  We explore the use of multiple deep learning models for detecting flaws in
software programs. Current, standard approaches for flaw detection rely on a
single representation of a software program (e.g., source code or a program
binary). We illustrate that, by using techniques from multimodal deep learning,
we can simultaneously leverage multiple representations of software programs to
improve flaw detection over single representation analyses. Specifically, we
adapt three deep learning models from the multimodal learning literature for
use in flaw detection and demonstrate how these models outperform traditional
deep learning models. We present results on detecting software flaws using the
Juliet Test Suite and Linux Kernel.
