Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Applications
The quality of Virtual Reality (VR) applications is vital, particularly the rendering quality of the VR Graphical User Interface (GUI). Different from traditional two-dimensional (2D) applications, VR applications create a 3D digital scene for users, by rendering two distinct 2D images for the user’s left and right eyes, respectively. Stereoscopic visual inconsistency (denoted as “SVI”) issues, however, undermine the rendering process of the user’s brain, leading to user discomfort and even adverse health effects. Such issues commonly exist in VR applications but remain under-explored. To comprehensively understand the SVI issues, we conduct an empirical analysis on 282 SVI bug reports collected from 15 VR platforms, summarizing 15 types of manifestations of the issues. The empirical analysis reveals that automatically detecting SVI issues is challenging, mainly because: (1) lack of training data; (1) the manifestations of SVI issues are diverse, complicated, and often application-specific; (2) most accessible VR applications are closed-source commercial software, we have no access to code, scene configurations, etc. for issue detection. Our findings imply that the existing pattern-based supervised classification approaches may be inapplicable or ineffective in detecting the SVI issues.
To counter these challenges, we propose a unsupervised black-box testing framework named StereoID to identify the stereoscopic visual inconsistencies, based only on the rendered GUI states. StereoID generates a synthetic right-eye image based on the actual left-eye image and computes distances between the synthetic right-eye image and the actual right-eye image to detect SVI issues. We propose a depth-aware left-right-eye image translator to power the image generation process, which captures the expected perspective shifts between left-eye and right-eye images. We build a large-scale unlabeled VR stereo screenshot dataset with larger than 170K images from real-world VR applications, which can be utilized to train our depth-aware leftright-eye image translator and evaluate the whole testing framework StereoID. After substential experiments, depth-aware left-right-eye image translator demonstrate superior performance in generating stereo images, outpacing traditional architectures. It achieved the lowest average L1 and L2 losses and the highest SSIM score, signifying its prowess in pixel-level accuracy and structural consistency for VR applications. StereoID further demonstrates its power for detecting SVI issues in both user-reported dataset and wild VR applications. In summary, this novel framework enables effective detection of elusive SVI issues, benefiting the quality of VR applications.
Thu 18 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
15:30 - 16:00 | |||
15:30 30mPoster | Can GPT-4 Replicate Empirical Software Engineering Research? Posters Jenny T. Liang Carnegie Mellon University, Carmen Badea Microsoft Research, Christian Bird Microsoft Research, Robert DeLine Microsoft Research, Denae Ford Microsoft Research, Nicole Forsgren Microsoft Research, Thomas Zimmermann Microsoft Research | ||
15:30 30mPoster | Evaluating Directed Fuzzers: Are We Heading in the Right Direction? Posters Tae Eun Kim KAIST, Jaeseung Choi Sogang University, Seongjae Im KAIST, Kihong Heo KAIST, Sang Kil Cha KAIST Link to publication Media Attached File Attached | ||
15:30 30mPoster | Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection Posters Yuxi Li Huazhong University of Science and Technology, Yi Liu Nanyang Technological University, Gelei Deng Nanyang Technological University, Ying Zhang Virginia Tech, Wenjia Song Virginia Tech, Ling Shi Nanyang Technological University, Kailong Wang Huazhong University of Science and Technology, Yuekang Li The University of New South Wales, Yang Liu Nanyang Technological University, Haoyu Wang Huazhong University of Science and Technology | ||
15:30 30mPoster | Do Words Have Power? Understanding and Fostering Civility in Code Review Discussion Posters Md Shamimur Rahman University of Saskatchewan, Canada, Zadia Codabux University of Saskatchewan, Chanchal K. Roy University of Saskatchewan, Canada | ||
15:30 30mPoster | CodePlan: Repository-level Coding using LLMs and Planning Posters Ramakrishna Bairi Microsoft Research, India, Atharv Sonwane Microsoft Research, India, Aditya Kanade Microsoft Research, India, Vageesh D C Microsoft Research, India, Arun Iyer Microsoft Research, India, Suresh Parthasarathy Microsoft Research, India, Sriram Rajamani Microsoft Research Indua, B. Ashok Microsoft Research. India, Shashank Shet Microsoft Research. India | ||
15:30 30mPoster | Understanding and Detecting Annotation-induced Faults of Static Analyzers Posters Huaien Zhang The Hong Kong Polytechnic University, Southern University of Science and Technology, Yu Pei The Hong Kong Polytechnic University, Shuyun Liang Southern University of Science and Technology, Shin Hwei Tan Concordia University | ||
15:30 30mPoster | Partial Solution Based Constraint Solving Cache in Symbolic Execution Posters Ziqi Shuai School of Computer, National University of Defense Technology, China, Zhenbang Chen College of Computer, National University of Defense Technology, Kelin Ma School of Computer, National University of Defense Technology, China, Kunlin Liu School of Computer, National University of Defense Technology, China, Yufeng Zhang Hunan University, Jun Sun School of Information Systems, Singapore Management University, Singapore, Ji Wang School of Computer, National University of Defense Technology, China | ||
15:30 30mPoster | Characterizing Python Library Migrations Posters Mohayeminul Islam University of Alberta, Ajay Jha North Dakota State University, Ildar Akhmetov Northeastern University, Sarah Nadi New York University Abu Dhabi, University of Alberta File Attached | ||
15:30 30mPoster | DeSQL: Interactive Debugging of SQL in Data-Intensive Scalable Computing Posters | ||
15:30 30mPoster | BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection Posters | ||
15:30 30mPoster | Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Applications Posters Shuqing Li The Chinese University of Hong Kong, Cuiyun Gao Harbin Institute of Technology, Jianping Zhang The Chinese University of Hong Kong, Yujia Zhang Harbin Institute of Technology, Yepang Liu Southern University of Science and Technology, Jiazhen Gu The Chinese University of Hong Kong, Yun Peng The Chinese University of Hong Kong, Michael Lyu The Chinese University of Hong Kong | ||
15:30 30mPoster | CORE: Resolving Code Quality Issues Using LLMs Posters Nalin Wadhwa Microsoft Research, India, Jui Pradhan Microsoft Research, India, Atharv Sonwane Microsoft Research, India, Surya Prakash Sahu Microsoft Research, India, Nagarajan Natarajan Microsoft Research India, Aditya Kanade Microsoft Research, India, Suresh Parthasarathy Microsoft Research, India, Sriram Rajamani Microsoft Research Indua | ||
15:30 30mPoster | Abstraction-Aware Inference of Metamorphic Relations Posters Agustin Nolasco University of Rio Cuarto, Facundo Molina IMDEA Software Institute, Renzo Degiovanni Luxembourg Institute of Science and Technology, Alessandra Gorla IMDEA Software Institute, Diego Garbervetsky Departamento de Computación, FCEyN, UBA, Mike Papadakis University of Luxembourg, Sebastian Uchitel Imperial College and University of Buenos Aires, Nazareno Aguirre University of Rio Cuarto and CONICET, Marcelo F. Frias Dept. of Software Engineering Instituto Tecnológico de Buenos Aires | ||
15:30 30mPoster | State Reconciliation Defects in Infrastructure as Code Posters Md Mahadi Hassan Auburn University, John Salvador Auburn University, Shubhra Kanti Karmaker Santu Auburn University, Akond Rahman Auburn University | ||
This room is conjoined with the Foyer to provide additional space for the coffee break, and hold poster presentations throughout the event.