A Comparative Study of the Application of Metamorphic Testing in Image Recognition and Processing
The rapid advancement of artificial intelligence (AI) and deep learning technologies has significantly improved image recognition systems, impacting critical domains like medical diagnostics, autonomous driving, and biometric security. However, validating these complex systems remains challenging du...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/124879 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/124879 |
| Access Level: | acceso abierto |
| Palabra clave: | 004(043.3) Software Testing Oracle Problem Metamorphic Testing Test Automation GeMTest Image Classification Pruebas de software Problema del oráculo Testing Metamórfico Automatización de pruebas Clasificación de imágenes Informática (Informática) 33 Ciencias Tecnológicas |
| Sumario: | The rapid advancement of artificial intelligence (AI) and deep learning technologies has significantly improved image recognition systems, impacting critical domains like medical diagnostics, autonomous driving, and biometric security. However, validating these complex systems remains challenging due to the oracle problem, this is the difficulty in determining correct outputs when clear expected results are unavailable. This thesis explores metamorphic testing (MT), a testing method using relationships between inputs and outputs (metamorphic relations, MRs), to effectively address the oracle problem. Focusing on a practical evaluation, the study applies metamorphic testing to a ResNet- 50 classifier validated on the ImageNet-1K dataset. Experiments using various MRs were automated through the GeMTest framework. Results show metamorphic testing’s practical effectiveness in detecting classification errors while highlighting limitations such as high false positive rates. The thesis concludes by summarising practical insights and suggesting realistic future research directions. |
|---|