Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.
- 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:
- 1668457
- Report Number(s):
- SAND--2020-10141R; 690845
- Country of Publication:
- United States
- Language:
- English
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