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DOE OSTI · 1668929

Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

Cooper, Alexis·Zhou, Xin·Dunlavy, Daniel·Heidbrink, Scott
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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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Cooper, Alexis, Zhou, Xin, Dunlavy, Daniel, Heidbrink, Scott. 2020-09-01. Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models. https://doi.org/10.2172/1668929

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