No. If you can't trace the consequences of a change through the system, your approval is a guess dressed up as a sign-off. A partial review can still be a useful contribution, but it needs to be labelled as one. Approving code you only half-understand gives the author, and everyone who reads the history later, false confidence about how to do a code review well.
This situation comes up constantly. The PR touches a service you've never worked in. It depends on a library you've only seen in passing. The ticket description is two sentences and the diff is four hundred lines. You're not incapable; you're just missing context, and that gap is exactly where bugs and security holes live.
What it means to only partially understand a pull request
Partial understanding isn't binary. There are at least three layers to reading a PR, and most reviewers who struggle are strong on the first and weak on the other two.
The first layer is line-by-line syntax and immediate logic: what each function does in isolation. The second is runtime behaviour: what the change does to the system once it's deployed, how it interacts with other services, and what happens under load or failure. The third is intent: whether the code actually solves the problem the ticket described.
A reviewer who only has the first layer is working without enough information. That gap is where the dangerous approvals happen, because the code looks fine locally and the reviewer doesn't have the system context to see what it breaks.
Common causes are straightforward. The diff touches an auth or caching layer you don't own. A third-party library is doing something you haven't seen before. The business requirement behind the change was never written down. None of these make you a bad reviewer; they make you a human with a reasonable knowledge boundary.
What you can write for a good review when your understanding has limits
The most useful thing you can do is name exactly where your understanding runs out. That's not an admission of failure. It tells the author and any other reviewers where scrutiny is still needed.
Concrete options work better than vague caveats. A comment like "I've reviewed the data-access logic but I can't trace how this change affects the auth middleware, someone who owns that layer should sign off" is a genuine contribution. It's honest, it's actionable, and it doesn't block the PR unnecessarily.
What to avoid is a "looks good to me" on code you only partially read. That approval carries the same weight as a fully informed one. The author may ship based on it. Someone debugging a production issue six months later will read that approval and assume the logic was verified.
Partial reviews are legitimate when they're labelled. The problem isn't the gap in knowledge; it's the gap disguised as full confidence.
How to do a code review on code you don't fully own
Start with the ticket, not the diff
Reading the diff first means you're judging code without knowing what problem it was supposed to solve. Open the linked issue or spec first. Confirm you understand the intended behaviour before you read a single line of implementation.
If there's no ticket, that's a signal worth flagging in itself. Ask for a description of what the change is meant to accomplish before you proceed.
Work through the diff in a deliberate order
Read tests first. Tests describe intended behaviour and reveal what the author thought was worth verifying. If the tests are sparse or missing, you've already found something to comment on before you've read the main implementation.
Then read the implementation against those tests. Where the implementation does something the tests don't cover, that's a gap. Where the tests cover an edge case the implementation ignores, that's a bug candidate.
For PRs that remove code entirely, the review discipline is different enough to treat separately. Reviewing a pull request that deletes code requires checking what still depends on the deleted paths before you can form an opinion.
Flag what you can verify and what you can't
Split your comments into two groups. The first group is things you've checked and have an opinion on. The second group is things that need a second reviewer with deeper context. Write both groups down. A comment that says "I've verified the query logic but the caching behaviour here is outside my knowledge of this service" is more useful than silence.
Is code review obsolete now that AI tools can scan a diff?
AI tools catch a different class of problem than a human reviewer does. They complement each other rather than one replacing the other.
AI linters and static analysis tools are strong on known anti-patterns, common vulnerabilities, and style violations. They have no context about team conventions, product decisions, or why a previous approach was retired. A human reviewer can write "this logic is correct but we removed this pattern six months ago because it caused race conditions under load." No static tool produces that comment.
The real risk is that AI-generated summaries make reviewers feel like the work is done when only the surface has been scanned. A tool that flags no issues in a PR that changes rate-limiting logic isn't telling you the logic is correct. It's telling you nothing matched its known patterns. Reviewing PRs that touch rate limiting or throttling requires understanding the system's behaviour under load, which is exactly what static analysis can't model.
Treat automated tools as a first pass that raises the floor, not a ceiling on what review can find.
What the 80/20 principle means for code review
In software, the 80/20 observation is that a small fraction of the codebase, typically core business logic and data-access paths, accounts for the majority of bugs and regressions. The principle comes from Vilfredo Pareto's general observation about uneven distributions, applied loosely to software quality over decades of practice.
Applied to code review, it means directing the most careful reading at high-change-frequency files, public interfaces, authentication paths, and anything that handles money or user data. It does not mean skimming 80 percent of a PR and reading the rest carefully. It means allocating attention in proportion to risk, and being explicit about which sections got a light read.
A practical starting point: before you open the diff, identify the two or three files that carry the most consequence if they're wrong. Start your deep reading there. If a PR introduces a new external API integration, that surface deserves more scrutiny than a change to a utility function. The checklist for new external API integrations is a useful reference for where the highest-risk decisions tend to cluster.
How to build the judgment to know what you're missing
The hardest part of code review isn't finding bugs you recognise. It's developing a sense for the bugs that aren't obviously there. That only comes from repeated practice with feedback.
Reading code in production codebases is useful, but it doesn't tell you what you missed. You need graded repetition: reviewing a pull request, committing to what you'd flag, then finding out what actually mattered. Without that feedback loop, you can spend years doing reviews and still have blind spots in areas you think you know well.
Stack-specific fluency matters more than many developers expect. The security surface on a React front end is different from a Django API or a Spring Boot service, and weak-spot patterns don't transfer cleanly across stacks. The common mistakes in a React PR, for example, are specific enough that reviewing React pull requests is its own skill set.
Goodcatch is built for exactly this kind of practice. It presents real pull requests across the stacks you work in, grades you on what you caught and what you missed, and tracks your weak spots over time. The free trial runs in-browser with no account required, so you can test a track before committing to anything. Plans start free with three graded reviews; unlimited access across all difficulty tiers is $19 per month.
If a code review surfaces a gap in your understanding of a particular stack, that's the signal to go deeper in that area rather than approve and move on.
Frequently asked questions
What is the best way to write code comments in a review?
Be specific about what you've verified and what you haven't. A comment that names the exact concern, such as "this will fail if the user record is null and the caller doesn't check the return value", is more useful than "is this safe?". Separate observations from requests: label nitpicks as nitpicks, and label blockers as blockers so the author knows what must change and what's optional.
What can I write for a good review when I'm not sure about something?
Name the uncertainty directly. Write what layer of the code you've checked and what you haven't. Phrases like "I've reviewed the input validation but not the downstream effect on the job queue" give the author and other reviewers precise information about where scrutiny is still needed. That's more valuable than a qualified approval.
Does code review skill plateau or keep developing?
It depends on how you practice. Reviewers who only review code they already understand well tend to plateau because they're never getting feedback on what they miss. Reviewers who deliberately work in unfamiliar areas, track what they catch versus what they don't, and study stack-specific patterns tend to keep improving. The skills that compound over a career are different from the ones that level off quickly, and the distinction is worth understanding explicitly.
Start a free Goodcatch trial and find out what your code reviews are missing.