Metamorphic Testing and Web Element Localization

In this issue, we are pleased to present three papers where the first two focus on metamorphic testing (MT) and the third one focuses on web element localization. The first paper, ‘Boosting Multimode Ruling in DHR Architecture With Metamorphic Relations’ by Ruosi Li, Xianglong Kong, Wei Guo, Jingdong Guo, Hongfa Li and Fan Zhang, presents a test segmentation method to transforming multimode ruling to a MT problem. In particular, to alleviate test oracle problem in systems based on dynamic heterogeneous redundancy (DHR) architecture, the authors convert multimode ruling to a MT scenario. For the text input that causes inconsistency of heterogeneous executors, the authors propose the test segmentation method to obtain subsets of follow-up tests, whose execution matrix is collected to examine which conditions can impact the ruling decision, namely, independent conditions. Based on the localization of independent conditions, the authors extract metamorphic relations. The evaluation results on two DHR-based cases show benefits of the proposed method (recommended by Changai Sun). The second paper, ‘Scenario-Driven Metamorphic Testing for Autonomous Driving Simulators’ by Yifan Zhang, Dave Towey, Matthew Pike, Jia Cheng Han, Zhi Quan Zhou, Chenghao Yin, Qian Wang and Chen Xie, presents three metamorphic relation patterns (MRPs) and their related metamorphic relation input patterns (MRIPs) for testing the autonomous driving system (ADS) simulator and ADSs, along with a scenario-driven MT framework for scenario testing that integrates metamorphic exploration (ME) and MT techniques. The successful application of MT to the self-developed simulator from NIO shows the potential of MT in validating ADS simulators, and the case study within a real-world industrial setting shows the strengths and limitations of the framework (recommended by Changai Sun). The third paper, ‘Improving Web Element Localization by Using a Large Language Model’ by Michel Nass, Emil Alégroth and Robert Feldt, presents a study that highlights the potential benefits and challenges of using large language models (LLMs) for web element localization in an automated GUI test case. The study results show that LLMs can be employed to understand the purpose of elements, analyse neighbouring text and evaluate web page structures, enabling more accurate localizations. Using LLMs can reduce manual intervention and script maintenance; however, using LLMs through APIs like GPT-4 introduces delays in test execution due to long response times, and the cost of utilizing the API can be significant and needs to be weighed against the expected reduction in maintenance costs (recommended by Benoit Baudry). We hope that these papers will inspire further research in related directions.