Publication Date
9-18-2025
Document Type
Article
Publication Title
Computers
Volume
14
Issue
9
DOI
10.3390/computers14090396
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.
Keywords
a case study, adequate test coverage, data augmentation, failure rate analysis, intelligence validation for computer vision systems, test generation, test modeling
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Applied Data Science; Computer Engineering
Recommended Citation
Jerry Gao and Radhika Agarwal. "AI Test Modeling for Computer Vision System—A Case Study" Computers (2025). https://doi.org/10.3390/computers14090396
