Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python

By Amir M. Mir, Evaldas Latoskinas, Sebastian Proksch, Georgios Gousios. 2021

77 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Software, Code Generation & Program Synthesis, Program Analysis & Verification

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Armando Solar-Lezama
2. Koushik Sen
3. Naman Jain
4. Tianjun Zhang
5. Wen-Ding Li
6. Ion Stoica
7. Swarat Chaudhuri
8. Rishabh Singh

In Information Systems

1. Armando Solar-Lezama
2. Koushik Sen
3. Naman Jain
4. Tianjun Zhang
5. Ion Stoica
6. Rishabh Singh
7. Mosharaf Chowdhury
8. Charles Sutton

In Software

1. Armando Solar-Lezama
2. Koushik Sen
3. Naman Jain
4. Tianjun Zhang
5. Ion Stoica
6. Patrice Godefroid
7. Rishabh Singh
8. Gordon Fraser

In Code Generation & Program Synthesis

1. Armando Solar-Lezama
2. Koushik Sen
3. Rishabh Singh

In Program Analysis & Verification

1. Armando Solar-Lezama
2. Koushik Sen

Citations in the corpus

• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024, background): “Finally, some works have additionally studied other tasks and scenarios like type prediction (Mir et al., 2022; Wei et al., 2023a; Malik et al., 2019), code summarization (LeClair et al., 2019; Iyer et al., 2016; Barone and Sennrich, 2017; Hasan et al., 2021; Alon et al., 2018), code security…”