Public-safe research and product guidance for AI-assisted software teams.
4 articleLarge AI-coded repo scans reveal why scanner reliability depends on archive fallback, context-aware detection, and supply-chain-level scoring.
Simple gates around dependencies, CI, auth boundaries, and public discovery files catch practical risks before they become incidents.
AI code review works better when findings are connected across frontend, backend, dependencies, data, CI, and security layers.
Aggregate lessons from large-scale AI code analysis: CI, dependencies, authentication boundaries, and maintainability signals.
Our research is based on continuous analysis of 128,000+ repositories and 3.27 billion lines of code using Repobility's proprietary scanning engine.
All data is aggregated and anonymized. No individual repository names or source code is disclosed.
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