SHF:Small:Differential Testing for Machine Learning Software

机器学习系统，包括深度学习（DL）系统，都需要可靠性。深度学习系统由两个关键组件组成：(1)执行复杂数学计算的模型和算法，以及(2)实现算法和模型的软件。这里的软件包括深度学习基础设施代码（例如，执行核心神经网络计算的代码）和应用程序代码（例如，加载模型权重的代码）。因此，要使整个DL系统可靠，软件实现和模型/算法都必须可靠。如果软件不能忠实地实现模型（例如，由于软件中的错误），则即使模型是正确的，软件的输出也可能是错误的，反之亦然。这个项目提高了测试深度学习软件实现的意识，以发现和定位深度学习软件中的缺陷。具体来说，该项目开发了端到端解决方案，通过对深度学习软件（包括源代码和数据）的差异测试来检测和定位错误，并防御对抗性输入，从而提高深度学习系统的可靠性和鲁棒性。除了提高技术水平之外，项目中开发的发现、方法和工具应该为测试DL软件提供教育和实用的工具。它可以帮助改变学生、开发人员和研究人员测试深度学习软件的方式。测试深度学习软件是具有挑战性的，因为对于开发人员来说，在给定输入实例的情况下，要知道被测软件的预期输出是特别困难的，因为深度学习算法和模型使用复杂的网络和数学公式。第二个挑战是在错误检测中识别DL软件中的许多错误功能。为了应对这些挑战，该项目使用差异测试来检测和定位深度学习软件中的错误，包括代码和数据，而不依赖于预期的输出。为了实现这一目标，必须了解具有相同配置的多次训练运行的方差，例如，相同的DL软件，相同的算法，相同的训练和测试数据，相同的网络等。这种差异表明深度学习算法和软件实现中的不确定性，这给研究人员和实践者带来了机遇和挑战。在方差结果的基础上，第一个推力创建了差分测试方法，以检测深度学习训练和推理阶段的软件缺陷，以及当多个实现不可用时，例如，通过改变模型。它解决了测试DL软件的基本oracle挑战。第二个推力构建了隔离多次运行差异的方法，以定位错误，帮助开发人员识别错误的根本原因，以便开发人员能够更快地正确修复它们。最后对深度学习软件的数据进行测试，以识别和防御敌对输入。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine-learning systems, including deep-learning (DL) systems, demand reliability. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural-network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable, both the software implementation and models/algorithms must be reliable. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa. This project raises awareness of testing DL software implementations to find and localize defects in DL software. Specifically, this project develops end-to-end solutions to improve the reliability and robustness of DL systems by differential testing DL software (including both source code and data) to detect and localize bugs, and defend against adversarial input. In addition to advancing the state of the art, the findings, approaches, and tools developed in the project should provide educational and practical tools to test DL software. It could help transform how students, developers, and researchers test DL software. Testing DL software is challenging, as it is particularly difficult for developers to know the expected output of the software under test given an input instance, because DL algorithms and models use complex networks and mathematical formula. A second challenge is to identify the faulty functions among many in the DL software upon bug detection. To address these challenges, this project uses differential testing to detect and localize bugs in DL software, including code and data, without relying on the expected output. To achieve this goal, one must understand the variances of multiple training runs with identical configurations, e.g., identical DL software, identical algorithm, identical training and test data, identical network, etc. Such variance indicates nondeterminism in the DL algorithms and software implementations, which imposes both opportunities and challenges for researchers and practitioners. Building on the variance results, the first thrust creates differential-testing approaches to detect software bugs in both the DL training and inference phases, as well as when multiple implementations are unavailable, e.g., by mutating models. It addresses the fundamental oracle challenge of testing DL software. The second thrust builds approaches to isolate the differences of multiple runs to localize bugs to help developers identify the bug root causes, so that developers can fix them correctly faster. The last thrust tests the data of DL software to identify and defend against adversarial input.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.