---
title: "An A/B Testing Checklist for Code Review"
description: "When an experiment variant shows up in a pull request, here's what an engineer should actually check before approving it — beyond the usual code review bar."
canonical: https://landtest.dev/blog/ab-testing-checklist-for-code-review
last-updated: 2026-10-30
---

# An A/B Testing Checklist for Code Review

> When an experiment variant shows up in a pull request, here's what an engineer should actually check before approving it — beyond the usual code review bar.

An A/B test variant showing up as a pull request needs a few checks beyond a normal code review: how assignment is determined (and whether it's sticky across sessions), whether there's a defined cleanup path for when the test ends, whether the variant meets the same accessibility bar as the control, and whether the statistical method being used is one that's valid to check before a fixed sample size is reached. Skipping these doesn't usually break the build — it breaks the trustworthiness of whatever the test concludes, which is a harder problem to catch after the fact.

## Assignment logic

Check where the split happens (client-side, server-side, edge, deploy-layer) and confirm it doesn't conflict with existing middleware, especially auth. If the assignment is client-side, check for flicker — does the control render before the variant swaps in. If it's meant to be sticky (same visitor sees the same variant across sessions), verify the persistence mechanism actually does that, rather than assuming a cookie check is sufficient without checking its expiry and scope.

## Cleanup path

Ask directly: when this test ends, what has to be deleted, and is that scoped to a single, reviewable change, or scattered across multiple files as inline conditionals? A flag check buried inside a shared component is harder to safely remove later than a variant isolated to its own branch or clearly-delineated code path. This is worth flagging at review time, before the test ships, because it's much easier to fix the structure now than to untangle it after the test concludes and everyone's moved on.

## Accessibility parity

A variant built quickly — especially one built by a non-engineer through a visual editor and then translated into code — can lose contrast ratios, proper heading hierarchy, or ARIA labels that the control page has. Treat this the same as any other accessibility regression: it's not exempt from review because it's "just a test."

## Statistical methodology

This one's not really a code-level check, but worth confirming before the PR merges: is the significance method being used (mSPRT/sequential vs. fixed-sample) one that's valid to check at any point, or does the team have a plan to wait for a pre-committed sample size without peeking? And has sample ratio mismatch been checked as part of the rollout plan? These aren't code review items in the traditional sense, but an engineer reviewing test infrastructure is often the last checkpoint before a flawed methodology ships.

## What "done" looks like

A clean A/B test PR should make clear: where assignment happens, how it's made sticky if needed, what the cleanup path is when the test concludes, and confirmation the variant was checked against the same accessibility bar as anything else shipping to production. A test that clears all four is one that survives scrutiny regardless of what it actually finds.

## FAQ

**Should A/B test variants go through the same code review as any other change?**
Yes — arguably more scrutiny, not less, since a flawed variant or assignment bug can silently invalidate a result that leadership acts on.

**What's the biggest code-review risk specific to A/B tests?**
Assignment logic that conflicts with existing middleware (especially auth), and cleanup paths that aren't scoped cleanly enough to remove later without hunting through the codebase.

**Does accessibility review apply to test variants?**
Yes — a variant that regresses contrast, heading structure, or ARIA labels is a real regression if it ships, independent of how it performed on the conversion metric.

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Related: [A/B testing for growth teams that need engineering to trust the results](/blog/ab-testing-for-growth-teams) · [A growth team's guide to SRM and mSPRT](/blog/growth-teams-guide-srm-msprt) · [Landtest for A/B testing](/ab-testing)