Mr. Tianqi Wan Profile

Tianqi Wan

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Proceedings Article | 8 November 2023 Paper

Introducing test theory to deep neural network testing: reliability, validity, difficulty and discrimination

Yifei Liu, Yinxiao Miao, Ping Yang, Tianqi Wan, Long Zhang

Proceedings Volume 12923, 1292318 (2023) https://doi.org/10.1117/12.3011360

KEYWORDS: Reliability, Neural networks, Neurons, Design and modelling, Analytical research, Data modeling, Machine learning, Testing and analysis

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Researchers have developed various test methods and tools to ensure the performance and security of deep neural network (DNN) applications and detect potential defects as much as possible. However, there is still a lack of a mature and comprehensive DNN test theory, leading to the uneven quality of test tools and inconsistent evaluation methods, which pose challenges for the selection and application of test tools. Test Theory (TT) has developed a series of mature theoretical approaches and has validated its usability over a long history and in a wide range of application scenarios. This paper analyses the DNN test process, defines the concept of DNN test tools, and surveys the current research status and limitations of the DNN test field. Inspired by TT, this paper systematically introduces TT into DNN testing for the first time building the theoretical model of reliability, validity, difficulty, and discrimination that conform to the characteristics of DNN, which provides a new research perspective for DNN testing and is a preliminary study for establishing DNN testing theory.

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