Sensitive Region-based Metamorphic Testing Framework using Explainable AI

Deep Learning (DL) is one of the most popular research topics in machine learning and DL-driven image recognition systems have developed rapidly. Recent research has employed metamorphic testing (MT) to detect misclassified images. Most of them discuss metamorphic relations (MR), with limited attention given to which regions should be transformed. We focus on the fact that there are sensitive regions where even small transformations can easily change the prediction results and propose an MT framework that efficiently tests for regions prone to misclassification by transforming these sensitive regions. Our evaluation demonstrated that the sensitive regions can be specified by Explainable AI (XAI) and our framework effectively detects faults.

Object-based Metamorphic Testing through Image Structuring

Testing software is often costly due to the need of mass-producing test cases and providing a test oracle for it. This is often referred to as the oracle problem. One method that has been proposed in order to alleviate the oracle problem is metamorphic testing. Metamorphic testing produces new test cases by altering an existing test case, and uses the metamorphic relation between the inputs and the outputs of the System Under Test (SUT) to predict the expected outputs of the produced test cases. Metamorphic testing has often been used for image processing software, where changes are applied to the image's attributes to create new test cases with annotations that are the same as the original image. We refer to this existing method as the image-based metamorphic testing. In this research, we propose an object-based metamorphic testing and a composite metamorphic testing which combines different metamorphic testing approaches to relatively increase test coverage.