Pull request reviews catch bugs in new code. But large codebases contain logic written years ago that never gets reviewed again, code that can harbor hidden bugs and accumulate risk as dependencies evolve.

Some issues only appear when analyzing code across multiple files or when old code is examined in today's context. These bugs often require deep investigation to uncover, something quick PR reviews can't do.

AI codebase scans analyze the full repository to surface system-level issues, including logic errors, performance risks, stability problems, and security vulnerabilities.

TLDR

• Codebase scans analyze the entire repository to find system-level issues that don't surface during pull request reviews.

• Most existing scanning tools focus primarily on security vulnerabilities. AI-powered codebase scans go further, detecting business logic errors, performance risks, stability problems, and security issues.

• Subtle bugs sometimes require deep investigation that quick PR reviews can't provide, cubic finds these through continuous analysis.

• Third-party dependencies and external code evolve independently of your repository; codebase scans catch when those changes introduce risk.

• Continuous scanning helps teams identify issues that have been hiding in the codebase for years.

The gap between code review and codebase security

Code review focuses on changes. When you open a pull request, the review process examines what you added or modified. This catches many bugs before they merge.

Most codebases contain thousands of lines of existing code that never get reviewed after the initial merge. That code sits there, unexamined, even as:

• Security researchers discover new vulnerabilities in patterns your old code uses.

• Third-party dependencies you rely on get compromised or updated with breaking changes.

• External repositories or libraries you depend on evolve in ways that create edge cases with your system.

• Business logic that made sense five years ago creates unexpected interactions with newer features.

• Cross-file interactions create bugs that don't appear when reviewing a single file or PR.

Code review checklists help ensure new code gets a thorough evaluation. They don't solve the problem of the existing codebase; code that needs continuous analysis because it's constantly exposed to new risks as the world around it changes.

What automated codebase scanning does

Codebase scanning relies on three complementary approaches. Each addresses a different class of issues that pull request reviews don't catch.

Most existing scanning tools focus primarily on security. They check for SQL injection risks, cross-site scripting, hardcoded secrets, and dependency vulnerabilities. Essential work, but incomplete.

AI-powered codebase scans go further. They detect business logic errors, performance risks, and stability issues alongside security vulnerabilities. This broader scope catches bugs that traditional tools miss.

The key difference is investigation depth. Traditional tools use pattern matching. cubic's AI agents understand code logic deeply, catching subtle bugs that require hours of investigation to surface. Think of issues like a logical flaw spread across multiple files, a race condition hiding in rarely used code, or an assumption about a third-party system that no longer holds. These need real analysis, something fixed rules can’t catch.

cubic's agents investigate these patterns across your entire repository as it evolves and as dependencies shift, something security-focused scanners and PR reviewers can't do. They surface issues that have been hiding in your code for years.

How cubic's codebase scans work

cubic takes automated codebase scanning beyond traditional SAST and SCA. The platform runs thousands of AI agents across your entire repository to find bugs and security issues continuously.

1. Continuous analysis across the repository

Rather than scanning on demand, cubic's codebase scans run continuously to catch new issues as they emerge. This includes:

• Vulnerabilities in older code that was written before current security standards.

• Bugs introduced by interactions between different parts of the codebase.

• Security issues from third-party dependencies being compromised.

• Architectural problems that span multiple files.

Nick Sweeting from Browser Use shared: "Within hours, cubic found a critical RCE our team had missed for months. We opened a CVE and deployed a fix in a fraction of the time thanks to cubic."

2. Automatic issue ownership and notification

When cubic identifies an issue, it automatically determines who introduced it and notifies the right person or team. Issue owners get notified in Slack or assigned tickets in Linear or JIRA, making the workflow seamless.

This automatic assignment means issues don't get lost. The developer who knows the code best gets notified immediately, making fixes faster and more accurate.

3. Scheduled scans for release preparation

cubic allows teams to run scans on a schedule or before major releases. This ensures code is thoroughly checked before important deployments without slowing down daily development.

Teams can configure scanning frequency based on their needs. Some run nightly scans. Others scan before each release. The flexibility lets teams balance thoroughness with velocity.

4. Integration with development workflows

cubic integrates with tools that developers already use. Developers can:

• Fix issues with one click using background agents.

• Copy prompts to their AI coding assistant for fixes.

• Review issues directly in their normal workflow tools.

Issues are automatically resolved when fixes merge, keeping the tracking clean without manual updates.

What codebase scans catch that code review misses

Code review focuses on individual changes. Codebase scans look at the system as a whole. That difference shows up clearly in the kinds of issues each approach finds.

1. Hidden legacy bugs

Older code often contains logical flaws that were never obvious. Because the code isn't actively changing, it never gets reviewed again. These bugs can lie dormant for years until the right conditions trigger them. Codebase scans surface these hidden issues by analyzing the entire repository deeply, not just reviewing recent commits.

2. Dependency and external code risks

Third-party packages and external repositories you depend on can be updated, compromised, or deprecated long after you integrated them. Your code stays the same, and no new pull request is opened, but the risk changes.

Codebase scans that include SCA catch these problems by continuously monitoring dependencies for new vulnerabilities, breaking changes, and behavioral shifts that might affect your system.

3. Cross-file bugs

Some bugs only appear when multiple files interact. One part of the code makes assumptions about what another part does, but those assumptions silently break in edge cases. Each file looks fine in isolation.

Codebase scans analyze these interactions across the system, catching issues that don't show up when reviewing a single PR or file.

4. Subtle logic errors

The most dangerous bugs sometimes don't break anything; they just create unexpected behavior under specific conditions, maybe a race condition in rarely-executed code or an off-by-one error that only surfaces with certain data. 

These bugs require deep investigation to uncover, sometimes taking 12+ hours of careful analysis. PR reviews, which last minutes, can't catch them. Continuous codebase scanning with AI-powered analysis can.

5. Architectural drift

As systems grow, older code may no longer follow current architectural boundaries. Each change still looks reasonable in isolation, so code review doesn't flag it. But over time, violations accumulate.

Codebase scans identify when the overall structure starts to drift, helping teams spot design problems before they become entrenched.

When to run codebase scans

Different scan schedules serve different goals. Most teams use a mix, depending on how often they ship and how much risk they want to catch early.

Continuous background scanning

Lightweight scans run in the background to catch issues as they emerge and as your dependencies shift. This helps teams spot problems quickly without slowing down day-to-day development. cubic runs continuous scans across the repository and reports issues as they appear.

Pre-release scanning

Deeper scans run before major releases to ensure no critical bugs or security issues reach production. These scans take longer but provide broader coverage at key deployment moments.

Scheduled periodic scans

Nightly or weekly scans offer a balance between coverage and resource usage. This works well for teams that want regular visibility into code health without constant scanning.

Beyond PRs: Scanning the full codebase

Code review and codebase scanning serve different purposes. Code review catches issues in new code. Codebase scanning finds problems in existing code and system-wide issues.

Together, they provide full coverage. AI-powered code review focuses on every new change as it’s introduced, while continuous codebase scanning keeps watching the rest of the repository as it evolves. Used together, they catch bugs and risks that either approach would miss on its own.

Modern teams ship fast. Dependencies evolve, external code changes, features interact in new ways, and codebases grow over time. In that environment, reviewing only pull requests leaves too many blind spots. As PRs keep changing, your code review process needs to keep up with constant change, and that’s where continuous codebase scanning comes in.

Ready to find bugs hiding in your codebase? 

Book a demo to see how cubic's AI agents continuously scan your repository for security issues and vulnerabilities.