We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.

中文翻译：

---

软件程序中用于缺陷检测的多模态深度学习

我们探索使用多种深度学习模型来检测软件程序中的缺陷。当前，用于缺陷检测的标准方法依赖于软件程序的单一表示（例如，源代码或程序二进制）。我们说明，通过使用来自多模态深度学习的技术，我们可以同时利用软件程序的多种表示来改进单一表示分析的缺陷检测。具体来说，我们采用了多模态学习文献中的三种深度学习模型用于缺陷检测，并展示了这些模型如何优于传统的深度学习模型。我们展示了使用 Juliet 测试套件和 Linux 内核检测软件缺陷的结果。