  Statistical language models are powerful tools which have been used for many
tasks within natural language processing. Recently, they have been used for
other sequential data such as source code.(Ray et al., 2015) showed that it is
possible train an n-gram source code language mode, and use it to predict buggy
lines in code by determining "unnatural" lines via entropy with respect to the
language model. In this work, we propose using a more advanced language
modeling technique, Long Short-term Memory recurrent neural networks, to model
source code and classify buggy lines based on entropy. We show that our method
slightly outperforms an n-gram model in the buggy line classification task
using AUC.
