VRExplorer: A Model-based Approach for Automated Virtual Reality Scene Testing
This program is tentative and subject to change.
With the proliferation of Virtual Reality (VR) markets, VR applications are rapidly expanding in scale and complexity, thereby driving an urgent need for assuring VR software quality. Different from traditional mobile applications and computer software, VR testing faces unique challenges due to diverse interactions with virtual objects, complex 3D virtual environments, and intricate sequences to complete tasks. All of these emerging challenges hinder existing VR testing tools from effectively and systematically testing VR applications. In this paper, we present VRExplorer, a novel model-based testing tool to effectively interact with diverse virtual objects and explore complex VR scenes. Particularly, we design the Entity, Action, and Task (EAT) framework for modeling diverse VR interactions in a generic way. Built upon the EAT framework, we then present the VRExplorer agent, which can achieve effective scene exploration by incorporating meticulously designed path-finding algorithms into Unity’s NavMesh. Moreover, the VRExplorer agent can also systematically execute interaction decisions on top of the Probabilistic Finite State Machine (PFSM). Experimental evaluation on 11 representative VR projects shows that VRExplorer consistently outperforms the state-of-the-art (SOTA) approach VRGuide by achieving significantly higher coverage and better efficiency. Specifically, VRExplorer yields up to 122.8% and 52.8% improvements over VRGuide in terms of executable lines of code (ELOC) coverage and method (function) coverage, respectively. Furthermore, ablation results also verify the essential contributions of each designed module. More importantly, our VRExplorer has successfully detected two functional bugs and one non-functional bug from real-world projects.
This program is tentative and subject to change.
Mon 17 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | ADPerf: Investigating and Testing Performance in Autonomous Driving Systems Research Papers Tri Minh-Triet Pham Concordia University, Diego Elias Costa Concordia University, Canada, Weiyi Shang University of Waterloo, Jinqiu Yang Concordia University | ||
11:10 10mTalk | VRTestSniffer: Test Smell Detector for Virtual Reality (VR) Software Projects Research Papers Faraz Gurramkonda University of Michigan-Dearborn, Avishak Chakroborty University of Michigan-Dearborn, Bruce Maxim University of Michigan - Dearborn, Mohamed Wiem Mkaouer University of Michigan - Flint, Foyzul Hassan University of Michigan at Dearborn | ||
11:20 10mTalk | A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems Research Papers Stefano Carlo Lambertenghi Technische Universität München, fortiss GmbH, Mirena Flores Valdez Technical University of Munich, Andrea Stocco Technical University of Munich, fortiss Pre-print | ||
11:30 10mTalk | On the Robustness Evaluation of 3D Obstacle Detection Against Specifications in Autonomous Driving Research Papers Tri Minh-Triet Pham Concordia University, Bo Yang Concordia University, Jinqiu Yang Concordia University | ||
11:40 10mTalk | TARGET: Traffic Rule-Based Test Generation for Autonomous Driving via Validated LLM-Guided Knowledge Extraction Journal-First Track Yao Deng Macquarie University, Zhi Tu Purdue University, Jiaohong Yao Macquarie University, Mengshi Zhang TensorBlock, Tianyi Zhang Purdue University, Xi Zheng Macquarie University | ||
11:50 10mTalk | IMUFUZZER: Resilience-based Discovery of Signal Injection Attacks on Robotic Aerial Vehicles Research Papers Sudharssan Mohan University of Texas at Dallas, Kyeongseok Yang Korea University, Zelun Kong The University of Texas at Dallas, Yonghwi Kwon University of Maryland, Junghwan Rhee University of Central Oklahoma, Tyler Summers University of Texas at Dallas, Hongjun Choi DGIST, Heejo Lee Korea University, Chung Hwan Kim University of Texas at Dallas | ||
12:00 10mTalk | Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs Research Papers Dingji Wang Fudan University, You Lu Fudan University, Bihuan Chen Fudan University, Shuo Hao Fudan University, Haowen Jiang Fudan University, China, Yifan Tian Fudan University, Xin Peng Fudan University | ||
12:10 10mResearch paper | VRExplorer: A Model-based Approach for Automated Virtual Reality Scene Testing Research Papers Zhu Zhengyang Sun Yat-sen University, Hong-Ning Dai Hong Kong Baptist University, Hanyang Guo School of Software Engineering, Sun Yat-sen University, Zeqin Liao Sun Yat-sen University, Zibin Zheng Sun Yat-sen University Pre-print | ||
12:20 10mTalk | When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We? Research Papers An Guo Nanjing University, Shuoxiao Zhang Nanjing University, Enyi Tang Nanjing University, Xinyu Gao , Haomin Pang Guangzhou University, Haoxiang Tian Nanyang Technological University, Singapore, Yanzhou Mu , Wu Wen Guangzhou University, Chunrong Fang Nanjing University, Zhenyu Chen Nanjing University Pre-print |