Code Review definition
Code review is the practice of having one or more developers examine a colleague's code changes before they are merged, to catch defects, improve design and share knowledge across the team. In most teams it happens through pull requests or merge requests on platforms such as GitHub, GitLab or Bitbucket, combined with automated checks.
How the code review process works
A developer finishes a change on a branch and opens a pull request describing what changed and why. Automated checks run first: builds, tests, linters and security scans in the CI/CD pipeline. One or more reviewers then read the diff, leave comments or suggestions, and either approve or request changes. The author responds, updates the code and the cycle repeats until the change is approved and merged.
Branch protection rules can require approvals, passing checks and review by the owners of specific folders, defined in a CODEOWNERS file. That turns review from a habit into a guarantee that no code reaches production without a second pair of eyes, which is also a control auditors look for in SOC 2 reports.
Review is not only about catching bugs. It spreads knowledge: reviewers learn parts of the codebase they did not write, authors pick up conventions from colleagues, and the team avoids depending on a single person who understands each critical module.
What reviewers should look for
Machines should handle formatting and style through linters and formatters, leaving humans to focus on what tools cannot judge. A useful review checklist for most product teams, roughly in order of importance, covers the points below.
Reviewers should also run the change when it touches the user interface or a tricky flow, rather than judging from the diff alone. A short checkout of the branch, or a preview deployment that every pull request gets automatically, catches layout, copy and interaction problems that are invisible in code. The checklist:
- Correctness: does the change do what the ticket asks, including edge cases and error paths?
- Design: does it fit the architecture, or add coupling and duplication?
- Security: input validation, authorization checks, secrets and injection risks
- Tests: are important behaviors covered, and would the tests fail if the code were wrong?
- Readability: clear names, small functions and comments that explain why, not what
- Operational impact: logging, metrics, migrations and backward compatibility
Best practices for fast, useful reviews
Keep pull requests small; a change of a few hundred lines gets a careful review, while thousands of lines get a skim and an approval. Write a description with context, screenshots for UI changes and testing notes. Review promptly, since waiting days for review is one of the biggest drags on delivery speed and shows up directly in DORA metrics such as lead time for changes.
Comment on the code, not the person, and separate blocking issues from suggestions, for example with a nit prefix for minor points. Approving with small suggestions is often better than forcing another round trip for something trivial.
AI-assisted code review
AI review tools, built into GitHub, GitLab and dedicated products, can summarize pull requests, flag likely bugs and suggest missing tests. They are useful as a first pass, especially for missed null checks or inconsistent error handling, but they lack business context and can be confidently wrong. Nexzem uses automated and AI-assisted checks alongside mandatory human review on client projects, with reviewers accountable for what they approve.