  Automatic source code analysis in key areas of software engineering, such as
code security, can benefit from Machine Learning (ML). However, many standard
ML approaches require a numeric representation of data and cannot be applied
directly to source code. Thus, to enable ML, we need to embed source code into
numeric feature vectors while maintaining the semantics of the code as much as
possible. code2vec is a recently released embedding approach that uses the
proxy task of method name prediction to map Java methods to feature vectors.
However, experimentation with code2vec shows that it learns to rely on variable
names for prediction, causing it to be easily fooled by typos or adversarial
attacks. Moreover, it is only able to embed individual Java methods and cannot
embed an entire collection of methods such as those present in a typical Java
class, making it difficult to perform predictions at the class level (e.g., for
the identification of malicious Java classes). Both shortcomings are addressed
in the research presented in this paper. We investigate the effect of
obfuscating variable names during the training of a code2vec model to force it
to rely on the structure of the code rather than specific names and consider a
simple approach to creating class-level embeddings by aggregating sets of
method embeddings. Our results, obtained on a challenging new collection of
source-code classification problems, indicate that obfuscating variable names
produces an embedding model that is both impervious to variable naming and more
accurately reflects code semantics. The datasets, models, and code are shared
for further ML research on source code.
