Cleaner AI training data, fewer bugs: Sonars SonarSweep explained
Summary
Sonar explains SonarSweep as a way to clean training data before models learn from it. The approach uses static analysis, synthesis, remediation, and aggressive curation to remove bugs, security issues, and low-quality code from datasets. Sonar says swept data can reduce vulnerabilities and bugs in generated code while also lowering token usage in agentic coding workflows. The article frames data quality as a foundation for trustworthy, efficient AI-assisted software development.
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