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