How Developers Optimize Virtual Reality Applications: A Study of Optimization Commits in Open Source Unity ProjectsTechnical Track
Sat 29 May 2021 05:40 - 06:00 at Blended Sessions Room 2 - 4.4.2. Defect Prediction: Modeling and Performance
Virtual Reality (VR) is an emerging technique that provides immersive experience for users. Due to the high computation cost of rendering real-time animation twice (for both eyes) and the resource limitation of wearable devices, VR applications often face performance bottlenecks and performance optimization plays an important role in VR software development. Performance optimizations of VR applications can be very different from those in traditional software as VR involves more elements such as graphics rendering and real-time animation. In this paper, we present the first empirical study on 182 real-world performance optimizations from 45 VR software projects. In particular, we manually categorized the optimizations into 11 categories, and applied static analysis to identify how they affect different life-cycle phases of VR applications. Furthermore, we studied the complexity and potential negative effects of performance optimizations, and how optimizations are different between large organizational software projects and smaller personal software projects. Our major findings include: (1) graphics simplification (24.2%), rendering optimization (16.5%), language / API optimization (15.4%), heap avoidance (14.8%), and value caching (12.1%) are the most common categories of performance optimization in VR applications; (2) game logic updates (29.9%) and before-scene initialization (20.1%) are the most common life-cycle phases affected by performance issues; (3) 45.6% of the optimizations have potential negative effects and 30.8% of the optimizations are systematic changes; (4) the distributions of optimization classes are very different between organizational VR projects and personal VR projects.
Fri 28 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:40 - 18:00 | 4.4.2. Defect Prediction: Modeling and PerformanceJournal-First Papers / Technical Track at Blended Sessions Room 2 +12h Chair(s): Ayse Tosun Istanbul Technical University | ||
16:40 20mPaper | On the Need of Preserving Order of Data When Validating Within-Project Defect ClassifiersJournal-First Journal-First Papers Davide Falessi California Polytechnic State University, Jacky Huang California Polytechnic State University, USA, Likhita Narayana California Polytechnic State University, USA, Jennifer Fong Thai California Polytechnic State University, USA, Burak Turhan Monash University Link to publication DOI Pre-print Media Attached | ||
17:00 20mPaper | Using black-box performance models to detect performance regressions under varying workloads: an empirical studyJournal-First Journal-First Papers Lizhi Liao Concordia University, Jinfu Chen Centre for Software Excellence, Huawei, Canada, Heng Li Polytechnique Montréal, Yi Zeng Concordia University, Weiyi Shang Concordia University, Jianmei Guo Alibaba Group, Catalin Sporea ERA Environmental Management Solutions, Andrei Toma ERA Environmental Management Solutions, Sarah Sajedi ERA Environmental Management Solutions Link to publication DOI Pre-print Media Attached | ||
17:20 20mPaper | Predicting Performance Anomalies in Software Systems at Run-timeJournal-First Journal-First Papers Guoliang Zhao Computer Science of Queen's University, Safwat Hassan Thompson Rivers University, Ying Zou Queen's University, Kingston, Ontario, Derek Truong IBM Canada, Toby Corbin IBM UK Pre-print Media Attached | ||
17:40 20mPaper | How Developers Optimize Virtual Reality Applications: A Study of Optimization Commits in Open Source Unity ProjectsTechnical Track Technical Track Fariha Nusrat University of Texas at San Antonio, Foyzul Hassan University of Michigan - Dearborn, Hao Zhong Shanghai Jiao Tong University, Xiaoyin Wang University of Texas at San Antonio Pre-print Media Attached |
Sat 29 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
04:40 - 06:00 | 4.4.2. Defect Prediction: Modeling and PerformanceJournal-First Papers / Technical Track at Blended Sessions Room 2 | ||
04:40 20mPaper | On the Need of Preserving Order of Data When Validating Within-Project Defect ClassifiersJournal-First Journal-First Papers Davide Falessi California Polytechnic State University, Jacky Huang California Polytechnic State University, USA, Likhita Narayana California Polytechnic State University, USA, Jennifer Fong Thai California Polytechnic State University, USA, Burak Turhan Monash University Link to publication DOI Pre-print Media Attached | ||
05:00 20mPaper | Using black-box performance models to detect performance regressions under varying workloads: an empirical studyJournal-First Journal-First Papers Lizhi Liao Concordia University, Jinfu Chen Centre for Software Excellence, Huawei, Canada, Heng Li Polytechnique Montréal, Yi Zeng Concordia University, Weiyi Shang Concordia University, Jianmei Guo Alibaba Group, Catalin Sporea ERA Environmental Management Solutions, Andrei Toma ERA Environmental Management Solutions, Sarah Sajedi ERA Environmental Management Solutions Link to publication DOI Pre-print Media Attached | ||
05:20 20mPaper | Predicting Performance Anomalies in Software Systems at Run-timeJournal-First Journal-First Papers Guoliang Zhao Computer Science of Queen's University, Safwat Hassan Thompson Rivers University, Ying Zou Queen's University, Kingston, Ontario, Derek Truong IBM Canada, Toby Corbin IBM UK Pre-print Media Attached | ||
05:40 20mPaper | How Developers Optimize Virtual Reality Applications: A Study of Optimization Commits in Open Source Unity ProjectsTechnical Track Technical Track Fariha Nusrat University of Texas at San Antonio, Foyzul Hassan University of Michigan - Dearborn, Hao Zhong Shanghai Jiao Tong University, Xiaoyin Wang University of Texas at San Antonio Pre-print Media Attached |