Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities
Sandia National Laboratories (SNL), Albuquerque, NM (United States). New Mexico Small Business Assistance (NMSBA) Program, NM (United States), Scott Heidbrink, USDOE Assistant Secretary for Human Resources and Administration, Kathryn Rodhouse, Daniel Dunlavy, Alexis Cooper, Xin Zhou · 2020
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.