Model‐based testing, test case prioritization and testing of virtual reality applications

Abstract

In this issue, we are pleased to present three papers on model-based testing, test case prioritization and testing of virtual reality applications. The first paper, ‘On transforming model-based tests into code: A systematic literature review’ by Fabiano C. Ferrari, Vinicius H. S. Durelli, Sten F. Andler, Jeff Offutt, Mehrdad Saadatmand and Nils Müllner, presents a systematic literature review based on 30 selected primary studies for computing source code coverage from test sets generated via model-based testing (MBT) approaches. The authors identify some common characteristics and limitations that may impact on MBT research and practice. The authors also discuss implications for future research related to these limitations. The authors find increasing adoption of MBT in industry, increasing application of model-to-code transformations and a complementary increasing need to understand how test cases designed for models achieve coverage on the code. (Recommended by Dan Hao). The second paper, ‘Research on hyper-level of hyper-heuristic framework for MOTCP’ by Junxia Guo, Rui Wang, Jinjin Han and Zheng Li, presents three evaluation strategies for the hyper-level of the hyper-heuristic framework for multi-objective test case prioritization (HH-MOTCP). The experimental results show that the selection method proposed by the authors performs best. In addition, the authors apply 18 selection strategies to dynamically select low-level heuristics during the evolution process of the HH-MOTCP. The results identify the best performing strategy for all test objects. Moreover, using the new strategies at the hyper-level makes HH-MOTCP more effective. (Recommended by Hyunsook Do). The third paper, ‘Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications’ by Stevao Alves de Andrade, Fatima L. S. Nunes and Marcio Eduardo Delamaro, presents an approach to testing virtual reality (VR) applications. The experimental results show that it is feasible to adopting an automated approach of test generation with metamorphic testing and deep reinforcement learning for testing VR applications, especially serving as an effective alternative to identifying crashes related to collision and camera objects in VR applications. (Recommended by Yves Le Traon). We hope that these papers will inspire further research in related directions.