gh-yaleh-meta-cc-claude/skills/technical-debt-management/reference/overview.md

Technical Debt Management Methodology - Reference

This reference documentation provides comprehensive details on the SQALE-based technical debt quantification methodology developed in bootstrap-012.

Core Methodology Components

Six Components (complete methodology):

1. Measurement Framework (SQALE Index calculation)
2. Categorization Framework (Code smell taxonomy)
3. Prioritization Framework (Value-effort matrix)
4. Paydown Framework (Phased roadmap)
5. Tracking Framework (Trend analysis)
6. Prevention Framework (Proactive practices)

SQALE Methodology

SQALE (Software Quality Assessment based on Lifecycle Expectations):

• Industry-standard debt quantification
• Development cost: LOC / 30 (30 LOC/hour productivity)
• Remediation cost: Graduated complexity thresholds
• TD Ratio: (Debt / Development Cost) × 100%
• Rating: A (≤5%) to E (>50%)

Knowledge Artifacts

All knowledge artifacts from bootstrap-012 are documented in: experiments/bootstrap-012-technical-debt/knowledge/

Patterns (3):

• SQALE-Based Debt Quantification (90% reusable)
• Code Smell Taxonomy Mapping (80% reusable)
• Value-Effort Prioritization Matrix (95% reusable)

Principles (3):

• Pay High-Value Low-Effort Debt First
• SQALE Provides Objective Baseline
• Complexity Drives Maintainability Debt

Templates (4):

• SQALE Index Report Template
• Code Smell Categorization Template
• Remediation Cost Breakdown Template
• Transfer Guide Template

Best Practices (3):

• Use SQALE standard productivity (30 LOC/hour)
• Apply graduated complexity thresholds
• Categorize debt by SQALE characteristics

Effectiveness Validation

Speedup: 4.5x vs manual approach

• Manual: 9 hours (ad-hoc review, subjective)
• Methodology: 2 hours (tool-based, SQALE)

Accuracy: Subjective → Objective (SQALE standard) Reproducibility: Low → High (industry standard)

Transferability

Overall: 85% transferable across languages

Language-Specific Adaptations:

• Go: 90% (native)
• Python: 85% (threshold 10→12, tools: radon, pylint, pytest-cov)
• JavaScript: 85% (threshold 10→8, tools: eslint, jscpd, nyc)
• Java: 90% (tools: PMD, JaCoCo, CheckStyle)
• Rust: 80% (threshold 10→15, tools: cargo-geiger, clippy, skip OO smells)

Universal Components (13/16, 81%):

• SQALE formulas (100%)
• Prioritization matrix (100%)
• Paydown roadmap structure (100%)
• Tracking approach (95%)
• Prevention practices (85%)

Language-Specific (3/16, 19%):

• Complexity threshold calibration (±20%)
• Tool selection (language-specific)
• OO smells applicability (OO languages only)

Experiment Results

See full results: experiments/bootstrap-012-technical-debt/results.md

Key Metrics:

• V_instance = 0.805 (CONVERGED)
• V_meta = 0.855 (CONVERGED)
• 4 iterations, ~7 hours total
• 4.5x speedup, 85% transferability
• meta-cc debt: 66 hours, 15.52% TD ratio, rating C
• Paydown roadmap: 31.5 hours → rating B (8.23%)