map-reduce

Parallelized codebase analysis splits the repository into chunks and processes them with independent mapper agents. A central reducer then synthesizes findings, performing cross-chunk validation and deduplication to maintain consistency across the entire audit.

Parallel processing for code analysis is needed when scaling to large repositories for bulk audits or architectural investigations. It distributes independent chunks to mapper agents, synthesizing results to manage cross-cutting concerns efficiently.

The best way to deduplicate code analysis findings is using a central reducer during cross-chunk synthesis. After independent mappers analyze distributed chunks, the reducer validates findings and removes duplicates, producing a consistent ReductionResult for auditing.

Yes, you can use this parallelized code analysis approach for bulk audits. It orchestrates end-to-end by spawning independent mappers per chunk and a reducer for cross-chunk validation, outputting a ReductionResult suitable for auditing and automation.

Scale code analysis by distributing workloads into independent chunks processed by parallel mappers. Cross-chunk validation and a central reducer synthesize findings, ensuring context is preserved and validated across chunks for a consistent ReductionResult.