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 code-bases is error-prone and laborious, machine learning (ML)-based approaches have been proposed to enable automatic type infer-ence 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 hier-archical 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 neigh-bor 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 Stu-dio Code extension, which uses TYPE4Py to provide ML-based type auto-completion for Python.

@inproceedings{MirLPG22,
  title = {Type4Py},
  author = {Amir M. Mir and Evaldas Latoškinas and Sebastian Proksch and Georgios Gousios},
  booktitle = {ICSE},
  year = {2022},
  doi = {10.1145/3510003.3510124}
}