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.

中文翻译：

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使用神经架构搜索改进多模态深度学习模型中的软件缺陷检测

使用多模态深度学习模型的软件缺陷检测已被证明是一种非常有竞争力的基准问题方法。在这项工作中，我们证明了使用神经架构搜索 (NAS) 结合多模态学习模型可以实现更好的性能。我们采用旨在研究图像分类的 NAS 框架来解决软件缺陷检测问题，并在 Juliet 测试套件上展示了改进的结果，Juliet 测试套件是用于测量该问题领域机器学习模型性能的流行基准数据集。