PoTo: A Hybrid Andersen's Points-to Analysis for Python

TL;DR

PoTo introduces a hybrid points-to analysis for Python that combines static analysis with concrete evaluation, improving type inference accuracy on large programs with external libraries.

Contribution

This paper presents PoTo, a novel hybrid Andersen-style points-to analysis tailored for Python, and PoTo+, a static type inference built on this analysis, addressing Python's dynamic features.

Findings

PoTo+ outperforms Pytype and DLInfer in type inference accuracy.

PoTo effectively handles Python's dynamic features and external libraries.

The hybrid approach scales to large Python programs.

Abstract

As Python is increasingly being adopted for large and complex programs, the importance of static analysis for Python (such as type inference) grows. Unfortunately, static analysis for Python remains a challenging task due to its dynamic language features and its abundant external libraries. To help fill this gap, this paper presents PoTo, an Andersen-style context-insensitive and flow-insensitive points-to analysis for Python. PoTo addresses Python-specific challenges and works for large programs via a novel hybrid evaluation, integrating traditional static points-to analysis with concrete evaluation in the Python interpreter for external library calls. Next, this paper presents PoTo+, a static type inference for Python built on the points-to analysis. We evaluate PoTo+ and compare it to two state-of-the-art Python type inference techniques: (1) the static rule-based Pytype and (2) the deep-learning based DLInfer. Our results show that PoTo+ outperforms both Pytype and DLInfer on existing Python packages.