8.6 KiB
8.6 KiB
Convergence Prediction Examples
Purpose: Worked examples of prediction model across different scenarios Model Accuracy: 85% (±1 iteration) across 13 experiments
Example 1: Error Recovery (Actual: 3 iterations)
Assessment
Domain: Error detection, diagnosis, recovery, prevention for meta-cc
Data Available:
- 1,336 historical errors in session logs
- Frequency distribution calculable
- Error rate: 5.78%
Prior Art:
- Industry error taxonomies (5 patterns borrowable)
- Standard recovery workflows
Automation:
- Top 3 obvious from frequency analysis
- File operations (high frequency, high ROI)
Prediction
Base: 4
Criterion 1 - V_meta(s₀):
- Completeness: 10/13 = 0.77
- Transferability: 5/10 = 0.50
- Automation: 3/3 = 1.0
- V_meta(s₀) = 0.758 ≥ 0.40? YES → +0 ✅
Criterion 2 - Domain Scope:
- "Error detection, diagnosis, recovery, prevention"
- <3 sentences? YES → +0 ✅
Criterion 3 - Validation:
- Retrospective with 1,336 errors
- Direct? YES → +0 ✅
Criterion 4 - Specialization:
- Generic data-analyst, doc-writer, coder sufficient
- Needed? NO → +0 ✅
Criterion 5 - Automation:
- Top 3 identified from frequency analysis
- Clear? YES → +0 ✅
Predicted: 4 + 0 = 4 iterations
Actual: 3 iterations ✅
Accuracy: Within ±1 ✅
Example 2: Test Strategy (Actual: 6 iterations)
Assessment
Domain: Develop test strategy for Go CLI project
Data Available:
- Coverage: 72.1%
- Test count: 590
- No documented patterns
Prior Art:
- Industry test patterns exist (table-driven, fixtures)
- Could borrow 50-70%
Automation:
- Coverage analysis tools (obvious)
- Test generation (feasible)
Prediction
Base: 4
Criterion 1 - V_meta(s₀):
- Completeness: 0/8 = 0.00 (no patterns)
- Transferability: 0/8 = 0.00 (no research done)
- Automation: 0/3 = 0.00 (not identified)
- V_meta(s₀) = 0.00 < 0.40? YES → +2 ❌
Criterion 2 - Domain Scope:
- "Develop test strategy" (vague)
- What tests? How much coverage?
- Fuzzy? YES → +1 ❌
Criterion 3 - Validation:
- Multi-context needed (3 archetypes)
- Direct? NO → +2 ❌
Criterion 4 - Specialization:
- coverage-analyzer: 30x speedup
- test-generator: 10x speedup
- Needed? YES → +1 ❌
Criterion 5 - Automation:
- Coverage tools obvious
- Clear? YES → +0 ✅
Predicted: 4 + 2 + 1 + 2 + 1 + 0 = 10 iterations
Actual: 6 iterations ⚠️
Accuracy: -4 (model conservative)
Analysis: Model over-predicted, but signaled "not rapid" correctly.
Example 3: CI/CD Optimization (Hypothetical)
Assessment
Domain: Reduce build time through caching, parallelization, optimization
Data Available:
- CI logs for last 3 months
- Build times: avg 8 min (range: 6-12 min)
- Failure rate: 25%
Prior Art:
- Industry CI/CD patterns well-documented
- GitHub Actions best practices (7 patterns)
Automation:
- Pipeline analysis (parse CI logs)
- Config generator (template-based)
Prediction
Base: 4
Criterion 1 - V_meta(s₀):
Estimate:
- Analyze CI logs: identify 5 patterns initially
- Expected final: 7 patterns
- Completeness: 5/7 = 0.71
- Borrow 3 industry patterns: 3/7 = 0.43
- Automation: 2 tools identified = 2/2 = 1.0
- V_meta(s₀) = 0.4×0.71 + 0.3×0.43 + 0.3×1.0 = 0.61 ≥ 0.40? YES → +0 ✅
Criterion 2 - Domain Scope:
- "Reduce CI/CD build time through caching, parallelization, optimization"
- Clear? YES → +0 ✅
Criterion 3 - Validation:
- Test on own pipeline (single context)
- Direct? YES → +0 ✅
Criterion 4 - Specialization:
- Pipeline analysis: bash/jq sufficient
- Config generation: template-based (generic)
- Needed? NO → +0 ✅
Criterion 5 - Automation:
- Caching, parallelization, fast-fail (top 3 obvious)
- Clear? YES → +0 ✅
Predicted: 4 + 0 = 4 iterations (rapid convergence)
Expected actual: 3-5 iterations
Confidence: High (all criteria met)
Example 4: Security Audit Methodology (Hypothetical)
Assessment
Domain: Systematic security audit for web applications
Data Available:
- Limited (1-2 past audits)
- No quantitative metrics
Prior Art:
- OWASP Top 10, industry checklists
- High transferability (70-80%)
Automation:
- Static analysis tools
- Fuzzy (requires domain expertise to identify)
Prediction
Base: 4
Criterion 1 - V_meta(s₀):
Estimate:
- Limited data, initial patterns: ~3
- Expected final: ~12 (security domains)
- Completeness: 3/12 = 0.25
- Borrow OWASP/industry: 9/12 = 0.75
- Automation: unclear (tools exist but need selection)
- V_meta(s₀) = 0.4×0.25 + 0.3×0.75 + 0.3×0.30 = 0.42 ≥ 0.40? YES → +0 ✅
Criterion 2 - Domain Scope:
- "Systematic security audit for web applications"
- But: which vulnerabilities? what depth?
- Fuzzy? YES → +1 ❌
Criterion 3 - Validation:
- Multi-context (need to test on multiple apps)
- Different tech stacks
- Direct? NO → +2 ❌
Criterion 4 - Specialization:
- Security-focused agents valuable
- Domain expertise needed
- Needed? YES → +1 ❌
Criterion 5 - Automation:
- Static analysis obvious
- But: which tools? how to integrate?
- Somewhat clear? PARTIAL → +0.5 ≈ +1 ❌
Predicted: 4 + 0 + 1 + 2 + 1 + 1 = 9 iterations
Expected actual: 7-10 iterations (exploratory)
Confidence: Medium (borderline V_meta(s₀), multiple penalties)
Example 5: Documentation Management (Hypothetical)
Assessment
Domain: Documentation quality and consistency for large codebase
Data Available:
- Existing docs: 150 files
- Quality issues logged: 80 items
- No systematic approach
Prior Art:
- Documentation standards (Google, Microsoft style guides)
- High transferability
Automation:
- Linters (markdownlint, prose)
- Doc generators
Prediction
Base: 4
Criterion 1 - V_meta(s₀):
Estimate:
- Analyze 80 quality issues: 8 categories
- Expected final: 10 categories
- Completeness: 8/10 = 0.80
- Borrow style guide patterns: 7/10 = 0.70
- Automation: linters + generators = 3/3 = 1.0
- V_meta(s₀) = 0.4×0.80 + 0.3×0.70 + 0.3×1.0 = 0.83 ≥ 0.40? YES → +0 ✅✅
Criterion 2 - Domain Scope:
- "Documentation quality and consistency for codebase"
- Clear quality metrics (completeness, accuracy, style)
- Clear? YES → +0 ✅
Criterion 3 - Validation:
- Retrospective on 150 existing docs
- Direct? YES → +0 ✅
Criterion 4 - Specialization:
- Generic doc-writer + linters sufficient
- Needed? NO → +0 ✅
Criterion 5 - Automation:
- Linters, generators, templates (obvious)
- Clear? YES → +0 ✅
Predicted: 4 + 0 = 4 iterations (rapid convergence)
Expected actual: 3-4 iterations
Confidence: Very High (strong V_meta(s₀), all criteria met)
Summary Table
| Example | V_meta(s₀) | Penalties | Predicted | Actual | Accuracy |
|---|---|---|---|---|---|
| Error Recovery | 0.758 | 0 | 4 | 3 | ✅ ±1 |
| Test Strategy | 0.00 | 5 | 10 | 6 | ⚠️ -4 (conservative) |
| CI/CD Opt. | 0.61 | 0 | 4 | (3-5 expected) | TBD |
| Security Audit | 0.42 | 4 | 9 | (7-10 expected) | TBD |
| Doc Management | 0.83 | 0 | 4 | (3-4 expected) | TBD |
Pattern Recognition
Rapid Convergence Profile (4-5 iterations)
Characteristics:
- V_meta(s₀) ≥ 0.50 (strong baseline)
- 0-1 penalties total
- Clear domain scope
- Direct/retrospective validation
- Obvious automation opportunities
Examples: Error Recovery, CI/CD Opt., Doc Management
Standard Convergence Profile (6-8 iterations)
Characteristics:
- V_meta(s₀) = 0.20-0.40 (weak baseline)
- 2-4 penalties total
- Some scoping needed
- Multi-context validation OR specialization needed
Examples: Test Strategy (6 actual)
Exploratory Profile (9+ iterations)
Characteristics:
- V_meta(s₀) < 0.20 (no baseline)
- 5+ penalties total
- Fuzzy scope
- Multi-context validation AND specialization needed
- Unclear automation
Examples: Security Audit (hypothetical)
Using Predictions
High Confidence (0-1 penalties)
Action: Invest in strong iteration 0 (3-5 hours) Expected: Rapid convergence (3-5 iterations, 10-15 hours) Strategy: Comprehensive baseline, aggressive iteration 1
Medium Confidence (2-4 penalties)
Action: Standard iteration 0 (1-2 hours) Expected: Standard convergence (6-8 iterations, 20-30 hours) Strategy: Incremental improvements, focus on high-value
Low Confidence (5+ penalties)
Action: Minimal iteration 0 (<1 hour) Expected: Exploratory (9+ iterations, 30-50 hours) Strategy: Discovery-driven, establish baseline first
Source: BAIME Rapid Convergence Prediction Model Accuracy: 85% (±1 iteration) on 13 experiments Purpose: Planning tool for experiment design