perf-code-paths

Mapping code paths before profiling involves identifying likely implementation routes and hot files. This approach targets entrypoints and handlers to spotlight hot routes, producing a structured list of file paths and symbols for prioritization.

Identifying code paths for profiling works best by analyzing the codebase structure to map candidate files and symbols. It enumerates top impacted files and includes import/export or call chains to provide contextual wiring for the profiling session.

Yes, code path mapping for profiling applies to codebases across Rust, Java, JS/TS, Go, and Python. It focuses on these specific languages to help developers map hotspots and entrypoints before starting performance profiling sessions.

Yes, you can analyze entrypoints and handlers without a repo-map. The code path mapping process uses grep to locate handlers and entrypoints when a repo-map is not available, still outputting a compact list of file paths and evidence for prioritization.

To locate call chains and implementation paths via static code analysis, you map the contextual wiring between files. This includes import/export chains and call relationships, yielding a structured list of symbols and evidence to guide performance profiling.

Mapping code paths before profiling focuses your performance analysis on likely hotspots and implementation routes. By identifying entrypoints and candidate files first, you avoid profiling blind spots and prioritize the most impacted code symbols.