论文标题
测试深度学习模型：多个测试技术的首次比较研究
Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques
论文作者
论文摘要
深度学习（DL）彻底改变了基于视觉系统（VB）的能力，例如自动驾驶，机器人手术，关键的基础设施监视，空气和海上交通控制等，通过分析图像，语音，视频或任何类型的复杂信号，DL已大大提高了这些系统的情况。同时，虽然越来越多地依靠训练有素的DL模型，但VB的可靠性和鲁棒性受到了挑战，并且对这些模型进行彻底测试以评估其功能和潜在错误至关重要。为了发现DL模型中的故障，现有的软件测试方法已得到了相应的调整和完善。在本文中，我们提供了这些软件测试方法的概述，即差异，变质，突变和组合测试，以及对vBS中使用的增强感知系统的部署时的对抗性扰动测试和审查一些挑战。我们还提供了关于VBS中使用的经典基准并讨论其结果的首次实验比较研究。
Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffic control, etc. By analyzing images, voice, videos, or any type of complex signals, DL has considerably increased the situation awareness of these systems. At the same time, while relying more and more on trained DL models, the reliability and robustness of VBS have been challenged and it has become crucial to test thoroughly these models to assess their capabilities and potential errors. To discover faults in DL models, existing software testing methods have been adapted and refined accordingly. In this article, we provide an overview of these software testing methods, namely differential, metamorphic, mutation, and combinatorial testing, as well as adversarial perturbation testing and review some challenges in their deployment for boosting perception systems used in VBS. We also provide a first experimental comparative study on a classical benchmark used in VBS and discuss its results.
