TY  - JOUR
T1  - Approximating the Accuracy of Classification Models Using Self-differential Testing
AU  - Lee, Jubin 
AU  - Kim, Taeho 
AU  - Ma, Yu-Seung 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.12.1143
KW  - deep-learning
KW  - classification
KW  - differential testing
KW  - accuracy approximation
AB  - Differential testing is a traditional software testing technique that detects errors by observing whether similar applications generate different outputs for the same input. Differential testing is also used in artificial intelligence systems. Existing research involves the cost of finding a high-quality reference neural network with the same function as the target neural network but different architectures. We propose a self-differential testing technique that evaluates a classification model by making a reference model using a target neural network without the need to find the neural network of another architecture when differential testing. Experiments confirmed that self-differential testing produced similar effects at a lower cost than the existing research that requires other reference models. In addition, we propose an accuracy approximation method for classification models using self-differential analysis, which is an application of self-differential testing. The approximate accuracy through self-differential testing was confirmed to show a small difference of 0.0002 to 0.09 from the actual accuracy in experiments using similar datasets of MNIST and CIFAR10.