Learning Semantic Vector Representations of Source Code via a Siamese Neural Network

The abundance of open-source code, coupled with the success of recent advances in deep learning for natural language processing, has given rise to a promising new application of machine learning to source code. In this work, we explore the use of a Siamese recurrent neural network model on Python so...

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Bibliographic Details
Main Authors:	Wehr, David, Fede, Halley, Pence, Eleanor, Zhang, Bo, Ferreira, Guilherme, Walczyk, John, Hughes, Joseph
Format:	Journal Article
Language:	English
Published:	26-04-2019
Subjects:	Computer Science - Learning Computer Science - Programming Languages Computer Science - Software Engineering Statistics - Machine Learning
Online Access:	Get full text
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Summary:	The abundance of open-source code, coupled with the success of recent advances in deep learning for natural language processing, has given rise to a promising new application of machine learning to source code. In this work, we explore the use of a Siamese recurrent neural network model on Python source code to create vectors which capture the semantics of code. We evaluate the quality of embeddings by identifying which problem from a programming competition the code solves. Our model significantly outperforms a bag-of-tokens embedding, providing promising results for improving code embeddings that can be used in future software engineering tasks.
DOI:	10.48550/arxiv.1904.11968