Tech Debt Management

Systematically identify, categorize, and prioritize technical debt.

Categories

| Type | Examples | Risk |

|------|----------|------|

| Code debt | Duplicated logic, poor abstractions, magic numbers | Bugs, slow development |

| Architecture debt | Monolith that should be split, wrong data store | Scaling limits |

| Test debt | Low coverage, flaky tests, missing integration tests | Regressions ship |

| Dependency debt | Outdated libraries, unmaintained dependencies | Security vulns |

| Documentation debt | Missing runbooks, outdated READMEs, tribal knowledge | Onboarding pain |

| Infrastructure debt | Manual deploys, no monitoring, no IaC | Incidents, slow recovery |

Prioritization Framework

Score each item on:

• Impact: How much does it slow the team down? (1-5)
• Risk: What happens if we don't fix it? (1-5)
• Effort: How hard is the fix? (1-5, inverted — lower effort = higher priority)

Priority = (Impact + Risk) x (6 - Effort)

Output

Produce a prioritized list with estimated effort, business justification for each item, and a phased remediation plan that can be done alongside feature work.