Choosing Open Source Code Scanning Software

This guide helps an engineering team evaluate and select open source code scanning software that fits their tech stack, security needs, and workflow. You’ll learn the key criteria to compare, how to run a practical pilot, and how to avoid common pitfalls that lead to wasted effort or false confidence.

What You’ll Need

• A list of your team’s primary programming languages and frameworks
• Access to a representative sample repository (not just a toy project)
• Your CI/CD pipeline details (GitHub Actions, GitLab CI, Jenkins, etc.)
• Clear understanding of your compliance or security requirements (e.g., OWASP Top 10, CWE coverage)
• Time budget for a pilot (usually 1–2 weeks)

Step-by-Step

1. Define your scanning goals. Decide what matters most: finding security vulnerabilities, enforcing coding standards, detecting secrets, or all three. Write down the top three outcomes you expect, such as “catch SQL injection in Python” or “block hardcoded API keys before merge.” This prevents you from being swayed by flashy features you don’t need.
2. Shortlist tools based on language support. Check that each candidate supports your primary languages with real depth, not just basic syntax. For example, if you’re a Java shop, a tool with excellent Java rules is better than one with broad but shallow coverage. Visit each project’s documentation or GitHub page to confirm language maturity.
3. Evaluate rule quality and customization. Look at how rules are written, whether you can suppress false positives, and if you can write custom rules. A tool that lets you adjust severity levels and add team-specific rules will be more useful long-term than a rigid one.
4. Check integration with your CI/CD. Confirm the scanner can run as a command-line tool or Docker container that fits your pipeline. Look for official plugins or actions for your CI system. If the integration requires a lot of custom scripting, factor that maintenance cost into your decision.
5. Run a pilot on a real repository. Pick a non-critical but representative project. Run each shortlisted tool with default settings, then with recommended settings. Record the number of true positives, false positives, and missed issues (if you have a known bug to test against).
6. Measure performance and noise. Note scan time on a typical commit and on a full repository. A tool that takes 20 minutes per push will slow down your team. Also count how many alerts are actionable—if 90% are false positives, developers will ignore the tool.
7. Assess the community and maintenance. Check how recently the project was updated, how many maintainers are active, and how responsive they are to issues. A healthy open source project should have recent releases and a clear roadmap. Avoid tools that look abandoned.
8. Compare licensing and compliance. Review the open source license (MIT, Apache 2.0, GPL, etc.) to ensure it fits your company’s legal requirements. Some tools are open source but have paid tiers for certain features—understand what’s free and what’s not.
9. Get developer feedback. Ask a few engineers to use the top two candidates for a week. Collect their input on ease of use, clarity of alerts, and how much time they spend triaging. The tool with the best developer experience often wins, even if it’s not the most powerful.
10. Make a decision and document it. Choose the tool that best meets your goals, has manageable false positives, and integrates cleanly. Write down why you chose it, what you expect from it, and how you’ll measure success (e.g., reduction in known vulnerabilities over 3 months).

Common Mistakes

• Choosing based on marketing buzzwords. Terms like “AI-powered” or “zero false positives” are often exaggerations. Test on your own code.
• Ignoring the false positive rate. A tool that flags everything will be ignored by developers. Aim for a rate you can handle, not zero.
• Not considering language depth. A tool that supports 50 languages but only superficially will miss real issues in your main language.
• Forgetting about scalability. A scanner that works on a small repo may choke on a monorepo. Test on your largest project.
• Overlooking license and support. Some open source tools have a small community or a restrictive license that could cause legal headaches later.

Tips

• Start with a small set of rules. Enable only the most critical rules initially, then gradually increase coverage. This reduces noise and helps developers trust the tool.
• Use a baseline. When first introducing the scanner, set a baseline of existing issues and only fail builds on new ones. This avoids a flood of alerts that overwhelms the team.
• Integrate pre-commit or pre-push. Running the scanner locally before code reaches CI saves time and gives immediate feedback.
• Schedule regular rule updates. Open source scanners release new rules periodically. Plan to update and retest your baseline to catch new vulnerability patterns.
• Combine tools for broader coverage. You might use one tool for SAST and another for secret detection. Just be mindful of overlapping alerts.

Troubleshooting

If the scanner is too slow: Try running it only on changed files in CI (incremental scanning). If that’s not supported, consider running it nightly or on a schedule instead of on every commit. You can also increase memory or parallelize the scan.

If you get tons of false positives: Start by tuning the severity levels and disabling the noisiest rules. Check if the tool has a “baseline” feature to ignore existing issues. If false positives persist, look for a way to suppress specific findings with inline comments or a suppression file.

If the tool misses known vulnerabilities: Verify that you’re using the latest version and that the language is fully supported. Some tools require additional configuration for frameworks (e.g., Django, Spring). Check the documentation for framework-specific setup.

If integration with CI fails: Look at the tool’s official documentation for CI examples. Common issues include missing dependencies, wrong Docker image, or insufficient permissions. Test the command locally first, then replicate in CI.

If the project seems abandoned: Check the GitHub issues and pull requests. If there’s no activity for months, consider a fork or an alternative. You can also search for community-maintained forks that are more active.

Frequently Asked Questions

What’s the difference between SAST, DAST, and SCA?

SAST (Static Application Security Testing) scans source code for vulnerabilities without running it. DAST (Dynamic Application Security Testing) tests a running application. SCA (Software Composition Analysis) checks open source dependencies for known vulnerabilities. This guide focuses on SAST, but many tools also offer SCA features.

Can I use the same scanner for all my projects?

It depends on the tool’s language support. If you have a polyglot codebase, choose a tool that supports all your languages well. Otherwise, you might need multiple tools, but try to minimize the number to reduce maintenance overhead.

How do I handle a huge backlog of existing issues?

Don’t try to fix everything at once. Use a baseline to mark existing issues as “known” and only fail builds on new issues. Then gradually reduce the backlog by fixing the most critical ones first.

Is open source scanning software as good as commercial?

For many teams, yes. Open source tools like Semgrep, Brakeman, and Bandit are widely used and effective. They may lack some enterprise features like a GUI dashboard or dedicated support, but they often have strong communities and are more customizable.

How often should I update the scanner?

At least monthly, or whenever a new version is released. Security rules evolve quickly, and staying current helps catch new vulnerability patterns. Subscribe to the project’s release notifications.

What if the scanner doesn’t support my language?

Look for tools that have a plugin architecture or a community of rules. Some tools, like Semgrep, allow you to write custom rules for any language. If no tool works, consider a commercial option or a language-specific linter as a fallback.

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