Metamorphic testing seeks to verify software in the absence of test oracles. Our application domain is ocean system modeling, where test oracles rarely exist, but where symmetries of the simulated physical systems are known. The input data set is large owing to the requirements of the application domain. This paper presents work in progress for the automated generation of metamorphic test scenarios using machine learning. We extended our previously proposed method [1] to identify metamorphic relations with reduced computational complexity. Initially, we represent metamorphic relations as identity maps. We construct a cost function that minimizes for identifying a metamorphic relation orthogonal to previously found metamorphic relations and penalize for the identity map. A machine learning algorithm is used to identify all possible metamorphic relations minimizing the defined cost function. We propose applying dimensionality reduction techniques to identify attributes in the input which have high variance among the identified metamorphic relations. We apply mutation on these selected attributes to identify distinct metamorphic relations with reduced computational complexity. For experimental evaluation, we subject the two implementations of an ocean-modeling application to the proposed method to present the use of metamorphic relations to test the two implementations of this application.
MR-Coupler: Automated Metamorphic Test Generation via Functional Coupling Analysis
The Hong Kong University of Science and Technology（香港科学与技术大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州HKUST傅以刚研究所）；University of Auckland（奥克兰大学）
Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
School of EECS, University of Ottawa（渥太华大学电气与计算机工程学院）；Lero SFI centre for Software Research, University of Limerick（利默里克大学Lero软件研究中心）；Waterloo Research Center of Huawei Technologies Canada Co., Ltd.（华为技术有限公司加拿大分公司滑铁卢研发中心）
