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A code review interview may ask you to inspect an existing pull request and explain what you would tell a teammate—not write new code. In one account published by DEV Community author jidonglab on September 8, 2026, the exercise is a 30-minute review of a 180-line change. Its purpose, as the author describes it, is to see how candidates gather context, rank risks and communicate under time pressure.
What is a code review interview?
It is an interview exercise in which you examine a proposed code change and talk through your review. In jidonglab’s example, the candidate reads and narrates the diff, asks questions about its purpose, and decides which findings should block a merge. The candidate does not write code during the exercise.
The author says he adopted this format three years before his September 2026 post and had not returned to algorithm questions. That is his account of his own interviewing practice, not evidence that review interviews are more predictive or effective than algorithm exercises generally.
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What the example PR contains
The fictional 180-line pull request adds an endpoint for resending a verification email. It includes a route handler, a service class, a database migration and two tests that pass. The planted issues below belong to that example; they are not a universal template for interview exercises.
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How should you approach the review?
Start by establishing what the change is supposed to do and who can use it. Then trace the behavior that matters most, including what happens on repeated requests and during deployment. Rank findings by their likely impact rather than trying to maximize the number of comments.
- Clarify the goal and exposure. Ask what user or system behavior the PR is intended to change and whether the endpoint is user-facing. Confirm relevant assumptions before treating a behavior as a defect.
- Look for high-impact failure modes. Follow inputs, responses, authorization and repeated requests. Consider whether the change leaks information, enables abuse or behaves incorrectly over time.
- Check operational context. For schema changes, consider how old and new application instances coexist during a rolling deployment.
- Prioritize findings. Separate a risk that should block merging from a worthwhile improvement or a low-impact style observation.
- Make each comment actionable. Describe the concrete failure mode and suggest a way to reproduce or verify it.
This is a practical approach consistent with the author’s example rubric, not a universal or validated hiring standard.
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What issues appear in the example pull request?
Jidonglab orders the six planted findings by potential production damage. The ranking is useful for understanding the exercise: several issues concern security or behavior across time, while others are comments rather than automatic blockers.
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| Finding | Why it matters in the example | Review priority in the author’s framing |
|---|---|---|
| User enumeration | An unknown email address receives 404 no such account, while an address tied to a real account receives 200 sent. The different responses reveal which addresses have accounts. |
Highest-impact finding in the author’s ordering; identify the information leak and ask how responses can avoid exposing account existence. |
| Unthrottled, user-controlled recipient | Requests can target arbitrary email addresses, creating potential for abuse of the sending endpoint. | High priority: examine who can trigger sends, which recipients they can specify and what limits apply. |
| Sliding rate-limit lockout | The limiter rewrites lastAttemptAt even when it rejects a request. Rejected attempts can therefore move the window forward and keep a locked-out user locked out. |
High priority: reason through repeated rejected requests, not just a single attempt. |
| Unsafe migration timing | The migration drops verification_token while older instances may still read it during a rolling deployment, causing errors until deployment finishes. |
Important operational risk: consider the overlap between application versions and the database schema. |
| Bounded N+1 query | The example includes a real N+1 issue, but the author says its scope is bounded. | Worth commenting on, but not an automatic merge blocker in this fixture. |
| Style bait | A leftover console.log and inconsistent naming are presented as low-value distractions likely to be caught by linting. |
Do not let minor style observations displace higher-impact review work. |
What separates strong candidates in this round?
In the author’s rubric, the key distinction is prioritization, not defect count. He also considers whether candidates ask about the PR’s goal and audience, explain findings clearly, and avoid spending disproportionate time on style nits. These are the author’s observations from his own interviews; the account gives no candidate-behavior denominator, hiring outcomes or independent validation.
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- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
A useful finding connects a specific condition to a consequence. For example, rather than saying only that a rate limiter looks wrong, explain that rejected attempts update the timestamp and can extend the lockout, then propose testing consecutive rejected requests. That gives the team something concrete to investigate.
It is also reasonable to state uncertainty. If the intended behavior or deployment process is unclear, ask rather than silently assuming. Context gathering is part of the review, not a detour from it.
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How do you practice for a code review interview?
The author suggests choosing an unfamiliar, already-merged pull request from an open-source project. Set a 15-minute timer, review it aloud before reading the discussion, and then compare your blocking findings with the maintainer’s comments.
- Pick a merged pull request whose purpose and code are unfamiliar to you.
- Start a 15-minute timer and explain your review aloud as if speaking to a teammate.
- Record the findings you believe should block the merge separately from non-blocking comments.
- After the timer, read the pull request discussion and compare your priorities with the maintainer’s comments.
The drill is intended to practice technical analysis and narration. The author does not provide evidence that it improves interview outcomes.
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How does this differ from an algorithm exercise?
A review exercise observes how you read a change, seek context, judge risk and communicate findings. An algorithm exercise centers on solving a coding problem. Which format is more relevant depends on the role and on whether the interview gives candidates realistic context and uses a consistent rubric. Jidonglab favors review interviews for his own process, but his account does not establish that either format predicts job performance better.
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