Evaluate a SAST tool by running it on your own repositories with a few known flaws planted, then measuring how many real issues it finds, how much noise you must triage, and how easily it fits your pull request flow. A vendor demo on a sample app tells you little about your code.
What should you decide before the trial starts?
Write down what a good outcome looks like. Pick two or three representative repositories in the languages and frameworks you actually use. Choose success measures in advance: findings you accept as real, time spent triaging, scan duration, and whether developers read the results.
Check basic fit first. Does the tool support your languages and build setups? Does code leave your environment during analysis, and is that acceptable to your policy? Those two questions end many trials early, which is a good outcome.
How do you measure accuracy honestly?
Seed known flaws. Ask a developer to add five to ten realistic vulnerabilities in a branch: a SQL query built from input, a hard-coded credential, an unsafe deserialization, a missing authorization check. Record which the tool finds. Do not publish or keep these branches.
Use your history. Run the tool on a release from before a fixed bug, and see whether it flags the code that was later patched.
Count false positives by sampling. Take 30 to 50 findings across severities and have an engineer classify each as real, not exploitable or wrong. Precision on your code matters more than total count.
Use public benchmarks carefully. The OWASP Benchmark is described as a Java and Python test suite built to verify the speed and accuracy of vulnerability detection tools, with scorecard generators for many tools. It is a useful common yardstick, but it is a synthetic application. Treat a good score as a floor, not proof of fit with your codebase.
What else belongs on the scorecard?
Developer experience. Are results shown in the pull request or IDE, with the code path and an explanation? Is a suggested fix included, and is it correct?
Triage tools. Can you suppress a finding with a reason, share that decision across the team, and see it again if the code changes?
CI behavior. Scan time on a typical pull request, incremental scanning, and whether you can fail builds on a threshold you choose. Output formats such as SARIF help if you already use code scanning dashboards.
Data handling. Where code is processed, what is stored, and for how long. If AI is part of the analysis, ask whether it can be switched off and whether you can bring your own model.
Coverage limits. Ask the vendor what the tool does not find. A credible answer lists business logic flaws, authorization bugs and anything needing runtime context. If the answer is "nothing," keep asking.
Cost model. Ask how pricing scales with developers, repositories or lines scanned, and what happens at your growth rate.
What to do
- Pick two or three real repositories and one success measure per criterion.
- Check language support and data handling before any scan.
- Seed five to ten known flaws on a private branch and record detection.
- Sample 30 to 50 findings and label them real or not.
- Time a typical pull request scan and the triage effort per finding.
- Let two developers use it for a week and collect their comments.
- Ask for the vendor's written list of what it cannot find.
- Compare at least two tools with the same repos and the same labels.
- Decide against your written criteria, not the demo.
See a scan in action
Our SAST demo shows what a scan and its findings look like.
Open the full demo at /demos/sast-code-scan.
Quick check
How many findings should I sample to judge false positives?
Thirty to fifty findings across severity levels is a practical sample for a trial. It is not a statistical study, but it shows whether noise is a minor irritation or a blocker.
Is a high score on a public benchmark enough?
No. Benchmarks are synthetic. They are good for comparing tools on common ground, but only your own code shows how a tool behaves on your frameworks and patterns.
Sources
- OWASP Benchmark Project, fetched 18 October 2026.
The seeded flaw method and sampling sizes are practical suggestions from this guide.
Last reviewed: 18 October 2026.
