Codebase Exploration

To map an unfamiliar codebase, use a progressive-zoom approach: survey directories, identify files, enumerate symbols via LSP document-symbols, and extract signatures to minimize unnecessary reading. This produces a structured JSON report with provenance for precise navigation.

Building a call graph for impact analysis requires using LSP goto-definition and find-references to trace accurate call chains. This method identifies entry points and dead code, providing a structured report to safely guide refactoring tasks across multi-module projects.

Yes, LSP-powered codebase exploration can identify missing tests and docstrings by extracting signatures and analyzing symbol structures. The structured JSON exploration report includes detected gaps like dead code and missing documentation to guide subsequent build tasks.

Tracing data flows without reading every file is achieved through progressive-zoom navigation using LSP find-references and goto-definition. This pinpoints exact call chains and symbol dependencies, returning a structured report so you only read relevant line-level code.

Codebase exploration works effectively for developer onboarding by systematically mapping large repositories. It surveys directories, enumerates public APIs, and lists callers using LSP tooling, rapidly bringing new developers up to speed with structured JSON reports.

You should not use text search alone when precise symbol navigation is required, as it is time-consuming and error-prone. For multi-module projects, LSP-powered call graphs and dependency analysis provide accurate entry points and dead code detection that text search misses.