Ultra-Large-Scale Repository Analysis via Graph Compression

We consider the problem of mining the development history—as captured by modern version control systems—of ultra-large-scale software archives (e.g., tens of millions software repositories corresponding). We show that graph compression techniques can be applied to the problem, dramatically reducing the hardware resources needed to mine similarly-sized corpus. As a concrete use case we compress the full Software Heritage archive, consisting of 5 billion unique source code files and 1 billion unique commits, harvested from more than 80 million software projects—encompassing a full mirror of GitHub. The resulting compressed graph fits in less than 100 GB of RAM, corresponding to a hardware cost of less than 300 U.S. dollars. We show that the compressed in-memory representation of the full corpus can be accessed with excellent performances, with edge lookup times close to memory random access. As a sample exploitation experiment we show that the compressed graph can be used to conduct clone detection at this scale, benefiting from main memory access speed.