论文标题
在软件程序中进行缺陷检测的多模式深度学习
Multimodal Deep Learning for Flaw Detection in Software Programs
论文作者
论文摘要
我们探索使用多个深度学习模型来检测软件程序中的缺陷。当前的，标准的缺陷检测方法取决于软件程序的单个表示（例如源代码或程序二进制）。我们说明，通过使用多模式深度学习的技术，我们可以同时利用软件程序的多个表示，以改善单个表示分析的漏洞检测。具体而言，我们从多模式学习文献中调整了三个深度学习模型，以用于缺陷检测，并演示这些模型的表现如何优于传统的深度学习模型。我们介绍了使用Juliet Test Suite和Linux内核检测软件缺陷的结果。
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.
