How to Review AI-Generated Refactors: A Repeatable Falsification Loop

Can you trust an AI-generated refactor without re-doing the work yourself? Yes — if you stop treating the diff as a patch to approve and start treating it as a hypothesis to falsify. The workflow below is a repeatable, scripted loop (snapshot behavior → generate in isolation → mechanical falsification → human review) that shrinks a 400-line AI diff into something your judgment can actually handle.

Last month I wrote about building a free evaluation suite for comparing AI coding models. That suite answers which model to trust. This article is about the next question, which turned out to be harder in practice: once you've picked a model, how do you review its output on real refactoring work without either rubber-stamping it or re-doing the work yourself?

The failure mode I kept hitting: an AI-generated refactor looks clean, passes a quick skim, and then breaks an edge case three files away. Manual review of a 400-line diff is exactly where attention fails. So I built a small, repeatable loop that treats every AI refactor as a hypothesis to falsify, not a patch to approve.

The loop in one picture

1. Snapshot behavior before the refactor. Capture the current behavior as executable evidence (tests + a few golden outputs), not as a vague memory of "it worked."
2. Generate the refactor in isolation. One concern per request, with an explicit diff scope.
3. Run mechanical falsification. Tests, type checks, and a diff-shape check that flags changes outside the declared scope.
4. Only then do human review. By this point the diff is smaller and already survived the cheap checks.

The key idea is that steps 1–3 are scripted and identical every time, so the human judgment in step 4 is spent where it actually matters. This mirrors the classic advice behind characterization tests: pin down what the code observably does before you restructure it, because a refactor by definition must not change behavior.

Artifact: the falsification script

This is the core of the workflow. It's deliberately boring shell — no framework to maintain:

#!/usr/bin/env bash
# falsify-refactor.sh — run after applying an AI-generated refactor
set -euo pipefail

SCOPE_FILE="${1:-.refactor-scope}"
BASE_BRANCH="${2:-main}"

echo "== 1. Test suite =="
npm test -- --silent || { echo "FAIL: tests"; exit 1; }

echo "== 2. Type check =="
npx tsc --noEmit || { echo "FAIL: types"; exit 1; }

echo "== 3. Scope check: did the diff stay inside the declared files? =="
CHANGED=$(git diff --name-only "$BASE_BRANCH"...HEAD)
VIOLATIONS=0
while read -r f; do
  if ! grep -qxF "$f" "$SCOPE_FILE"; then
    echo "  OUT OF SCOPE: $f"
    VIOLATIONS=1
  fi
done <<< "$CHANGED"
[ "$VIOLATIONS" -eq 0 ] || { echo "FAIL: scope"; exit 1; }

echo "== 4. Behavior snapshot =="
# Golden outputs captured BEFORE the refactor (see below)
node scripts/capture-behavior.js | diff -u .golden/behavior.txt - \
  || { echo "FAIL: behavior drift"; exit 1; }

echo "PASS: refactor survived falsification"

Before requesting the refactor, I capture the golden behavior once:

git checkout main
node scripts/capture-behavior.js > .golden/behavior.txt

capture-behavior.js is just a script that exercises the module's public API with representative inputs and prints normalized output — a poor man's characterization test for code that doesn't have full test coverage yet.

The scope file is the part people skip, and it's the part that catches the most real damage. When I ask a model to "extract the validation logic from checkout.ts into a pure module," I write down exactly which files may change:

src/checkout.ts
src/validation.ts
src/validation.test.ts

If the model also "helpfully" tweaks an unrelated utility, the script fails loudly instead of that change slipping through inside a big diff.

Where the free model access fits

This loop is generation-heavy: each refactor request is cheap to check but not cheap to produce, and I often want two or three candidate diffs for the same task so I can compare approaches. Running that volume through a paid API adds up fast for what is essentially iterative drafting.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

In my current setup I run the generation step through MonkeyCode, which offers free access to coding models and a free server option for hosting the workflow. That combination matters here for a specific reason: the falsification script is fully automated, so I can point it at the server, batch several candidate refactors, and only look at the ones that survive. The economics of "generate three, discard two" stop mattering when the generation step is free.

The important thing is that the loop doesn't depend on any particular model. If the free tier disappears tomorrow, the script doesn't change — only the generation source does.

Decision table: when a candidate diff is worth human review

After running this loop for a few weeks, I noticed my review decisions collapsed into a small table:

Tests	Types	Scope	Behavior	Verdict
✅	✅	✅	✅	Review properly — worth your time
❌ any	—	—	—	Discard, don't debug the model's diff
✅	✅	❌	✅	Re-scope: ask again with a tighter prompt
✅	✅	✅	❌	Interesting: either the refactor changed semantics, or your golden snapshot was wrong. Check the snapshot first.

The last row is the underrated one. About a third of my "behavior drift" failures were actually stale golden outputs, which means the loop doubles as a cheap audit of how well I understand my own code's observable behavior.

Limitations, honestly

• The golden snapshot is only as good as the inputs you thought of. This is characterization testing, not proof. A refactor can pass and still break an input you never captured.
• It doesn't work for behavior-changing work. This loop is for refactors — semantics-preserving changes. Feature work needs actual tests, not diffed output.
• Shell + git diff is fragile on monorepos with generated files. You'd want path filters and to exclude lockfiles from the scope check.
• Free model access has practical limits. Throughput, context size, and availability can vary, so I keep prompts narrow (one concern per request) rather than shipping whole subsystems at once.

Who should not use this

If your codebase already has fast, high-coverage tests, steps 1 and 4 are mostly redundant — your CI is already the falsification loop, and you just need the scope check. And if your changes are usually behavior-changing features rather than refactors, this whole framing will slow you down.

Try it this week

If, like me, you maintain code with patchy coverage and you're increasingly letting models do the mechanical restructuring, a scriptable falsification step is the difference between "AI-assisted refactoring" and "AI-generated risk." Here's how to start today:

1. Copy the falsify-refactor.sh script above into your repo and adapt the test/type commands to your stack.
2. Pick one low-risk module and write a capture-behavior.js that exercises its public API with 5–10 representative inputs.
3. Run your next AI refactor through the loop — with a scope file, even if it feels bureaucratic. Watch what it catches.
4. If you want the batch-generation part without paying per candidate, MonkeyCode's free model access and free server are one low-friction way to run it — the script above works regardless of what generates the diffs.

What does your review process for AI-generated diffs look like? Drop a comment — I'm especially curious how people handle the scope-creep problem on larger refactors, and I'll fold the best answers into a follow-up post.

Comments

[Luis Cruz]

I appreciate how you've outlined a structured approach to reviewing AI-generated refactors by treating the diff as a hypothesis to falsify, rather than a patch to approve. The use of a scripted loop with mechanical falsification steps, such as tests, type checks, and a diff-shape check, helps to shrink the diff into a more manageable size for human review. The falsify-refactor.sh script you provided is a great example of how to automate these checks, and I'm curious to know how you've found the effectiveness of this approach in catching edge cases and preventing regressions in your own projects. Have you noticed any common patterns or types of issues that this process helps to identify?