security-ownership-map

Mapping security ownership from git history requires building a bipartite people-to-file graph to compute ownership risk. This identifies who touches sensitive code and surfaces lone maintainers or orphaned hotspots grounded in actual commit contributions.

A co-change graph clusters files by how they move together in commits, helping detect ownership drift. By analyzing historical movements, you visualize file communities and identify when patterns diverge from established CODEOWNERS rules.

Yes, you can export git ownership analytics to Neo4j or Gephi. The analysis produces CSV and JSON formats compatible with these graph databases and visualization tools, also supporting optional graphML output for external network visualization workflows.

Yes, you need Python 3 and the networkx library to compute bus factor and ownership risk. The analysis relies on networkx to construct bipartite graphs and execute community detection algorithms required for security topology mapping.

The best way to perform a CODEOWNERS reality check is analyzing historical commit data to build a security ownership topology. This compares actual contribution patterns against declared CODEOWNERS, surfacing gaps, sensitive hotspots, and risky lone maintainers.

Limitations of using git commit history for sensitive code analysis include reliance on commit metadata accuracy and inability to capture uncommitted local changes. Optional exclusion flags filter noise, but historical data alone cannot reflect real-time access controls or undocumented reviews.