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.

Computer Science educators assessing student programming assignments are typically responsible for two challenging tasks: grading and providing feedback. Producing grades that are fair and feedback that is useful to students is a goal common to most educators. In this context, automated test generation and program repair offer promising solutions for detecting bugs and suggesting corrections in students' code which could be leveraged to inform grading and feedback generation. Previous research on the applicability of these techniques to simple programming tasks (e.g., single-method algorithms) has shown promising results, but their effectiveness for more complex programming tasks remains unexplored. To fill this gap, this paper investigates the feasibility of applying existing test generation and program repair tools for assessing complex programming assignment projects. In a case study using a real-world Java programming assignment project with 296 incorrect student submissions, we found that generated tests were insufficient in detecting bugs in over 50% of cases, while full repairs could only be automatically generated for only 2.1% of submissions. Our findings indicate significant limitations in current tools for detecting bugs and repairing student submissions, highlighting the need for more advanced techniques to support automated assessment of complex assignment projects.

Extended Reality (XR) technologies offer immersive user experiences across diverse application domains, presenting unique testing challenges due to their spatial interaction paradigms. While existing works test XR applications through scene navigation and interaction triggering, they fail to synthesise realistic spatial input via specialised XR devices, such as 6 degrees of freedom controller gestures, that are essential for modern XR user experiences. To address this gap, we present XRintTest, an automated testing framework for Unity-based XR applications. XRintTest starts by constructing an XR User Interaction Graph that models interaction targets and required events. Leveraging this graph, it then automatically explores the XR scene under test and generates user interactions. We evaluated XRintTest on XRBench3D, a novel benchmark comprising seven XR scenes containing 367 distinct 3D user interactions. XRintTest shows great effectiveness, achieving 97% coverage of trigger and grab interactions across all scenes, 9x more effective and 5x more efficient than random exploration, while detecting runtime exceptions and functional defects. We open-sourced our tool and dataset at https://github.com/ruizhengu/XRintTest and https://github.com/ruizhengu/XRBench3D, respectively. A video demo is available on YouTube at https://youtu.be/K0Q6waE47Us.

Abstract Extended Reality (XR) is an emerging technology spanning diverse application domains and offering immersive user experiences. However, its unique characteristics, such as six degrees of freedom interactions, present significant testing challenges distinct from traditional 2D GUI applications, demanding novel testing techniques to build high-quality XR applications. This paper presents the first systematic mapping study on software testing for XR applications. We selected 34 studies focusing on techniques and empirical approaches in XR software testing for detailed examination. The studies are classified and reviewed to address the current research landscape, test facets, and evaluation methodologies in the XR testing domain. Additionally, we provide a repository summarising the mapping study, including datasets and tools referenced in the selected studies, to support future research and practical applications. Our study highlights open challenges in XR testing and proposes actionable future research directions to address the gaps and advance the field of XR software testing.