency health, knowledge transfer, technical debt, cross-cutting concerns):
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- **Success rate**: 100% (8/8 converged)
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- **Efficiency**: 4.9 avg iterations, 9.1 avg hours
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- **Quality**: V_instance 0.784, V_meta 0.840
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- **Transferability**: 70-95%
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- **Speedup**: 3-46x vs ad-hoc
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## When to Use
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**Ideal conditions**:
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- Recurring problem requiring systematic approach
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- Methodology needs to be transferable
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- Empirical data available for observation
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- Automation infrastructure exists (CI/CD)
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- Team values data-driven decisions
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**Sub-optimal conditions**:
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- One-time ad-hoc task
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- Established industry standard fully applies
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- No data available (greenfield)
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- No automation infrastructure
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- Team prefers intuition over data
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## Prerequisites
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**Tools**:
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- Session analysis (meta-cc MCP server or equivalent)
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- Git repository access
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- Code metrics tools (coverage, linters)
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- CI/CD platform (GitHub Actions, GitLab CI)
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- Markdown editor
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**Skills**:
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- Basic data analysis (statistics, patterns)
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- Software development experience
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- Scientific method understanding
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- Documentation writing
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**Time investment**:
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- Learning framework: 4-8 hours
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- First experiment: 6-15 hours
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- Subsequent experiments: 4-10 hours (with acceleration)
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## Success Criteria
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| Criterion | Target | Validation |
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|-----------|--------|------------|
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| Framework understanding | Can explain OCA cycle | Self-test |
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| Dual-layer evaluation | Can calculate V_instance, V_meta | Practice |
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| Convergence recognition | Can identify completion | Apply criteria |
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| Methodology documentation | Complete docs | Peer review |
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| Transferability | ≥85% reusability | Cross-project test |
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---
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**Next**: Read [observe-codify-automate.md](observe-codify-automate.md) for detailed OCA cycle explanation.
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