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

Alexis Cooper; Xin Zhou; Scott Heidbrink; Daniel M. Dunlavy

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arXiv · 2009.10644

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

Alexis Cooper·Xin Zhou·Scott Heidbrink·Daniel M. Dunlavy

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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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stat.ML cs.AI cs.CR cs.LG

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BibTeX RIS

Alexis Cooper, Xin Zhou, Scott Heidbrink, Daniel M. Dunlavy. 2020-09-22. Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models. https://arxiv.org/abs/2009.10644

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