Performance Optimization Strategies

Incremental scanning dramatically reduces execution time by analyzing only changed code. Configure scanners to identify modified files through git integration and limit analysis scope. However, ensure incremental scanning doesn't miss vulnerabilities in unchanged code affected by modifications. Implement periodic full scans to catch issues incremental scanning might miss. Balance scan thoroughness with performance through intelligent scheduling.

Parallel execution leverages modern multi-core systems and distributed infrastructure. Many security scanners support parallel analysis across files, modules, or microservices. Configure appropriate parallelism levels based on available resources and scanner capabilities. Monitor resource utilization to identify optimal parallelism – excessive parallelism can cause resource contention and actually reduce performance.

Caching strategies prevent redundant work across scanner executions. Cache vulnerability databases locally to avoid repeated downloads. Store analysis results for unchanged code components and reuse across scans. Implement distributed caching for team environments where multiple developers scan similar code. Design cache invalidation strategies ensuring security updates propagate while maintaining performance benefits.

Rule optimization reduces scanner workload by eliminating unnecessary checks. Review enabled rules regularly, disabling those consistently producing false positives or checking for irrelevant vulnerabilities. Create custom rule sets for different application types – web applications need different rules than batch processing systems. Profile scanner execution to identify expensive rules consuming disproportionate resources.