LLMs trained on public code repositories absorb bugs, insecure patterns, and poor practices alongside good code. Sonar's SonarSweep addresses this by filtering and improving training data through four phases: deep static analysis, synthesis of high-quality examples, automated remediation of flawed patterns, and aggressive curation. Training on swept data produced a 41% reduction in both security vulnerability density and bug density in generated code. Beyond code quality, cleaner training data also reduces token usage in agentic coding sessions — Sonar's research across ~660 Claude Code task runs found roughly 7-8% fewer tokens used in SonarQube-verified codebases with no drop in task completion.
Table of contents
Why data quality engineering mattersThe payoff is measurableGenerating quality from the ground up49 Impressions