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.
@article{arxiv.2009.04549,
title = {Multimodal Deep Learning for Flaw Detection in Software Programs},
author = {Scott Heidbrink and Kathryn N. Rodhouse and Daniel M. Dunlavy},
journal= {arXiv preprint arXiv:2009.04549},
year = {2020}
}