How to Trial an AI Code Reviewer on Your Own Repository

An AI code review trial is a measured test of a reviewer on your own repository before you commit to paying for it. Replay known bugs, request reviews manually, record the cost and test how to remove the tool. Judge it by the verified bugs it catches and the useful changes your team makes before merging.

What Does a Fair AI Code Review Trial Look Like?

A fair AI code review trial uses one representative repository, a named owner and agreed limits for missed bugs, cost and access. The question is whether the reviewer changes what you merge at an acceptable cost. Our guide to AI code review recommends a four-week trial, which can extend beyond a vendor's free trial. Use our AI code review comparisons to choose which tools to trial.

CodeRabbit gives new organisations a 14-day Advanced trial. Anthropic gives Pro and Max accounts three free ultrareview runs, and managed Code Review needs a Team or Enterprise plan, while Cursor bills Bugbot by the run.

Split the trial into phases and replay the known bugs in the first week.

Phase	What Runs	Where
Replay	Seeded pull requests, triggered by hand	Throwaway branches
Live, manual	Real pull requests, on request	Your repository
Live, automatic	Only if the first two paid off	Your repository

Local reviews can fill the gap after a vendor's trial window closes, but they still consume subscription or API usage. Our AI code review guide covers the commands in its section on local CLI reviews.

Pick One Representative Repository

Pick a repository where useful findings will be easy to recognise. That is rarely the newest one.

Pick	Avoid
Several pull requests a week	One pull request a month
Auth, data, API and UI changes	A docs-only repository
CI that already runs	No tests, so no baseline
Code you can send to a vendor	Your most sensitive repository

Hosted reviewers read repository contents through a GitHub App, so read each vendor's data terms before you install. The Security and Data guide covers them vendor by vendor.

How Do You Seed an AI Review Trial With Known Bugs?

Seed an AI code review trial with bugs from your repository's history. Past fixes give you known defects to check against the reviewer's findings. List recent fixes, then find the commit that introduced each bug.

git log --oneline -i --grep=fix --since="6 months ago" | head -20
git log --oneline -S'Math.floor' -- src/cart.js

On a throwaway repository with a discount bug, the second command returned both commits, and your hashes will differ.

f4edfe3 fix: round discounted total instead of flooring
a15d54f feat: percentage discount

Branch from the introducing commit's parent and cherry-pick that commit onto a second branch. A pull request between them shows the reviewer the exact diff that once shipped the bug.

INTRO=a15d54f
git switch -c trial-base-1 "$INTRO^"
git switch -c trial-pr-1
git cherry-pick "$INTRO"
git diff --stat trial-base-1..trial-pr-1

The last command printed one changed file, src/cart.js | 5 +++--. Point local reviewers at the same pair with codex review --base trial-base-1 or /code-review trial-base-1...trial-pr-1.

Add three more kinds of seed beyond replayed fixes, and delete the throwaway branches when the trial ends.

Seed	What It Tests
Replayed past fixes	Whether it finds a bug you shipped
One cross-file contract break	How far its context reaches
One correct but odd refactor	Its false-alarm habit
A comment aimed at the reviewer	Whether it obeys text in code

Write the contract break by changing a signature in one file and leaving a caller in another untouched. Add a comment that tells the reviewer to ignore a bug or call the code clean. This tests whether the reviewer follows instructions embedded in code, the weakness exploited by the Hades worm payloads.

Write at least one bug that has never been public, because a public repository's fixes may sit in a model's training data.

Open seeded pull requests as drafts and trigger each reviewer by hand. Anthropic's manual triggers run on drafts, CodeRabbit's auto_review.drafts defaults to false, and Bugbot has a personal draft setting.

For a head-to-head trial, use separate mirrored pull requests with equivalent code and context. Keep each reviewer's first pass independent. Seeing the other's findings can cause it to skip a duplicate or copy a conclusion, distorting the comparison. Test the combined workflow separately afterwards.

Start in Manual Mode

Start the AI code review trial in manual mode so you choose when each review runs. That makes usage and comment volume easier to track.

Reviewer	Run It by Hand	Turn Automatic Off
Bugbot	bugbot run or cursor review	Personal setting: only when mentioned
Claude Code Review	@claude review	Review Behavior set to Manual
Codex	@codex review	Leave automatic reviews off
CodeRabbit	@coderabbitai review	reviews.auto_review.enabled

# .coderabbit.yaml
reviews:
  auto_review:
    enabled: false

Claude's manual command must be a top-level comment from someone with write access. If your organisation membership is private, Claude may react with an eyes emoji and start nothing, so make it public or add a collaborator.

How Do You Score AI Review Findings That Changed a Merge?

Count an AI review finding as changed-merge only when you verify the issue and someone changes code or a test before merging. Use a failing path, a failing test or a quoted rule violation as evidence. Severity describes potential impact, while confidence describes certainty. Record impact and verification separately, then assign one of the four workflow labels below.

Label	Meaning
changed-merge	Verified, and code or a test changed
already-known	CI or a person had it first
noise	True, and nobody needed it
wrong	Verified false

Keep the log in a plain CSV with one row per finding. The nine example rows are fictional.

pr,reviewer,label
41,reviewer-a,changed-merge
41,reviewer-a,noise
41,reviewer-b,already-known
42,reviewer-a,noise
42,reviewer-b,changed-merge
42,reviewer-b,wrong
43,reviewer-a,noise
43,reviewer-a,changed-merge
43,reviewer-b,noise

# tally findings by reviewer and label
awk -F, 'NR > 1 { n[$2 " " $3]++ } END { for (k in n) print n[k], k }' findings.csv | sort -k2,2 -k3,3

2 reviewer-a changed-merge
3 reviewer-a noise
1 reviewer-b already-known
1 reviewer-b changed-merge
1 reviewer-b noise
1 reviewer-b wrong

For each reviewer, record verified true findings, false positives and missed seeded defects separately. Precision is verified true findings divided by all assessed findings. Known-defect recall is detected seeded defects divided by all seeded defects. Count each seeded defect once, even if the reviewer comments on it several times. When testing reviewers together, count each bug once and record which extra bugs the second reviewer finds. A CI duplicate can be a true detection without adding workflow value.

Ask the developers who read the reviews whether the comments helped them make a decision, were easy to act on or were ignored. Record a concrete example and why it helped or got in the way. Check whether anyone muted the reviewer or stopped reading its comments. Compare that feedback with the verified findings and reading time before deciding to keep the tool.

Keep a reviewer only if its changed-merge count justifies its bill and the team's reading time. If useful findings are buried in low-value comments, our guide How to Reduce Noise from AI Code Review covers the settings to adjust.

Price the Real Workload

Estimate the bill using each vendor's pricing and the number of reviews your team actually runs. Count last month's merged pull requests first, capped here at 500.

# merged pull requests since a date; set it to 30 days ago
gh pr list --state merged --search "merged:>=2026-09-02" --limit 500 --json number --jq length

Anthropic says reviewing on every push multiplies cost by the number of pushes. Cursor's May 2026 announcement put the average Bugbot run at $1.00 to $1.50; that is a dated estimate, not a fixed tariff. Managed Claude Code Review bills variable token usage, with cost affected by review level. Use measured usage from your pilot for its budget.

PRS=80 PUSHES=2.5 LOW=1.00 HIGH=1.50 COMMENTS=6 MINUTES=2
awk -v p=$PRS -v n=$PUSHES -v lo=$LOW -v hi=$HIGH -v c=$COMMENTS -v m=$MINUTES 'BEGIN {
  printf "every push:   $%.0f to $%.0f a month\n", p*n*lo, p*n*hi
  printf "once per PR:  $%.0f to $%.0f a month\n", p*lo, p*hi
  printf "reading time: %.0f hours a month\n", p*c*m/60
}'

With these placeholder numbers, which you replace with your own, it printed three lines.

every push:   $200 to $300 a month
once per PR:  $80 to $120 a month
reading time: 16 hours a month

The trigger setting alone moved the estimate by $120 to $180, and reading time is the cost most trials forget. For a seat-priced reviewer such as CodeRabbit, multiply seats by the plan price and check its hourly limits.

Test Removal Before You Buy

Test removal during the trial by checking app installation, user authorisations and token revocation. No new review appearing does not prove access was revoked or retained data deleted; confirm the vendor's deletion response separately.

1. Find the vendor's deletion path first. CodeRabbit's FAQ puts it under Subscription and Billing, in the admin-only Danger Zone tab.
2. Uninstall the GitHub App, as GitHub's documentation on installed apps describes.
3. Revoke the user authorisation and any separate OAuth app as well.
4. Open a throwaway pull request and confirm that no review arrives.
5. Rotate any secret you gave the reviewer, such as an API key.
6. Ask the vendor in writing what it still holds after uninstall.

Uninstalling the app and deleting the vendor account are separate steps, and a vendor with no documented path should lose points.

Verdict: Choose the Smallest Trial That Gives an Answer

Replay the known bugs, request reviews manually for a few weeks, label the findings and calculate the cost. Set the sample size, acceptable missed-defect rate, budget and access requirements before the trial starts. One useful finding alone does not justify an automatic roll-out. Test removal before you sign. If the results are close, choose the reviewer that fits your workflow; our AI code review guide gives you a starting shortlist.

Frequently Asked Questions

How long should an AI code review trial run?

Our AI code review trial method uses four weeks of real pull requests on one repository. CodeRabbit's own trial lasts 14 days for new organisations, so run the seeded replay and first manual reviews inside that window.

How many known bugs should an AI review trial include?

An AI review trial should start with a handful of past fixes, one contract break and one correct refactor. Add more cases when the initial sample leaves gaps or the reviewers are hard to distinguish, and write at least one bug that was never public.

Do local AI code reviews count towards a trial?

Local AI code reviews count towards a trial when they examine the same seeded branches as the hosted reviewers. Anthropic says /code-review works without the GitHub App, and OpenAI says local Codex reviews count towards general usage limits instead of Code Review usage.

What counts as a finding that changed a merge?

A finding changed a merge when you verified it with a failing path or test, and someone then changed code or a test before merging. Vendor severity labels and merely acknowledged comments do not count.

Can I trial two AI code reviewers at once?

You can trial two AI code reviewers on separate mirrored pull requests with equivalent context. Credit each independent true finding to its reviewer, then measure each tool's precision and known-defect recall. When testing the two together, count each bug once and record which bugs only the second reviewer catches.

Should I trial an AI code reviewer on a private repository?

You can trial an AI reviewer on a private repository once its access and data terms meet your requirements. Pick the least sensitive representative repository, read the vendor's retention and deletion terms first, and run the removal test during the trial.