Multimodal Deep Learning for Flaw Detection in Software Programs
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1660805
- Report Number(s):
- SAND--2020-9429R; 690450
- Country of Publication:
- United States
- Language:
- English
Similar Records
Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
Technical Report
·
Tue Sep 01 00:00:00 EDT 2020
·
OSTI ID:1670527
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
Technical Report
·
Tue Sep 01 00:00:00 EDT 2020
·
OSTI ID:1668929
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
Technical Report
·
Tue Sep 29 00:00:00 EDT 2020
·
OSTI ID:1668457