Dynamic languages, such as Python and Javascript, trade static typing for
developer flexibility and productivity. Lack of static typing can cause
run-time exceptions and is a major factor for weak IDE support. To alleviate
these issues, PEP 484 introduced optional type annotations for Python. As
retrofitting types to existing codebases is error-prone and laborious, machine
learning (ML)-based approaches have been proposed to enable automatic type
inference based on existing, partially annotated codebases. However, previous
ML-based approaches are trained and evaluated on human-provided type
annotations, which might not always be sound, and hence this may limit the
practicality for real-world usage. In this paper, we present Type4Py, a deep
similarity learning-based hierarchical neural network model. It learns to
discriminate between similar and dissimilar types in a high-dimensional space,
which results in clusters of types. Likely types for arguments, variables, and
return values can then be inferred through the nearest neighbor search. Unlike
previous work, we trained and evaluated our model on a type-checked dataset and
used mean reciprocal rank (MRR) to reflect the performance perceived by users.
The obtained results show that Type4Py achieves an MRR of 77.1%, which is a
substantial improvement of 8.1% and 16.7% over the state-of-the-art approaches
Typilus and TypeWriter, respectively. Finally, to aid developers with
retrofitting types, we released a Visual Studio Code extension, which uses
Type4Py to provide ML-based type auto-completion for Python.

本文介绍了一种基于深度相似性学习的层次神经网络模型，用于类型推断，以实现 Python 编程的机器学习自动类型补全。Type4Py 是一种新方法，它是在人工验证的类型检查数据集上训练和评估，其平均倒数排名为 77.1%，是现有方法 Typilus 和 TypeWriter 的改进。同时，作者还发布了 Visual Studio Code 插件以帮助开发者进行类型的自动补全。