Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models
This work addresses software flaw detection for developers and security analysts, but it is incremental as it adapts an existing NAS framework to a new domain.
The paper tackled software flaw detection by applying neural architecture search (NAS) to multimodal deep learning models, achieving improved results on the Juliet Test Suite benchmark.
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.