Predicting Type Annotations for Python using Embeddings from Graph Neural Networks

Vladimir Ivanov; Vitaly Romanov; Giancarlo Succi

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Paper

Predicting Type Annotations for Python using Embeddings from Graph Neural Networks

In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, 548-556, 2021

Authors: Vladimir Ivanov ; Vitaly Romanov and Giancarlo Succi

Affiliation: Innopolis University, Russia

Keyword(s): Neural Networks, Source Code, GNN, CNN, Embeddings, Type Prediction, Python.

Abstract: An intelligent tool for type annotations in Python would increase the productivity of developers. Python is a dynamic programming language, and predicting types using static analysis is difficult. Existing techniques for type prediction use deep learning models originated in the area of Natural Language Processing. These models depend on the quality of embeddings for source code tokens. We compared approaches for pre-training embeddings for source code. Specifically, we compared FastText embeddings to embeddings trained with Graph Neural Networks (GNN). Our experiments showed that GNN embeddings outperformed FastText embeddings on the task of type prediction. Moreover, they seem to encode complementary information since the prediction quality increases when both types of embeddings are used.

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Paper citation in several formats:

Ivanov, V.; Romanov, V. and Succi, G. (2021). Predicting Type Annotations for Python using Embeddings from Graph Neural Networks. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 548-556. DOI: 10.5220/0010500305480556

@conference{iceis21,
author={Vladimir Ivanov. and Vitaly Romanov. and Giancarlo Succi.},
title={Predicting Type Annotations for Python using Embeddings from Graph Neural Networks},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={548-556},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010500305480556},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Predicting Type Annotations for Python using Embeddings from Graph Neural Networks
SN - 978-989-758-509-8
IS - 2184-4992
AU - Ivanov, V.
AU - Romanov, V.
AU - Succi, G.
PY - 2021
SP - 548
EP - 556
DO - 10.5220/0010500305480556
PB - SciTePress