  Programmers make rich use of natural language in the source code they write
through identifiers and comments. Source code identifiers are selected from a
pool of tokens which are strongly related to the meaning, naming conventions,
and context. These tokens are often combined to produce more precise and
obvious designations. Such multi-part identifiers count for 97% of all naming
tokens in the Public Git Archive - the largest dataset of Git repositories to
date. We introduce a bidirectional LSTM recurrent neural network to detect
subtokens in source code identifiers. We trained that network on 41.7 million
distinct splittable identifiers collected from 182,014 open source projects in
Public Git Archive, and show that it outperforms several other machine learning
models. The proposed network can be used to improve the upstream models which
are based on source code identifiers, as well as improving developer experience
allowing writing code without switching the keyboard case.
