Python 3 is a highly dynamic language, but it has introduced a syntax for expressing types with PEP484. This paper ex- plores 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?

https://www.cs.rpi.edu/~milanova/docs/dls2020.pdf

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