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Outdated libraries, unmaintained dependencies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Missing runbooks, outdated READMEs, tribal knowledge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.