Scalable Demand-Driven Call Graph Generation for Python

Call graph generation is the foundation of inter-procedural static analysis. PyCG is the state-of-the-art approach for generating call graphs for Python programs. Unfortunately, PyCG does not scale to large programs when adapted to whole-program analysis where dependent libraries are also analyzed. Further, PyCG does not support demand-driven analysis where only the reachable functions from given entry functions are analyzed. Moreover, PyCG is flow-insensitive and does not fully support Python's features, hindering its accuracy. To overcome these drawbacks, we propose a scalable demand-driven approach for generating call graphs for Python programs, and implement it as a prototype tool Jarvis. Jarvis maintains an assignment graph (i.e., points-to relations between program identifiers) for each function in a program to allow reuse and improve scalability. Given a set of entry functions as the demands, Jarvis generates the call graph on-the-fly, where flow-sensitive intra-procedural analysis and inter-procedural analysis are conducted in turn. Our evaluation on a micro-benchmark of 135 small Python programs and a macro-benchmark of 6 real-world Python applications has demonstrated that Jarvis can significantly improve PyCG by at least 67% faster in time, 84% higher in precision, and at least 10% higher in recall.