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Statistical language modeling techniques have successfully been applied tolarge source code corpora, yielding a variety of new software developmenttools, such as tools for code suggestion, improving readability, and APImigration. A major issue with these techniques is that code introduces newvocabulary at a far higher rate than natural language, as new identifier namesproliferate. Both large vocabularies and out-of-vocabulary issues severelyaffect Neural Language Models (NLMs) of source code, degrading theirperformance and rendering them unable to scale.  In this paper, we address this issue by: 1) studying how various modellingchoices impact the resulting vocabulary on a large-scale corpus of 13,362projects; 2) presenting an open vocabulary source code NLM that can scale tosuch a corpus, 100 times larger than in previous work; and 3) showing that suchmodels outperform the state of the art on three distinct code corpora (Java, C,Python). To our knowledge, these are the largest NLMs for code that have beenreported.  All datasets, code, and trained models used in this work are publiclyavailable.

Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code