Start by confirming what the pull request is supposed to change. Then inspect the diff in context, trace the affected behavior through success and failure cases, check whether tests would expose likely defects, and scrutinize security-sensitive or dependency changes. Leave specific, impact-focused feedback and choose a review outcome that reflects whether the change is ready under your team’s standards.
How do I review a pull request for bugs before it’s merged?
Review the behavior, not just the lines of code. A useful review connects the stated requirement to the implementation, the paths it affects, and the evidence that those paths work. The exact checks depend on the repository and product contract; not every edge case applies to every change.
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- Establish intent. Read the PR title and description, linked issue, acceptance criteria, and any review notes. Identify what should change and what should remain unchanged. If the goal is unclear or the PR combines unrelated work, ask for context before judging correctness. GitHub recommends focused PRs with useful context for reviewers (GitHub guidance for authors); Google’s reviewer guidance emphasizes how a change affects users (Google Engineering Practices: Reviewing).
- Map the scope. Scan the changed-file list before reading every hunk. Notice public interfaces, configuration, schemas, dependency manifests and lockfiles, permissions, authentication, workflows, and generated files. Review the diff file by file, opening surrounding source when a changed line depends on context. GitHub’s review workflow supports tracking files as reviewed, which can help prevent overlooking part of a change (GitHub: Reviewing proposed changes in a pull request).
- Trace behavior. For each meaningful change, follow relevant inputs through decisions, state changes, outputs, and side effects. Consider successful and failed operations, boundary conditions, error handling, cleanup, ordering or concurrency assumptions, and compatibility with callers or stored data when relevant.
- Evaluate tests and automation. Find tests changed or added alongside the implementation. Ask whether a test would fail if a suspected defect were present, and whether important failure paths are exercised. Check relevant build and CI results, but treat passing automation as evidence rather than proof. GitHub recommends self-review and checking relevant builds or tests before requesting review; Google also calls attention to behavior and build or test changes (see the GitHub author guidance and Google reviewer guidance).
- Inspect higher-risk changes deliberately. Give extra attention to authentication, authorization, permissions, workflows, user-controlled input, sensitive data, and dependency changes. Verify that access checks protect the specific action and resource before protected work happens. Read dependency diffs directly as well as any automated alerts: GitHub notes that dependency review may not show every manifest or lockfile change, including unparsed dependencies. OWASP’s code review guide includes authorization and business-logic checks (GitHub: About dependency review; OWASP Code Review Guide).
- Write findings and select an outcome. Anchor feedback to the smallest useful code range. Explain the triggering condition and impact, then suggest a concrete fix or ask a focused question if the evidence is incomplete. Submit a general comment if you have feedback but no explicit approval or blocking request; approve if the change is ready under team standards; request changes if a concern should be fixed before merge.
What should I look for in a code review?
Use prompts that fit the change rather than treating a checklist as a claim that every PR has every risk:
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- Does the implementation match the requirement and the behavior users will observe?
- What happens with empty, invalid, repeated, unusually large, or boundary input?
- Could data be lost, duplicated, partially updated, or left inconsistent after an error?
- Are identity and permission checks applied to the requested action and resource?
- Do error handling and cleanup work on both success and failure paths?
- Could a configuration, workflow, dependency, or schema change affect behavior beyond the obvious lines?
- Do tests cover realistic failure cases as well as the happy path?
- Could an existing caller, deployment, migration, or supported environment break?
These questions combine user-impact, security, dependency, and test considerations reflected in Google’s reviewer guidance, GitHub’s dependency review documentation, and the OWASP Code Review Guide. Choose only the prompts relevant to the code and its contract.
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How should I write a useful review comment?
Describe a defect or uncertainty in terms the author can reproduce and assess. Separate correctness or security risks from preferences about style. For a finding, state the scenario, what the code appears to do, and the consequence; then ask a focused question or propose a direction for a fix. Avoid vague comments such as “this seems wrong” without explaining why.
Use a line-level comment when the concern belongs to a particular change, and a general comment when it applies to the PR as a whole. GitHub supports line comments and suggested edits in its review workflow (GitHub: Reviewing proposed changes in a pull request).
When should I approve, comment, or request changes?
| Review outcome | Use it when |
|---|---|
| Comment | You have feedback or questions, but are not explicitly approving or asking that changes be made before merge. |
| Approve | You believe the change is ready under the team’s review standards. |
| Request changes | You found a concern that should be addressed before merge. |
An approval is a readiness decision under your team’s standards, not a guarantee that no bug remains. If a high-risk change requires expertise you do not have—such as a specialized security or privacy assessment—say so and seek the appropriate reviewer rather than implying certainty.
How should automated code review fit into the process?
Automated review can add another source of findings, but its output still needs human validation. GitHub describes Copilot code review as able to identify bugs and security issues and offer suggestions (GitHub: Using Copilot code review). That is a description of the feature, not evidence that it catches every defect.
Compare human and automated review by the questions that matter to your team:
Quick Recap
Best Value
- Coverage: A person may understand product intent and wider system context; an automated tool works with the code and repository context available to it.
- Evidence: A reviewer can explain and reproduce a concern; a tool’s alert or suggestion is a lead to verify, not a confirmed defect by itself.
- Risk: Authentication, authorization, dependencies, and sensitive data warrant deliberate scrutiny regardless of whether automation reports an issue.
- Workflow fit: Findings are useful only if the team can inspect, discuss, and resolve them before merge.
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