Level Test
Level testing encompasses a broad range of techniques for evaluating the performance and reliability of systems, from software and AI models to physical structures and robotic systems. Current research focuses on developing automated testing frameworks, leveraging AI models (like LLMs and CNNs) for test generation, analysis, and evaluation, and employing techniques such as Structure from Motion for high-precision 3D modeling in physical testing. These advancements aim to improve the efficiency, accuracy, and robustness of testing processes across diverse scientific and engineering domains, ultimately leading to more reliable and trustworthy systems.
Papers
Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
Lev Sorokin, Damir Safin, Shiva Nejati
Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep Learning
Rijun Wang, Guanghao Zhang, Hongyang Chen, Xinye Yu, Yesheng Chen, Fulong Liang, Xiangwei Mou, Bo Wang
Towards Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation: An Empirical Study
Zhijie Wang, Zhehua Zhou, Jiayang Song, Yuheng Huang, Zhan Shu, Lei Ma
On the Effectiveness of LLMs for Manual Test Verifications
Myron David Lucena Campos Peixoto, Davy de Medeiros Baia, Nathalia Nascimento, Paulo Alencar, Baldoino Fonseca, Márcio Ribeiro