Abstract: As deep neural networks are widely used in various fields, it is particularly important to test and evaluate them and ensure their safety. When the test dataset is large and the labelled cost is expensive, the test input selection method can select and sort the test samples to improve the test efficiency and test coverage. In order to further understand the research progress in the field of test input selection for deep neural networks, 91 academic papers in related fields over the past five years are systematically sorted out. Firstly, the basic concepts and processes of deep neural networks testing are introduced, including the construction of deep learning systems, test input selecting and the test metrics. Secondly, the paper outlines and analyzes the applicable scenarios and shortcomings of various metrics and test input selection methods, as well as the interconnections among them. Finally, current challenges and opportunities for deep neural networks test input selection and metrics are pointed out.

Key words: deep neural networks testing, test input metrics, test input selection, test input prioritization

摘要： 随着深度神经网络在各个领域受到广泛应用，对其进行测试评估并确保其安全性显得尤为重要。测试输入选择方法可以帮助在测试数据集规模较大且标注成本较高的情况下，对测试样本进行选择与排序，以提高测试效率和测试覆盖率。为了深入了解深度神经网络测试输入选择领域的研究进展，对近5年来91篇相关领域的学术论文进行了系统梳理。介绍了深度神经网络测试的基本概念和流程，包括深度学习系统的构建、测试输入的选择和测试结果的评估；概括分析了各种度量标准和测试输入选择方法的适用场景与不足之处，以及彼此之间的相互联系。最后，指出了当前深度神经网络测试输入选择与评估工作面临的挑战和机遇。

关键词: 深度神经网络测试, 测试输入度量标准, 测试输入选择, 测试输入优先级