Tech Debt Quantifier

Identify, classify, and quantify technical debt across architecture, code, tests, documentation, and infrastructure, estimating the ongoing cost (interest) of carrying each debt item.

Guiding Principle

"Technical debt is not inherently bad — uninventored debt is. You can't manage what you haven't measured."

Procedure

Step 1 — Debt Discovery

1. Scan for explicit debt markers: TODO, FIXME, HACK, WORKAROUND, TECH_DEBT in source files.
2. Identify implicit debt: deprecated dependencies, legacy patterns, duplicated code.
3. Review git history for files with high churn + high complexity (debt hotspots).
4. Check for deferred migrations: old framework versions, pending schema changes.
5. Catalog each debt item with location, type, and origin context [HECHO].

Step 2 — Debt Classification

1. Classify by category: Architecture, Code, Test, Documentation, Infrastructure, Dependency.
2. Classify by intentionality: Deliberate (known shortcut) vs. Inadvertent (accumulated cruft).
3. Classify by risk: Reckless (no plan to pay back) vs. Prudent (calculated trade-off).
4. Tag each item with affected quality attribute: maintainability, performance, security, reliability.

Step 3 — Interest Estimation

1. Estimate ongoing cost per debt item: developer time lost per week/month.
2. Assess blast radius: how many features or teams are affected by this debt.
3. Calculate compounding risk: debt that makes other debt worse.
4. Estimate payoff effort: developer-days to remediate each item [INFERENCIA].
5. Compute interest rate: ongoing cost / payoff effort.

Step 4 — Prioritized Debt Register

1. Rank debt items by interest rate (highest ongoing cost per remediation effort first).
2. Group into quick wins (<1 day), sprints (1-2 weeks), projects (>2 weeks).
3. Produce a debt register with owner assignment recommendations.
4. Calculate total estimated debt in developer-days.

Quality Criteria

• Every debt item traced to specific code locations [HECHO]
• Classification uses consistent taxonomy
• Interest estimates include reasoning, not just numbers
• Prioritization considers both cost and risk

Anti-Patterns

• Counting TODOs as the only source of tech debt (most debt is implicit)
• Treating all debt as equally urgent without prioritization
• Estimating payoff time without considering testing and migration effort
• Ignoring infrastructure and documentation debt (it compounds silently)