Language-Agnostic Representation Learning of Source Code from Structure and Context

• URL: http://arxiv.org/abs/2103.11318v1
• Date: Sun, 21 Mar 2021 06:46:06 GMT
• Title: Language-Agnostic Representation Learning of Source Code from Structure and Context
• Authors: Daniel Z\"ugner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan G\"unnemann
• Abstract summary: We propose a new model, which jointly learns on Context and Structure of source code. We show that jointly training on non-parallel data from multiple programming languages improves results on all individual languages.
• Score: 43.99281651828355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly either on Structure or Context. We propose a new model, which jointly learns on Context and Structure of source code. In contrast to previous approaches, our model uses only language-agnostic features, i.e., source code and features that can be computed directly from the AST. Besides obtaining state-of-the-art on monolingual code summarization on all five programming languages considered in this work, we propose the first multilingual code summarization model. We show that jointly training on non-parallel data from multiple programming languages improves results on all individual languages, where the strongest gains are on low-resource languages. Remarkably, multilingual training only from Context does not lead to the same improvements, highlighting the benefits of combining Structure and Context for representation learning on code.

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