ramsterr / rootrank

How ramsterr/rootrank works

spectrum-based root cause analysis for any graph with pass/fail traces. Ranks which node is the most likely failure source using Ochiai SBFL + propagation. Use cases: microservice incidents, CI , supply-chain failure analysis. Pure Python, pip-installable. This page is an auto-generated, always-fresh map of the ramsterr/rootrank repository, written primarily in Python. Repowise indexes the source, parses every symbol, computes a dependency graph, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.

The codebase has 12 source files, 118 symbols, and 3 languages. Git churn analysis flags 0 high-frequency files as hotspots — places where bugs, rewrites, and code review tend to concentrate. The dependency graph clusters into 4 tightly-coupled module communities.

Use the panels above to open the interactive dashboards, or connect this repo to your editor via the Repowise MCP server for grounded answers inside Claude, Cursor, or VS Code.