Type4py: Practical Deep Similarity Learning-based Type Inference For Python

Abstract

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

Stats

• citations59
• S2 citations—
• github stars0
• HF likes0
• heat score13.34
• arxiv keymir2021type4py

Related papers

• Metric Learning By Similarity Network For Deep Semi-supervised Learning (2020)3.58
• It's The Best Only When It Fits You Most: Finding Related Models For Serving Based On Dynamic Locality Sensitive Hashing (2020)0.00
• Words Are All You Need? Language As An Approximation For Human Similarity Judgments (2022)0.00
• Learning Deep Representation Of Multityped Objects And Tasks (2016)0.00
• Deep Graph Similarity Learning: A Survey (2019)13.97
• Improving Deep Representation Learning Via Auxiliary Learnable Target Coding (2023)5.84
• Attention-based Dynamic Subspace Learners For Medical Image Analysis (2022)3.58
• Learning From One And Only One Shot (2022)5.24