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Software flaw detection using multimodal deep learning models has beendemonstrated as a very competitive approach on benchmark problems. In thiswork, we demonstrate that even better performance can be achieved using neuralarchitecture search (NAS) combined with multimodal learning models. We adapt aNAS framework aimed at investigating image classification to the problem ofsoftware flaw detection and demonstrate improved results on the Juliet TestSuite, a popular benchmarking data set for measuring performance of machinelearning models in this problem domain.

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