AI Test Modeling for Computer Vision System—A Case Study

Abstract

This paper presents an intelligent AI test modeling framework for computer vision systems, focused on image-based systems. A three-dimensional (3D) model using decision tables enables model-based function testing, automated test data generation, and comprehensive coverage analysis. A case study using the Seek by iNaturalist application demonstrates the framework’s applicability to real-world CV tasks. It effectively identifies species and non-species under varying image conditions such as distance, blur, brightness, and grayscale. This study contributes a structured methodology that advances our academic understanding of model-based CV testing while offering practical tools for improving the robustness and reliability of AI-driven vision applications.