How to Integrate Code Reviews in a CI/CD Pipeline

Continuous integration, delivery, and deployment tests confirm that software runs as expected. They do not tell if the code is clean, secure, efficient, and readable. Integrating code reviews in a CI/CD pipeline solves those issues.

Our article shows how to make something as complex as code reviews a part of your CI/CD without extreme overhead. We will explain which rules and practices matter, how to automate review checks, and which mistakes slow teams down.

Integrating code review in a CI/CD means your pipeline will accept code changes only after they pass defined technical tests. Your rules determine when a change is ready to merge or release.

A regular CI/CD pipeline runs automated tests that prove software runs correctly. Continuous integration (CI) ensures code can run without errors before pushing it into the codebase. Continuous delivery and continuous deployment (both short for CD) prepare approved changes for release or deploy them to production.

However, regular checks do not assess the code quality, nor do they check if the code duplicates logic, leaks secrets, or breaks internal rules.

In continuous integration, code review adds more automated checks with human assessment before the merge. This way, the pipeline enforces consistent rules that make the code even better.

Benefits of integrating code review in a CI/CD

When you enforce code review inside CI and CD, new lines of code will move further down the pipeline after passing all controls. This is a more deliberate method that can impact your software development lifecycle in several ways.

• Issues are identified earlier: Review checks surface build failures, unsafe patterns, and rule violations at merge time, so developers fix problems before they spread or create technical debt.
• Releases are more secure: Integrated review checks scan for known risky patterns and common vulnerabilities before changes reach production, reducing avoidable security exposure.
• Accountability is improved: Your pipeline can leave an approval trail that shows who tested and approved critical merges.
• Teams understand the code better: Reviews expose design decisions, edge cases, and trade-offs, helping developers avoid repeating the same mistakes.
• Codebase is more consistent: Your pipeline enforces code quality standards and readability practices, making the codebase easier to understand, maintain, and scale.

However appealing these benefits may be, they require deliberate placement inside the development pipeline.

The development process already has rules and quality gates. Code review works best when it enhances your existing process.

1. Map the code changes

You need to see how the code changes flow through your current CI/CD pipeline before you add reviews.

Testing often drifts outside the pipeline, with final checks happening minutes before release. Listing each check a code must pass through can reveal gaps. These could be anything from security scans running after the merge to skipped performance checks for hotfixes. Proper mapping, however, can tell you where to put code to catch and fix more issues.

2. Set clear pass rules

A change can only move forward when it meets predefined rules. Pass rules describe concrete conditions, like how many people must approve a change or whether checks must complete without errors.

After a pull request, CI should build a runnable program from the code and run a series of tests. If the pipeline determines that the program doesn’t run, there’s no point in reviewing it, as it will waste computing resources and developers’ time.

3. Standardize the unit of change

CI/CD code review works only if everyone follows the same review path. Every pull request should stop at a single place that shows who owns it, which automated checkers passed, what was changed manually, and who approved the review.

Avoid situations where everyone follows their own process. For example, one can push code directly, another asks for feedback in Slack, and a third sends a file to a lead by email. This would just bring chaos and defeat the purpose of a CI/CD code review pipeline.

4. Choose where to enforce review gates

Decide on a moment when the pipeline blocks the merge without a thorough code assessment.

DevOps teams use review gates to prevent unfinished or impactful changes. For example, you can put them before merging into the shared codebase or releasing to users.

5. Set unified artifacts for builds

To keep reviews consistent, the pipeline should build an artifact once. This artifact should be reused rather than rebuilt for every environment.

An artifact is the output produced after CI builds the code. It can be a compiled application, a container image, or another runnable form. If it’s rebuilt, the pipeline may produce a slightly different result even if you didn’t touch the source code. If a defect appears in production, the team cannot confidently trace it back to the reviewed code.

6. Create company-wide visibility

Developers need immediate and detailed feedback. Pull requests should clearly show which checks ran, which failed, and why. When a rule blocks progress, the pipeline should point directly to the file and the condition that caused the block.

Non-technical stakeholders don’t need the code details. Instead, your CI/CD pipeline should track other measurable indicators. These can include how often approved changes reach production, how long changes take to ship, and how often teams roll back releases.

7. Review, fine-tune, and scale

You should track how the code review rules and gates affect the development workflow. Watch where changes slow down, which checks fail most often, and which fixes prevent real issues.

Some checks run slowly but rarely catch problems. You can either remove them, move them down the pipeline, or limit them to high-risk changes.

Keep the core workflow stable first. Once teams consistently meet existing rules, add stricter gates for sensitive areas.

Review rules work only if your team can apply them consistently.

Efficient practices inside a CI/CD make reviews repeatable and predictable —  something you want to keep the pipeline moving.

Keep pull requests single-purpose

A pull request should have one objective, such as adding a feature slice, fixing a bug, or refactoring a component. If the work is too large, there’s a greater risk that developers will miss edge cases or unintended side effects. If necessary, split larger pull requests into smaller, sequential steps.

Write reviewable pull requests

Developers need to explain their changes because the code alone doesn’t explain some decisions. Make it a rule to state what changed, what was left intentionally untouched, what the trade-offs were, and how reviewers can verify the result.

Give feedback as soon as possible

Comments should include actionable feedback points to avoid back-and-forth. Comments should name the issue in the change, explain the risks it introduces, and point toward exact fixes. For example, instead of saying logic feels unsafe, point to the input that breaks it or the future change that will make it harder to maintain.

Use consistent severity language

Define a small set of severity labels, so your reviewers don’t debate importance on every change. Once everyone uses the same labels, reviews will move faster. You can defer some formatting issues without much risk, while critical ones should block the merge until resolved.

Combine manual and automated checks

Automated and manual reviews should go hand in hand. Automated checks usually flag formatting issues, naming rules, known security vulnerabilities, and basic mistakes. Human reviewers should assess product and business risk. This includes how a pull request affects behavior, usability, performance, and whether it introduces unexpected side effects.

These practices are hard to maintain by hand, which is why you need automation.

To improve the code review, CI/CD should automate as much as possible without reducing the assessment quality. Here are the mundane checks and organizational tasks that you can cover.

• Enforcing pull request inputs: Automated checks can block a pull request that lacks context (such as test notes, rollout constraints, or affected components). This prevents reviewers from chasing missing information.
• Routing reviews with rules: Set code ownership rules that assign reviewers based on risk factors or developer expertise to remove manual tagging.
• Notifying about stalled reviews: Notifications should inform your team when reviewers or owners change, or if certain pull requests stay idle for too long.
• Capturing review evidence: The system records approvals, required comments, and final decisions alongside each pull request. When an issue appears later, teams can trace why the change passed review.
• Merging through a controlled queue: A queue orders approved pull requests to avoid conflicting changes. Otherwise, you may end up with multiple clean changes that break the build.
• Creating lightweight review summaries: Review tools generate summaries that highlight waiting reviews, long-idle pull requests, and blocking reasons. Team leads can use this signal to rebalance workload or adjust review rules.

Another important part is knowing what tools can automate the code review process in your pipeline.

A solid CI/CD code review setup combines a few tool types. You can think of them as layers that cover different issues. Below are the tools you can configure into your pull requests to block issues before, during, and shortly after merges.

Code quality

Code quality review tools keep the codebase efficient and readable as it grows. In CI/CD, use them as merge gates that fail a pull request when new code is below your standards.

• SonarQube: Checks the code for code smells, bugs, and known risky patterns, enforcing quality gates on changes.
• Codacy: Runs automated pull request checks and applies the same rule set across repositories, keeping review standards consistent without manual policing.
• CodeClimate: Highlights duplicated code and logic, so you can see which parts of a change to optimize before it accumulates technical debt.

Code security

Code security review tools catch risky patterns in pull requests. Human reviewers still look for edge cases manually, but automated checks help spot and remove often missed issues (like unsafe input handling, overly permissive access checks, and hardcoded credentials).

• Codelantis: Flags pull requests for security weaknesses, pairing findings with explanations and suggested fixes.
• Korbit.ai: Ranks found issues by severity so your team fixes high-impact problems first.

Static analysis

Static analysis tools inspect code without running it. A clean CI/CD setup uses two layers of static analysis: a fast diff-focused scan on every pull request, and a scheduled full repository scan that checks deeper patterns across the whole codebase.

• DeepSource: Checks pull requests for easily skippable mistakes (null handling mistakes, unsafe resource usage, unreachable or contradictory branches, failing logic paths, etc.).
• Coverity: Runs deeper static checks to detect critical defects (such as memory leaks, uninitialized values, and API misuse).

Dependency checks

Dependency checks scan the third-party libraries that you did not write. Most apps rely on external code for dozens of operations, like logging, authentication, encryption, parsing, and file uploads. Add these checks to CI so the pull request catches vulnerabilities you might inherit.

• DeepSource: Combines static scans with software composition analysis, spotting vulnerable libraries and unsafe ways your apps interact with them.
• Deepcode: Adds dependency-based security feedback to pull requests so you can assess likely exposure before the merge.
• SonarQube: Enforces rule-based checks for risk patterns, bugs, and vulnerability signals in external libraries, acting as a merge readiness gate.

Collaboration tools

Collaboration tools help teams review critical parts of your code. You can use these tools on tasks that require stricter assessments (like core services and databases) because there are so many more risks they can break your production.

• GitHub PR Reviews: Routes reviews through required approvals, required status checks, and ownership rules, so merges only happen when approved by the right stakeholders.
• Review Board: Supports formal review flows and customized approval steps, which help teams that rely on legacy systems or need stricter review tracking.
• Collaborator: Helps your team enforce multi-stage sign-off for database checks and audit-ready reports, so you can see who approved each change and why.

AI-powered review assistants

AI review assistants speed up pull request reviews in multiple ways. They summarize changes, flag suspicious logic, highlight bug-prone patterns, and suggest refactors. Your reviewers should not rely on AI too much, treating it merely as a supportive tool.

• CodeRabbit: Comments directly in the review thread with quick and specific problems, so your team can see the risks and potential fixes immediately.
• Qodo Merge: Builds an internal map of how parts of the system relate to each other, and suggests fixes based on patterns you already used in the same repository.
• Bito AI: IDE-friendly assistant that gives real-time review-style feedback during development, helping you correct problems before pull requests.

Up until now, we have only discussed what you should do, but a big part of the integration is knowing what can go wrong.

Mistakes tend to repeat across teams and create obstacles, which is why it’s important to examine the most common ones.

CI and CD confirm that the software runs, but not how. Gates, weak patterns, rushed fixes, and hidden issues can move forward because tests passed.

Adding code reviews as a part of your pipeline confirms that it holds to your standards. But that opens you up to overwork. Without experience, a review pipeline can slow your project to a halt.

When that expertise is missing or stretched thin, DevCom fills the gap. Our team can review your source code and optimize your DevOps pipeline, so your quality improves without stalling.