Automated Testing of Prevalent 3D User Interactions in Virtual Reality Applications

TL;DR

This paper introduces XRintTest, an automated approach for testing VR interactions by modeling them with Interaction Flow Graphs, achieving high coverage and detecting issues in diverse VR scenes.

Contribution

It presents a novel Interaction Flow Graph abstraction, a comprehensive VR interaction benchmark, and an automated testing method that improves coverage and efficiency over random exploration.

Findings

Achieves 93% interaction coverage in VR scenes.

Performs 12x more effectively and 6x more efficiently than random testing.

Detects runtime exceptions and design issues in VR interactions.

Abstract

Virtual Reality (VR) technologies offer immersive user experiences across various domains, but present unique testing challenges compared to traditional software. Existing VR testing approaches enable scene navigation and interaction activation, but lack the ability to automatically synthesise realistic 3D user inputs (e.g, grab and trigger actions via hand-held controllers). Automated testing that generates and executes such input remains an unresolved challenge. Furthermore, existing metrics fail to robustly capture diverse interaction coverage. This paper addresses these gaps through four key contributions. First, we empirically identify four prevalent interaction types in nine open-source VR projects: fire, manipulate, socket, and custom. Second, we introduce the Interaction Flow Graph, a novel abstraction that systematically models 3D user interactions by identifying targets, actions, and conditions. Third, we construct XRBench3D, a benchmark comprising ten VR scenes that encompass 456 distinct user interactions for evaluating VR interaction testing. Finally, we present XRintTest, an automated testing approach that leverages this graph for dynamic scene exploration and interaction execution. Evaluation on XRBench3D shows that XRintTest achieves great effectiveness, reaching 93% coverage of fire, manipulate and socket interactions across all scenes, and performing 12x more effectively and 6x more efficiently than random exploration. Moreover, XRintTest can detect runtime exceptions and non-exception interaction issues, including subtle configuration defects. In addition, the Interaction Flow Graph can reveal potential interaction design smells that may compromise intended functionality and hinder testing performance for VR applications.