  Bug prediction is a resource demanding task that is hard to automate using
static source code analysis. In many fields of computer science, machine
learning has proven to be extremely useful in tasks like this, however, for it
to work we need a way to use source code as input. We propose a simple, but
meaningful representation for source code based on its abstract syntax tree and
the Doc2Vec embedding algorithm. This representation maps the source code to a
fixed length vector which can be used for various upstream tasks -- one of
which is bug prediction. We measured this approach's validity by itself and its
effectiveness compared to bug prediction based solely on code metrics. We also
experimented on numerous machine learning approaches to check the connection
between different embedding parameters with different machine learning models.
Our results show that this representation provides meaningful information as it
improves the bug prediction accuracy in most cases, and is always at least as
good as only using code metrics as features.
