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Tech Debt Reduction | Uzera

Use cases / Tech Debt Reduction

Use case

Find the debt.Prove it went down.

Code Health scans every repository with sixteen deterministic checks in six categories: security, complexity and size, duplication and architecture, dead code, testing. Every finding has a severity. Findings flow into the same review loop, close themselves when the code changes, and the backlog per repository shows whether it is going down.

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What Uzera does with the debt

Sixteen checks. Fixed in the loop. A backlog that goes down. Rules so it stays gone.

Code Health finds it, Code Review closes it, Guardrails stops it coming back.

Code Health

Sixteen checks, not opinions

Security: SQL injection, weak randomness, weak encryption, unsafe data loading, hardcoded secrets. Complexity and size: long functions, high complexity, deep nesting, large files, too many parameters, god classes, deep loop nesting. Plus duplicate code, dead code, missing tests and low test ratio.

  • Deterministic, no model in the loop: the same code always gets the same finding
  • Every finding carries a severity: critical, serious or minor
  • Grouped by category, so the biggest piles are obvious

Code Review

Into the same loop

Findings flow to the pull request where the work happens and close themselves when the code changes. Nobody maintains a separate debt backlog by hand.

  • Resolved the moment the code changes
  • No manual triage
  • A human approves the merge

Code Health

The trend

Density and trend per repository, scan after scan. Whether the debt is going down is a number the whole team can see, not a feeling.

  • Per repository, per scan
  • Going down, or not
  • The answer when leadership asks

Guardrails

Rules that stop it coming back

Turn the patterns that created the debt into rules. The agent is held to them as it writes, so the same debt is not re-created next sprint.

  • Plain-English rules
  • Denied before anything is saved
  • One rule set across repositories

How the debt comes down

Scan. Fix. Track.

Three steps, repeated every scan.

Step 01

Scan

Every repository is scanned with sixteen deterministic checks. Findings are named by check, file and line, carry a severity, and are grouped by category so the biggest piles are obvious.

Step 02

Fix

Findings flow into the pull requests your team already opens. Push the fix and the finding closes itself. A human approves the merge.

Step 03

Track

Density and trend per repository, scan after scan. Turn the patterns that caused the debt into rules so it stays gone.

Where it fits

Your team pays down the debt. Uzera finds it and proves it is gone.

Your engineers and their agents make the changes. Uzera finds the debt the same way every scan, puts it in the review loop they already use, and shows the trend so nobody has to argue about whether it worked.
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Before the budget conversation

Questions people ask before a debt sprint.

01How is this different from the linter we already have?

The sixteen checks are deterministic and scored the same way every scan, so the backlog is comparable between sprints. Findings also close themselves when the code changes, so nobody maintains the list.

02Is the debt going down?

Open findings per repository, scan after scan, and how many of them closed because the fix landed. If the first number is not falling, the sprint is not working.

03Will this generate a backlog nobody looks at?

Findings are grouped by category and ranked by severity, and they close themselves when the fix lands, so the list maintains itself.

Governance for your coding agents

Every team adopted AI agents.
Few governed them well.

AI writes your code. You stay in charge.

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