Testing Deep Neural Network Programs

近年来，人工智能，特别是机器学习（ML）已经成为全球大多数公司研发的核心。像深度学习这样的技术已经在许多环境中得到应用，并提供了惊人的结果。然而，当我们在各种应用程序和上下文中使用这些ML技术时，我们的日常生活变得更加依赖于它们的性能。例如，在安全关键型或任务关键型应用程序的上下文中，可靠、健壮、安全和可靠的ML是必须的。因此，在这个项目中，我们建议研究测试机器学习程序的技术（特别是在使用深度学习的视频对象检测的背景下），以了解现有技术在检测实际缺陷方面的效果，以及我们如何在OASIS公司的案例研究中改进这些技术的缺点。

In recent years, artificial intelligence and in particular Machine Learning (ML) have been the core of research and development in most companies around the world. Techniques such as deep learning have been employed in many contexts and provided astonishing results. However, as we employ these ML techniques on a variety of applications and contexts, our everyday life becomes more dependent on their performance. For example, in the context of safety critical or mission critical applications a reliable, robust, safe, and secure ML is a must. Thus, in this project, we propose studying techniques for testing ML programs (particularly in the context of object detection in videos using deep learning) to understand how good the existing techniques are in terms of detecting real defects and how we can improve those techniques with respect to their shortcomings in our case study with OASIS Inc company.