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
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
Kathy Jang, Nathan Lichtlé, Eugene Vinitsky, Adit Shah, Matthew Bunting, Matthew Nice, Benedetto Piccoli, Benjamin Seibold, Daniel B. Work, Maria Laura Delle Monache, Jonathan Sprinkle, Jonathan W. Lee, Alexandre M. Bayen
The Door and Drawer Reset Mechanisms: Automated Mechanisms for Testing and Data Collection
Kyle DuFrene, Luke Strohbehn, Keegan Nave, Ravi Balasubramanian, Cindy Grimm
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties
Jasmine Chiat Ling Ong, Liyuan Jin, Kabilan Elangovan, Gilbert Yong San Lim, Daniel Yan Zheng Lim, Gerald Gui Ren Sng, Yuhe Ke, Joshua Yi Min Tung, Ryan Jian Zhong, Christopher Ming Yao Koh, Keane Zhi Hao Lee, Xiang Chen, Jack Kian Chng, Aung Than, Ken Junyang Goh, Daniel Shu Wei Ting
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
YuHe Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
Xi Zheng, Aloysius K. Mok, Ruzica Piskac, Yong Jae Lee, Bhaskar Krishnamachari, Dakai Zhu, Oleg Sokolsky, Insup Lee
Context Consistency between Training and Testing in Simultaneous Machine Translation
Meizhi Zhong, Lemao Liu, Kehai Chen, Mingming Yang, Min Zhang
Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests
Max J. van Duijn, Bram M. A. van Dijk, Tom Kouwenhoven, Werner de Valk, Marco R. Spruit, Peter van der Putten
Does GPT-4 pass the Turing test?
Cameron R. Jones, Benjamin K. Bergen
Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency
Eric Zelikman, Wanjing Anya Ma, Jasmine E. Tran, Diyi Yang, Jason D. Yeatman, Nick Haber
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy, Erin Lanus, Laura Freeman