  There are several approaches for encoding source code in the input vectors of
neural models. These approaches attempt to include various syntactic and
semantic features of input programs in their encoding. In this paper, we
investigate Code2Snapshot, a novel representation of the source code that is
based on the snapshots of input programs. We evaluate several variations of
this representation and compare its performance with state-of-the-art
representations that utilize the rich syntactic and semantic features of input
programs. Our preliminary study on the utility of Code2Snapshot in the code
summarization and code classification tasks suggests that simple snapshots of
input programs have comparable performance to state-of-the-art representations.
Interestingly, obscuring input programs have insignificant impacts on the
Code2Snapshot performance, suggesting that, for some tasks, neural models may
provide high performance by relying merely on the structure of input programs.
