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arXiv · 1904.11968

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

David Wehr·Halley Fede·Eleanor Pence·Bo Zhang·Guilherme Ferreira·John Walczyk·Joseph Hughes
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Abstract

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.

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David Wehr, Halley Fede, Eleanor Pence, Bo Zhang, Guilherme Ferreira, John Walczyk, Joseph Hughes. 2019-04-26. Learning Semantic Vector Representations of Source Code via a Siamese Neural Network. https://arxiv.org/abs/1904.11968

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