Manual vs. Automated (AI) Code Review:
Key Differences and Use Cases

The choice between manual code review vs. automated code review affects delivery timelines, security, and maintenance costs. In practice, organizations don’t pick one over the other. The most effective approach is to decide which checks require human judgment and which can be handled by AI-enhanced automation tools.

This article breaks down the differences, advantages, and limitations of manual code review and automated code review. I will also show you when to use each approach and how to blend them for consistent, efficient results.

Manual vs. automated code review at a glance:

• Manual code review is human-driven and context-aware, making it best for architecture, design, and security modeling.
• Automated code review, often AI-enhanced, is tool-driven, fast, and scalable, making it best for large-scale scans, rule enforcement, and dependency checks.
• Manual review excels in greenfield features, integrations, and PII handling, while automated review is strongest in dependency hygiene, secret scanning, and CI/CD consistency.
• A combined approach uses manual review for depth and automated review for breadth.
• DevCom recommends keeping pull requests small, shifting left with IDE checks, consolidating findings, automating dependency updates, and protecting sensitive data.

A manual code review is a human-driven process where developers, testers, and (QA) engineers read code changes in a version control system (typically through pull requests or PRs). 

During a manual code review, engineers and testers read through the changes to spot errors, unnecessary dependencies, security risks, and unclear logic. They also add context, explaining why the change was made, how it was tested, and which part of the system it affects. A project manager, tech lead, or code owner usually gives the final approval.

Manual reviews typically take place after the author has run local tests and passed linting checks, but before the code is merged into the main branch. The depth of the review depends on the size and complexity of the change.

Importantly, manual review complements automated testing — it’s not meant to replace it.

Automated code review means the team uses software (sometins AI-based) to check the code for defects, policy violations, common security issues, and more. These tools run inside an Integrated Development Environment (IDE), in pre-commit hooks, in continuous integration (CI) pipelines, and via scheduled scans.

Automated reviews can check higher volumes of code faster than manual assessments and source code audits. However, they do not replace human judgment.

Manual and automated checks help find and solve different types of problems with the codebase, which is why it’s best to combine both during a review.

Manual review thrives when decisions require reasoning that automated tools lack, but it comes with trade-offs.

Manual Code Review Pros

• Human reviewers can determine if the code adheres to correct business rules, whether it functions as expected, and if the change will impact real workloads.
• Reviewers can more accurately detect architectural alignment and boundary policing issues (such as code directly reading the databases instead of calling an approved service).
• Engineers are better at threat modeling because they can mentally simulate how attackers would try to expose the codebase’s weak points.
• Reviewers have an easier time spotting “race conditions” that aren’t based on static rules (such as situations where bugs happen due to operations using the same resources at once).
• Only human reviewers can sufficiently assess the quality of testing scenarios and edge cases.
• Reviewers can explain to the developers why a particular approach is better or riskier, helping the rest of the team raise their skill level.

Manual Code Review Cons

• Human review takes more time; change complexities, review planning and scheduling, and merge conflicts will slow delivery and increase rework.
• Reviewers often become tired and start skimming through code, which can cause them to miss subtle coding errors and edge cases.
• Different reviewers may apply different rules that can introduce inconsistency if they lack agreed-upon guidelines.
• Humans are unlikely to notice if a dangerous pattern appears in many places or any that appeared a long time ago.
• Manual code review process can’t check all dependency vulnerabilities, insecure cloud settings, or unsafe infrastructure configurations as efficiently as automated code review software.
• It’s hard to know if reviews are effective without automated tools that track essential metrics (like turnaround time, bug detection, issues reported after release, etc.).
• Overly strict review processes can discourage contributions (for example, developers who are instructed to look for important risks may start skipping over linting mistakes).

As with manual reviews, automated tools have their strengths and limitations.

Machines can review on commit across dozens of repositories much faster than a team of experienced developers, but some issues will slip through without human oversight.

Automated (AI) Code Review Pros

• Automated tools can catch errors the moment you save code or commit a change.
• A single automated review setup can combine multiple scanning methods that cover areas that manual reviews may skip.
• Automated reviews enforce consistent rules for every repository, team, and project.
• AI-enhanced models can group related warnings, hide duplicates, and rank issues, helping developers focus on the most sensitive problems.
• Automated review tools can suggest code changes, problem mitigation instructions, or replace problematic code, speeding up the mean time to repair.
• Tools can scan the entire codebase for long-standing issues, helping uncover flaws, bottlenecks, or coding patterns that no longer meet current quality standards.
• Review rules can be stored alongside the source code, so any changes to them are trackable, reducing manual checks during audits.
• Automated tools are easily scalable (adding a new repository to automated review is usually a matter of turning on a profile or installing a plugin).

Automated (AI) Code Review Cons

• A tool lacks contextual awareness about the business intent (for example, it may find outdated algorithms but be unaware that they support legacy clients).
• Automated tools can produce false positives (flagging code that is actually safe) and false negatives (missing real issues), requiring ongoing double-checks.
• Tools can miss unknown vulnerabilities or lose accuracy without regular updates, as threat patterns, programming languages, and software libraries change constantly.
• Generative AI review tools need periodic updates to newer versions, as old models are prone to model drift and hallucinations.
• Implementation of multiple fragmented tools may result in duplicate fixes or even contradictory results.
• Cloud-based review platforms may send source code or scan results to servers outside the organization’s control, which can violate data residency laws.

With all these advantages and disadvantages, it’s easy to get lost in the question, “When should a team use manual code review, automated code review, or both?”

Some situations call for human judgment, others are better handled by automated checks, and many require a mix of the two. The table below shows where each approach fits best.

These examples show that manual and automated reviews work best in tandem, each covering gaps the other leaves behind. To make that combination effective in everyday development, teams should follow a few proven best practices.

Some tasks could benefit from one or both types of reviews. The following tips can help you make the review more efficient.

• Keep PRs small and single-purpose. Each PR should address a specific change with a limited number of changed lines.
• Shift left with IDE and pre-commit checks. Run automated checks (linters, SAST rules, secret scanners, etc.) locally before committing code to avoid unnecessary CI failures.
• Use ephemeral environments for complex changes. Deploy branch-specific testing environments where reviewers can run manual and automated checks.
• Consolidate findings into a single queue. Route all automated tool outputs into a centralized tracking system that deduplicates issues, categorizes their severity, and assigns them to code owners.
• Enable two-tier scanning. Separate automated checks into two groups based on speed and depth (quick tier for linting, formatting, basic SAST in code pushes and PRs; deeper tier for DAST, SCA, and secret scans in repositories).
• Set explicit timelines for reviews. Limit the time for reviewing specific risk categories or changes to maintain predictable release cycles and avoid delays for urgent fixes.
• Scan repository history for secrets continuously. Automated tools can flag commit history for sensitive data (API keys, passwords, private certificates, etc.) and immediately notify reviewers to revoke and rotate the credentials.
• Automate dependency hygiene with grouping. Use update bots to submit grouped update PRs at scheduled intervals to reduce the review fatigue from multiple small version bumps, combining them with SCA policy checks and smoke tests.
• Revalidate rules against adversarial cases. Maintain a small library of problematic code patterns and periodically run them against AI-based review tools.
• Protect privacy and IP data. Ensure that all automated scans run within a secure on-premises or private cloud environment and redact findings with sensitive information.

To put these practices into action, teams also need the right tools to handle scanning, dependency checks, and security reviews.

Automated code review tools integrate into every stage of development: from IDEs and pre-commit hooks to CI pipelines and staging environments. Here are some of the most widely used options, organized by category.

SAST and Code Analysis

Static application security testing (SAST) examines code without running it to detect unsafe data flows, bug patterns, and other maintainability issues.

• GitHub CodeQL, integrated with GitHub Code Scanning, treats the codebase as a database that you can query for vulnerability patterns, and gates merges that fail checks.
• SonarQube and SonarCloud enforce “quality gates” on new code by combining rules for reliability, maintainability, and potential security vulnerabilities.
• Amazon CodeGuru Reviewer provides an algorithm-based code review assistant for several programming languages, including Java and Python.

SCA and Dependency Hygiene

Software composition analysis (SCA) inspects the third-party components of the codebase for insecure components, performance issues, incompatible licenses, and other issues.

• OWASP Dependency-Check maps libraries to known Common Platform Enumerations (CPE) and flags them against the Common Vulnerabilities and Exposures (CVE) database.
• GitLab Dependency Scanning shows vulnerabilities in merge requests.
• Dependabot (a GitHub-native automation) raises pull requests for dependency updates.

Secret Detection

Secret detection tools scan code and commit history for credentials, API keys, and other sensitive data.

• GitHub Secret Scanning can detect known token formats in repositories.
• GitGuardian Secrets Detection uses high-accuracy detectors to scan codebases and history.
• TruffleHog catches high-entropy strings and credentials in repositories and commit history.

DAST and Runtime Analysis

Dynamic application security testing (DAST) runs crafted inputs to detect cross-site scripting vulnerabilities, misconfigurations, and authentication issues.

• OWASP ZAP has manual and AI code review automation testing models for application scanning.
• StackHawk focuses on API scanning and actionable remediation guidance.

IaC and Container Scanners

Infrastructure scanners review environments and container files for risks, unwanted dependencies, insecure defaults, and more.

• Trivy checks container images, file systems, and infrastructure configurations.
• Checkov scans Terraform, CloudFormation, Kubernetes, and similar IaC files for potential security vulnerabilities and compliance violations.

Code Quality Linters

Linters and formatters enforce coding standards and catch logic errors to maintain style and readability. These are varied and depend on your programming framework.

• Codacy runs multiple analyzers, posts feedback on pull requests, and enforces coding standards.

Neither manual nor automated review alone can deliver high-quality code. A balanced software development life cycle should use automated review for breadth and manual review for depth, ensuring neither coverage nor comprehension suffers. Recognizing the role of each makes it easier to design a process that delivers both accuracy and efficiency.

DevCom’s source code audit process blends automated scanning with targeted human expertise to identify broader issues in your architecture, performance, and usability. Feel free to contact us to learn more about our process.