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| Dependency debt | Outdated libraries, unmaintained dependencies | Security vulns |
| Documentation debt | Missing runbooks, outdated READMEs, tribal knowledge | Onboarding pain |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.