PR Reviews as Interview Tests

A software interview is usually split into separate boxes: one round for coding, one round for system design, one round for “communication,” and sometimes a puzzle about binary trees that never shows up on the job. A pull request review collapses those boxes into one artifact: a candidate reads real code, spots risks, and explains what should change. (github.blog) That idea is getting attention because a pull request is already how many engineering teams make decisions. GitHub describes its own take-home process as a pull request with code changes and comments, which means the interview format mirrors the tool many developers use at work every day. (github.blog) A pull request is just a proposed code change with a written explanation attached, like handing a teammate a marked-up draft instead of asking them to solve a riddle on a whiteboard. Good reviewers are expected to ask about edge cases, naming, tests, performance, and whether the change actually solves the user problem it claims to solve. (github.blog) That makes the format useful for judging product sense, which is the habit of asking whether the software behavior matches what a customer or teammate actually needs. GitHub’s guidance on pull requests says authors should be explicit about whether they want critique on design, technical approach, or even copy, because a code change often includes product decisions hiding inside technical ones. (github.blog) It also surfaces architecture judgment without forcing the candidate to invent a giant system from scratch in 45 minutes. When engineers review a pull request, they can point to one concrete trade-off at a time: whether a shortcut creates future maintenance cost, whether a dependency is worth adding, or whether a decision should be written down in an architecture decision record. (github.blog) The communication signal is unusually strong because the candidate has to write comments another engineer could actually act on. GitHub’s code review guidance treats review as more than bug hunting, and that matters in interviews because hiring teams can see whether feedback is precise, calm, and prioritized instead of vague or combative. (github.blog) This shift is also a reaction to tools like large language models making rote coding easier to outsource. Karat wrote in 2025 that interviews will need to focus less on memorizing syntax and more on problem-solving, systems thinking, and handling edge cases as artificial intelligence coding assistants become common inside real engineering teams. (karat.com) A review-based interview is harder to fake because the candidate has to exercise judgment in context, not just paste a working answer. It is one thing for a model to generate a function that passes tests; it is another to explain why a migration is risky, why a user-facing error message is confusing, or why a seemingly small change will be painful six months later. (github.blog) There is also a practical reason companies like this format: pull requests are where engineering time gets spent. Stack Overflow cited LinearB data showing about one million pull requests, four million review cycles, and roughly five days to review and merge, which means review quality is not a side skill but a daily operating skill. (stackoverflow.blog) The catch is that a pull request review only works if the rubric is tight. Stack Overflow’s interview science piece argues that unstructured interviews are weak predictors, so a review exercise still needs clear scoring for what counts as a strong comment, a missed risk, or a good product question. (stackoverflow.blog) GitHub’s version hints at how companies can do that without turning the exercise into guesswork. Its take-home process anonymizes the pull request, strips identifying details, and lets reviewers focus on the code changes and comments, which makes the interview feel less like theater and more like the work itself. (github.blog)