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Code Review Process in 2026: 6 Steps Including AI-Assisted Review
Key Takeaways
A consistent code review process rests on six steps, and AI-assisted coding makes small pull requests, a shared checklist and a human approval more necessary.
- Keep pull requests under 400 changed lines. A Cisco Systems study cited by SmartBear found defect detection diminishes beyond that, and AI assistants make large diffs cheap to produce.
- A written checklist makes every reviewer check the same things.
- Linters and static analysis should handle style and known bug patterns. AI reviewers add a first pass, but a human should always give the approving review.
- Good feedback names the problem and suggests a fix.
- Labels such as "nit:" and "blocking:" show which comments are optional and which must change before merge.
- Track five metrics monthly and change one rule at a time. A first review regularly taking over 48 hours is a warning sign.
Start with the 400-line limit and the checklist, then add automation and review metrics.
A code review process works when every reviewer checks the same things at a predictable speed. On many teams, one reviewer argues about naming while another approves a very large pull request without reading it. AI coding assistants widen that gap. Developers can now produce code faster than teammates can read it carefully. This guide gives tech leads and developers six steps they can adopt as a team working agreement, with a size threshold, a review checklist, the split between human and AI reviewers, feedback wording and rules for acting on review metrics.
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Why code reviews matter more in AI-assisted codebases
Code review is now the main point where a person checks code that an assistant may have written in seconds. The DORA 2025 report, based on a survey of nearly 5,000 technology professionals, found that AI adoption among software development professionals had reached 90%. Trust lags behind. Only 24% of respondents said they trust AI a great deal or a lot, and DORA's summary of the research is that AI improves throughput but often at the cost of stability when a team's foundation is weak.
That foundation is usually older than new hires expect. An Arbisoft engineer's account of real production codebases described a 15,000-line file of validation conditionals, a React 16 app built from class components wrapped in stacks of higher-order components, untyped JavaScript with no working autocomplete, and five API calls sharing one Redux loading state, which made components render 48 times as users moved back and forth. Most codebases are years old. Every new change, typed or generated, lands on top of that history.
Our view is that review is where technical debt either grows or shrinks. A reviewer who asks whether a change leaves the touched code easier to change protects codebase health in small, regular steps. For a large legacy file, ask for one extraction per pull request; our post on refactoring large React components with divide and conquer shows that approach.
Step 1. Keep pull requests under 400 lines
Small pull requests get more accurate reviews. SmartBear's guide "Best Practices for Code Review" reports that a study of a Cisco Systems programming team found developers should review no more than 200 to 400 lines of code at a time, and that beyond 400 lines the ability to find defects diminishes. The same guide advises against reviewing for more than 60 minutes at a time.
Treat 400 changed lines as the upper limit. Anything larger is a signal to split the work. If a branch fixes a bug and also restyles part of the UI, open two pull requests.
The limit matters more with AI assistants, because they make large diffs cheap to produce. Generated tests and scaffolding count toward the total. Our comparison of production-ready code and vibe-coded prototypes lists the problems that slip through when generated code goes unread, including duplicated logic and weak error handling.
Step 2. Write code review guidelines and a checklist
A written checklist makes reviews consistent. Every reviewer checks the same list, and authors know what will be checked before they ask. The table below works as a starting point for a pull request template.
| Area | What the reviewer checks |
|---|---|
| Correctness | The change does what the ticket says, including edge cases such as empty or null input |
| Readability | A teammate can follow the code without the author explaining it |
| Tests | New or changed logic has tests, and they pass |
| Security | No secrets in code; new inputs and libraries add no obvious risk |
| Performance | No clear waste, such as a database query inside a loop |
| Documentation | Comments, README or API docs change when behavior changes |
| Codebase health | The touched code is no harder to change than before |
Style and naming rules belong in linter configuration, where a machine enforces them. Our guide to writing clean, maintainable Python code shows what those rules look like for Python teams.
Authors have work to do before asking for a review. They self-review the diff, write a description of what changed and why, confirm that all checks pass, and mark which parts an AI assistant generated. The team also agrees on the number of approvals per pull request and on who reviews which areas, such as front end, back end and APIs. We suggest a first response within 24 to 48 hours.
Step 3. Automate routine checks and use AI reviewers for a first pass
Machines should catch formatting problems and known bug patterns, which leaves people free to judge design and intent. Linters such as ESLint for JavaScript, Pylint for Python and RuboCop for Ruby flag style issues and common errors. SonarQube reviews each pull request for bugs, vulnerabilities, security hotspots and code smells, and its quality gates can block a merge. DeepSource checks every pull request for bugs and security vulnerabilities, pairing more than 5,000 rule-based checks with an AI review agent. Qlty covers linting, formatting, duplication, complexity and security. GitHub Actions or GitLab CI pipelines can run these checks on every pull request or merge request.
AI code reviewers add a fast first pass. GitHub Copilot code review, for example, comments on a pull request and suggests fixes the author can apply in a couple of clicks. By default, its reviews do not count toward required approvals. Copilot is also not guaranteed to spot every problem, and it sometimes makes mistakes. In our view, AI comments are suggestions, and a human should always give the approving review.
| Reviewer | Catches well | Misses | Best for |
|---|---|---|---|
| Linters and static analysis | Style, formatting, known bug and security patterns | Intent, design, business rules | Blocking merges on rule violations |
| AI code reviewer | Likely bugs and missing checks, with suggested fixes | Some real issues; it sometimes makes mistakes | A first pass before a human opens the pull request |
| Human reviewer | Fit with requirements, design, maintainability | Repetitive style issues, slowly and inconsistently | Approval, risk decisions and knowledge sharing |
Tool choice follows the languages and frameworks a team runs, which our guide on how to choose the right tech stack for your custom software project covers.
Step 4. Give specific and respectful code review feedback
Good review comments point at the code, name the problem and suggest a fix. "This is wrong" gives the author nothing to act on. "This function may return null when the user has no orders. Can we add a fallback here?" names the risk and proposes a change in two short sentences.
Questions suit judgment calls. "Could we make this loop a map function for readability?" leaves room for the author to explain the choice. Labelling severity helps too: a "nit:" prefix marks an optional suggestion and "blocking:" marks something that must change before merge.
Praise counts as feedback. Pointing out a clean abstraction or a well-written test shows the team what good looks like. The most common mistake is asking for rewrites of working code to match personal taste; if the checklist does not cover it, it is a suggestion at most.
Step 5. Build a review culture where people can admit mistakes
Reviews only surface problems when people feel safe asking questions and admitting errors in public comments. Google's team research, "Team dynamics: Five keys to building effective teams" on Think with Google, found psychological safety was the most important of the five dynamics. The same article says psychologically safe teams accelerate learning and innovation by acknowledging mistakes and exploring new ideas.
- Pair senior and junior developers on reviews, and ask juniors to review senior engineers' code as well.
- When a bug reaches production, update the checklist or tests instead of naming the reviewer who missed it.
- Thank people who catch problems in your own pull requests.
- Mention good code by name in review comments.
Silence is the warning sign. If junior developers never comment, the process is teaching them to stay quiet.
Step 6. Track code review metrics and act on them
Five metrics are enough, as long as each one is tied to a specific change. Check them monthly. Change one rule at a time so the team can see what helped.
| Metric | Warning sign | What to change |
|---|---|---|
| Time to first review | Regularly over 48 hours | Shrink pull requests, add reviewers for busy areas, set daily review slots |
| Pull request size | Many pull requests over 400 lines | Split work by behavior during planning |
| Comments per pull request | Almost none on large changes, or dozens about style | Add checklist items for silent approvals; move style rules into the linter |
| Bugs found after merge | Rising for two months or more | Add tests and a checklist item for the defect type that escaped |
| Developer satisfaction | Reviews described as slow or personal | Revisit the guidelines and agree on feedback wording |
Our view is that these numbers describe the process. Ranking individual reviewers by them invites fast, shallow approvals.
Frequently asked questions
How long should a code review take?
Keep each review session under an hour. SmartBear's guidance pairs the 200 to 400 line limit with sessions of no more than 60 minutes, because reviewer performance drops after that. The wait before a review starts matters too, and a first response within 24 to 48 hours keeps authors from losing context.
Can AI code review tools replace human reviewers?
AI reviewers such as GitHub Copilot code review are useful for a first pass. They are not guaranteed to catch every problem, and they sometimes make mistakes. Keep a human approval required on every merge.
How many approvals should a pull request need?
One approval from someone who knows the area is enough for most changes. Our view is that security-sensitive code, payment logic, shared libraries and database migrations deserve two, with one reviewer from the owning team. Write the number into the guidelines and enforce it with branch protection rules.
How do you review AI-generated code?
Review it like any other code, with extra attention to error handling and duplicated logic. Check that the change matches the ticket, that tests check behavior, and that the author can explain every part. If they cannot, the pull request is not ready.
Where to start with your code review process
Start with the two cheapest steps: a 400-line limit on pull requests and the checklist above in your pull request template. Add linters and an AI first pass in CI next, and keep a human approval on every merge. After a month, look at time to first review and bugs found after merge, then change one rule. Revisit the process when the team adds reviewers or when generated code makes up a growing share of pull requests. If your team needs more testing capacity while it sets this up, Arbisoft's QA and software testing services cover automated and regression testing alongside your developers.
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