Using Neural Architecture Search for Improving Software Flaw Detection
in Multimodal Deep Learning Models
- URL: http://arxiv.org/abs/2009.10644v1
- Date: Tue, 22 Sep 2020 15:59:21 GMT
- Title: Using Neural Architecture Search for Improving Software Flaw Detection
in Multimodal Deep Learning Models
- Authors: Alexis Cooper and Xin Zhou and Scott Heidbrink and Daniel M. Dunlavy
- Abstract summary: 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.
- Score: 2.5705339271809753
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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