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Dependency debt Outdated libraries, unmaintained dependencies Security vulns

Documentation debt Missing runbooks, outdated READMEs, tribal knowledge Onboarding pain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