Metamorphic Testing of Image Processing Applications: A General Framework and Optimization Strategies

Metamorphic testing (MT) is widely adopted for testing image processing applications. Although a variety of metamorphic relations (MRs) have been proposed, using all of them for testing will cost a large amount of computational resources. In addition, complex transformation operations are not well supported when generating follow-up test images based on MRs. To overcome these limitations, this study proposes a general MT framework for image processing applications, which employs CycleGAN to generate images that are very close to the realistic scenarios and leverages MRs for various categories of image processing applications. Two optimization strategies called EquivalentMR and SSampling are further proposed to reduce MRs and test images, respectively. A prototype tool called MT4I was developed. The experimental results showed that the proposed framework was capable of effectively testing various categories of image processing applications, while optimization strategies can reduce the amounts of MRs and test images without significantly jeopardizing the fault detection effectiveness.