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

Source: [Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models](<https://www.sota2.com/research/paper/68fa68681e209131cea1be55>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:36:00.183Z

Authors: Alexis Cooper, Xin Zhou, Scott Heidbrink, Daniel M. Dunlavy

Published: 2020-09-22T15:59:21.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.10644>)

[PDF](<https://arxiv.org/pdf/2009.10644.pdf>)