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Python type inference is challenging in practice. Due to its dynamicproperties and extensive dependencies on third-party libraries without typeannotations, the performance of traditional static analysis techniques islimited. Although semantics in source code can help manifest intended usage forvariables (thus help infer types), they are usually ignored by existing tools.In this paper, we propose PYInfer, an end-to-end learning-based type inferencetool that automatically generates type annotations for Python variables. Thekey insight is that contextual code semantics is critical in inferring the typefor a variable. For each use of a variable, we collect a few tokens within itscontextual scope, and design a neural network to predict its type. Onechallenge is that it is difficult to collect a high-quality human-labeledtraining dataset for this purpose. To address this issue, we apply an existingstatic analyzer to generate the ground truth for variables in source code.  Our main contribution is a novel approach to statically infer variable typeseffectively and efficiently. Formulating the type inference as a classificationproblem, we can handle user-defined types and predict type probabilities foreach variable. Our model achieves 91.2% accuracy on classifying 11 basic typesin Python and 81.2% accuracy on classifying 500 most common types. Our resultssubstantially outperform the state-of-the-art type annotators. Moreover,PYInfer achieves 5.2X more code coverage and is 187X faster than astate-of-the-art learning-based tool. With similar time consumption, our modelannotates 5X more variables than a state-of-the-art static analysis tool. Ourmodel also outperforms a learning-based function-level annotator on annotatingtypes for variables and function arguments. All our tools and datasets arepublicly available to facilitate future research in this direction.

PYInfer: Deep Learning Semantic Type Inference for Python Variables