brownfield

Codebase analysis scans your repository layout, detects programming languages and frameworks, and identifies key indicators like package.json, requirements.txt, Cargo.toml, and pom.xml to map the tech stack. This approach works across Node.js, Python, Rust, Java, and other languages to reveal the underlying architecture without manual inspection.

Yes. Analyzing an existing codebase generates a high-level feature map and structure overview, helping new developers understand components, APIs, and modules without guessing. This accelerates onboarding by providing a documented snapshot of the project's architecture and observed functionality.

The analysis scans for manifest and configuration files—package.json for Node.js, requirements.txt for Python, Cargo.toml for Rust, and pom.xml for Java—to identify languages, frameworks, and dependencies. These files serve as the primary signals for inferring the complete tech landscape.

After mapping structure and features, the analysis generates an initial migration or refactor plan based on observed components and dependencies. This proposal provides a starting point for planning changes without requiring deep manual code review.

Yes. Legacy codebases benefit most from structure and tech stack inference, since documentation is often outdated or missing. The analysis surfaces actual components, entry points, and dependencies to support brownfield projects and ongoing maintenance.

Feature mapping extracts observed APIs, modules, tests, and other components to generate a high-level inventory of functionality. This output helps teams understand what the codebase does and identify dependencies between features for refactoring or migration planning.