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

Statistical language modeling techniques have successfully been
applied to large source code corpora, yielding a variety of new
software development tools, such as tools for code suggestion, improving readability, and API migration. A major issue with these
techniques is that code introduces new vocabulary at a far higher
rate than natural language, as new identifier names proliferate.
Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading
their performance and rendering them unable to scale.
In this paper, we address this issue by: 1) studying how various
modelling choices impact the resulting vocabulary on a large-scale
corpus of 13,362 projects; 2) presenting an open vocabulary source
code NLM that can scale to such a corpus, 100 times larger than in
previous work; and 3) showing that such models 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 been
reported.

• Software Engineering

• Natural Language Processing