Large language models are powerful but inherit flaws from their training data. Sonar is working with AI labs and enterprise organizations to train specialized coding models optimized for cost, performance, quality, and security.
To learn more about our methodology and findings:
We’re not just keeping quality high; we’re actually able to go faster… AI makes it easier to deliver velocity, but only if you provide the right context from tools like SonarQube.
Overall I love the tool and I’m excited to dial up our usage, particularly as tools like Claude Code gain much wider adoption and we may be forced to reckon with the quality of what we’re creating.
With over 2,000 repos, manual enforcement isn’t feasible… now, every pull request automatically goes through quality gate checks, security analysis, and secret detection.
The central verification platform is how we… avoid that trade-off [between speed and safety]. It keeps the checks early. It keeps them consistent, creates visibility so the devs can move quickly.
As we move toward using AI tooling for code generation, it is reassuring to know that all our code is checked and scanned to provide a sanity check on the quantity of code being produced.
AI is generating code faster than teams can govern it. Sonar was named a Leader, and placed highest on Ability to Execute. We built the verification layer the AI development cycle actually needs.
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