Analyzing Performance Issues of Virtual Reality Applications

TL;DR

This paper presents an empirical study of performance issues in Unreal Engine-based XR applications, identifying common bug patterns and introducing a static analyzer tool to improve performance debugging.

Contribution

It provides the first detailed analysis of performance bottlenecks specific to Unreal Engine XR projects and introduces UEPerfAnalyzer for automated bug detection.

Findings

Identified 14 types of performance bugs in UE XR projects.

Most bugs are related to UE settings, with some from source code.

Developed UEPerfAnalyzer to detect bugs in configuration and code.

Abstract

Extended Reality (XR) includes Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR). XR is an emerging technology that simulates a realistic environment for users. XR techniques have provided revolutionary user experiences in various application scenarios (e.g., training, education, product/architecture design, gaming, remote conference/tour, etc.). Due to the high computational cost of rendering real-time animation in limited-resource devices and constant interaction with user activity, XR applications often face performance bottlenecks, and these bottlenecks create a negative impact on the user experience of XR software. Thus, performance optimization plays an essential role in many industry-standard XR applications. Even though identifying performance bottlenecks in traditional software (e.g., desktop applications) is a widely explored topic, those approaches cannot be directly applied within XR software due to the different nature of XR applications. Moreover, XR applications developed in different frameworks such as Unity and Unreal Engine show different performance bottleneck patterns and thus, bottleneck patterns of Unity projects can't be applied for Unreal Engine (UE)-based XR projects. To fill the knowledge gap for XR performance optimizations of Unreal Engine-based XR projects, we present the first empirical study on performance optimizations from seven UE XR projects, 78 UE XR discussion issues and three sources of UE documentation. Our analysis identified 14 types of performance bugs, including 12 types of bugs related to UE settings issues and two types of CPP source code-related issues. To further assist developers in detecting performance bugs based on the identified bug patterns, we also developed a static analyzer, UEPerfAnalyzer, that can detect performance bugs in both configuration files and source code.