AI Refactoring · 14 min read

AI-assisted legacy refactoring: how to increase test coverage safely

AI-assisted refactoring is the disciplined use of AI tools — guided by senior engineers — to analyze, document, test, and incrementally improve legacy software without disrupting the product roadmap. Used correctly, it raises meaningful test coverage faster than manual work alone. Used carelessly, it accelerates technical debt.

By Pernix Engineering TeamMay 15, 2026

TL;DR

AI is excellent at proposing tests, generating docs, and mapping dependencies. AI is dangerous at autonomous refactoring, verifying behavior, and making architectural decisions. Always pair AI with senior engineer review. Always raise coverage only in areas you’re about to modify.

What is AI-assisted refactoring?

AI-assisted refactoring uses LLMs and code-aware AI agents to accelerate the human engineer's work. The engineer remains the decision maker. AI handles the time-consuming, mechanical parts: reading thousands of lines to understand context, drafting test cases, proposing rename refactors, generating documentation skeletons, flagging suspicious patterns.

What it is NOT: it is not vibe-coding your way through legacy code. It is not letting Copilot autocomplete its way across critical systems. It is not generating a new module by prompt.

What AI should never do alone

Never let AI commit code unsupervised.

Every AI-generated change needs human review before it lands in main. No exceptions.

Never let AI write the spec.

The specification is the contract. Humans define what success looks like. AI executes against the spec, never defines it.

Never let AI delete tests it doesn't understand.

Failing tests in legacy code often guard behavior that isn't documented anywhere. Deleting them silently is how systems break in production six weeks later.

Never trust AI tests without reading them.

AI often writes tests that pass by mirroring the code's current bugs. A test that codifies a bug is worse than no test.

Never let AI choose the architecture.

AI tends toward the most common pattern in its training data. That pattern may be terrible for your specific constraints.

How to prioritize high-risk areas

Test coverage is a tool, not a goal. The goal is change safety in the areas you'll touch this quarter. Here’s the priority order we use at Pernix:

Code paths in this sprint's tickets — if you're modifying it, test it first.
Code paths in the next 2 sprints — look at your roadmap, work backwards.
Code paths touched 10+ times in the last 6 months — high churn = high risk.
Code paths with 0% coverage AND high complexity — cyclomatic complexity above 15.
Everything else — defer.

A 40% coverage target in the active areas beats a 70% coverage average across dead code.

Raise coverage without slowing delivery

The "modernization tax" is what kills teams: spending all sprint capacity on tech debt and shipping nothing new. The fix is a fixed allocation model:

60–70% feature work — your normal product roadmap continues.
20–30% modernization work — tests, refactors, docs, in areas adjacent to features being shipped.
5–10% spike work — exploring high-risk unknowns before committing.

Every PR that adds a feature also adds tests around what it touches. The codebase gets safer at exactly the rate the product moves. No separate "modernization sprint" ever ends — it's continuous.

Mini example: HelloCollege

"They are extremely flexible and understanding, which has been critical for us as a start-up with often changing cash flow and needs." — Andrea Emmons, CEO of HelloCollege

HelloCollege came to Pernix with an MVP needing custom integrations, refactoring, and ongoing maintenance — under tight budget pressure. We applied this exact playbook: AI-generated test scaffolds reviewed by senior engineers, characterization tests around the most-changed modules first, and incremental refactoring inside feature PRs. The engagement helped reduce projected servicing costs while continuing to ship new features through the entire engagement.

Where to start

Want to know which parts of your codebase are safe to refactor with AI? Start with the Legacy Code Risk Assessment — a free, 30-minute self-administered framework that produces a prioritized map of where AI-assisted refactoring is appropriate.

How we use AI safely

We never send client code to public AI tools without explicit written approval.
We use approved tools and tenant-scoped access controls.
Human engineers review every AI-generated output before it lands in your codebase.
AI does not define architecture or specifications — humans do.
Client IP remains client-owned at all times.
Sensitive code access is handled through agreed security policies and NDAs.

Frequently asked questions

What is the difference between AI-assisted refactoring and vibe-coding?

AI-assisted refactoring uses AI under senior engineering supervision to analyze, document, and propose changes — with human review at every step. Vibe-coding is prompting AI to generate code without specification, review, or tests. One raises code quality incrementally; the other accelerates technical debt while appearing to make progress.

How do we raise test coverage without slowing down feature delivery?

Focus coverage only on code you are about to change. Write tests for the specific paths in this sprint's tickets, not global coverage as a goal. At Pernix we target 60-70% of engineering capacity for feature work and 20-30% for modernization running in parallel — so the codebase gets safer at the same rate the product moves.

Can AI write tests for legacy code that has no documentation?

AI can propose tests based on reading the code, but the proposals must be reviewed by senior engineers who understand the business context. AI often mirrors existing bugs in tests — a test that codifies a bug is worse than no test. Every AI-proposed test needs human verification before being committed.

How long does it take to see meaningful results from AI-assisted refactoring?

In a 14-day sprint, you can expect new test coverage in your highest-risk area, a risk map, and documentation on the refactored section. Meaningful coverage improvement across a full codebase typically takes 8-16 weeks of consistent, incremental work running alongside normal feature delivery.

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