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

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

doi:10.1117/12.3011360

8 November 2023 Introducing test theory to deep neural network testing: reliability, validity, difficulty and discrimination

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

Author Affiliations +

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292318 (2023) https://doi.org/10.1117/12.3011360
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifei Liu, Yinxiao Miao, Ping Yang, Tianqi Wan, and Long Zhang "Introducing test theory to deep neural network testing: reliability, validity, difficulty and discrimination", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292318 (8 November 2023); https://doi.org/10.1117/12.3011360

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