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.

Abstract Application programming interfaces often have correctness constraints that cut across multiple arguments. Violating these constraints causes the underlying code to raise runtime exceptions, but at the interface level, these are usually documented at most informally. This article presents novel principled static analysis and the first interprocedural weakest‐precondition analysis for Python to extract inter‐argument constraints. The analysis is mostly static, but to make it tractable for typical Python idioms, it selectively switches to the concrete domain for some cases. This article focuses on the important case where the interfaces are machine‐learning operators and their arguments are hyperparameters, rife with constraints. We extracted hyperparameter constraints for 429 functions and operators from 11 libraries and found real bugs. We used a methodology to obtain ground truth for 181 operators from 8 machine‐learning libraries; the analysis achieved high precision and recall for them. Our technique advances static analysis for Python and is a step towards safer and more robust machine learning.

Machine-learning operators often have correctness constraints that cut across multiple hyperparameters and/or data. Violating these constraints causes the operator to raise runtime exceptions, but those are usually documented only informally or not at all. This paper presents the first interprocedural weakest-precondition analysis for Python to extract hyperparameter constraints. The analysis is mostly static, but to make it tractable for typical Python idioms in machine-learning libraries, it selectively switches to the concrete domain for some cases. This paper demonstrates the analysis by extracting hyperparameter constraints for 181 operators from a total of 8 ML libraries, where it achieved high precision and recall and found real bugs. Our technique advances static analysis for Python and is a step towards safer and more robust machine learning.

Python 3 is a highly dynamic language, but it has introduced a syntax for expressing types with PEP484. This paper explores how developers use these type annotations, the type system semantics provided by type checking and inference tools, and the performance of these tools. We evaluate the types and tools on a corpus of public GitHub repositories. We review MyPy and PyType, two canonical static type checking and inference tools, and their distinct approaches to type analysis. We then address three research questions: (i) How often and in what ways do developers use Python 3 types? (ii) Which type errors do developers make? (iii) How do type errors from different tools compare?