  Source code representations are key in applying machine learning techniques
for processing and analyzing programs. A popular approach in representing
source code is neural source code embeddings that represents programs with
high-dimensional vectors computed by training deep neural networks on a large
volume of programs. Although successful, there is little known about the
contents of these vectors and their characteristics. In this paper, we present
our preliminary results towards better understanding the contents of code2vec
neural source code embeddings. In particular, in a small case study, we use the
code2vec embeddings to create binary SVM classifiers and compare their
performance with the handcrafted features. Our results suggest that the
handcrafted features can perform very close to the highly-dimensional code2vec
embeddings, and the information gains are more evenly distributed in the
code2vec embeddings compared to the handcrafted features. We also find that the
code2vec embeddings are more resilient to the removal of dimensions with low
information gains than the handcrafted features. We hope our results serve a
stepping stone toward principled analysis and evaluation of these code
representations.

Machine Learning;Programming Languages;Software Engineering;Machine Learning