Advanced Graph-Based Deep Learning for Probabilistic Type Inference

Dynamically typed languages such as JavaScript and Python have emerged as the most popular programming languages in use. Important benefits can accrue from including type annotations in dynamically typed programs. This approach to gradual typing is exemplified by the TypeScript programming system which allows programmers to specify partially typed programs, and then uses static analysis to infer the remaining types. However, in general, the effectiveness of static type inference is limited and depends on the complexity of the program's structure and the initial type annotations. As a result, there is a strong motivation for new approaches that can advance the state of the art in statically predicting types in dynamically typed programs, and that do so with acceptable performance for use in interactive programming environments. Previous work has demonstrated the promise of probabilistic type inference using deep learning. In this paper, we advance past work by introducing a range of graph neural network (GNN) models that operate on a novel type flow graph (TFG) representation. The TFG represents an input program's elements as graph nodes connected with syntax edges and data flow edges, and our GNN models are trained to predict the type labels in the TFG for a given input program. We study different design choices for our GNN models for the 100 most common types in our evaluation dataset, and show that our best two GNN configurations for accuracy achieve a top-1 accuracy of 87.76% and 86.89% respectively, outperforming the two most closely related deep learning type inference approaches from past work -- DeepTyper with a top-1 accuracy of 84.62% and LambdaNet with a top-1 accuracy of 79.45%. Further, the average inference throughputs of those two configurations are 353.8 and 1,303.9 files/second, compared to 186.7 files/second for DeepTyper and 1,050.3 files/second for LambdaNet.