Software Testing for Extended Reality Applications: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2501.08909v2
• Date: Thu, 20 Mar 2025 18:11:30 GMT
• Title: Software Testing for Extended Reality Applications: A Systematic Mapping Study
• Authors: Ruizhen Gu, José Miguel Rojas, Donghwan Shin,
• Abstract summary: Extended Reality (XR) is an emerging technology spanning diverse application domains and offering immersive user experiences.<n>This paper presents the first systematic mapping study on software testing for XR applications.
• Score: 2.3418061477154786
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Requirements-Driven Automated Software Testing: A Systematic Review [13.67495800498868]
  This study synthesizes the current state of REDAST research, highlights trends, and proposes future directions. This systematic literature review ( SLR) explores the landscape of REDAST by analyzing requirements input, transformation techniques, test outcomes, evaluation methods, and existing limitations.
  arXiv  Detail & Related papers  (2025-02-25T23:13:09Z)
• Testing Research Software: An In-Depth Survey of Practices, Methods, and Tools [3.831549883667425]
  Testing research software is challenging due to the software's complexity and to the unique culture of the research software community. This study focuses on test case design, challenges with expected outputs, use of quality metrics, execution methods, tools, and desired tool features.
  arXiv  Detail & Related papers  (2025-01-29T16:27:13Z)
• XRZoo: A Large-Scale and Versatile Dataset of Extended Reality (XR) Applications [36.285311508797825]
  XRZoo contains 12,528 free XR applications, spanning nine app stores, across all XR techniques (i.e., AR, MR, and VR) and use cases.<n>We aim to foster reproducible XR software engineering and security research, enable cross-disciplinary investigations, and also support the development of advanced XR systems.
  arXiv  Detail & Related papers  (2024-12-09T18:49:27Z)
• Systematic Mapping Protocol -- UX Design role in software development process [55.2480439325792]
  We present a systematic mapping protocol for investigating the role of the UX designer in the software development process. We define the research questions, scope, sources, search strategy, selection criteria, data extraction, and analysis methods that we will use to conduct the mapping study.
  arXiv  Detail & Related papers  (2024-02-20T16:56:46Z)
• Large-scale information retrieval in software engineering -- an experience report from industrial application [9.62054859086279]
  We describe an engineering task, test case selection, and illustrate our problem analysis and solution discovery process. We analyze, in the context of the studied company, how test case selection is performed and design a series of experiments evaluating the performance of different IR techniques.
  arXiv  Detail & Related papers  (2023-08-22T19:30:56Z)
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A survey [89.45221564651145]
  This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications. It covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets.
  arXiv  Detail & Related papers  (2022-11-10T13:37:36Z)
• A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks [14.22330197686511]
  Deep Reinforcement Learning (DRL) has been successfully employed in complex testing tasks such as game testing, regression testing, and test case prioritization. DRL frameworks offer well-maintained implemented state-of-the-art DRL algorithms to facilitate and speed up the development of DRL applications. There is no study that empirically evaluates the effectiveness and performance of implemented algorithms in DRL frameworks.
  arXiv  Detail & Related papers  (2022-08-25T14:52:16Z)
• Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning [65.12226891589592]
  This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming.
  arXiv  Detail & Related papers  (2022-08-15T11:08:44Z)
• Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing [7.8729820663730035]
  This paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology. Three variants of exploratory strategies are presented with the algorithms implemented in the automated datamorphic testing tool Morphy. Their capability and cost of discovering borders between classes are evaluated via a set of controlled experiments with manually designed subjects and a set of case studies with real machine learning models.
  arXiv  Detail & Related papers  (2021-10-01T11:47:56Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [76.20963684020145]
  Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. Bizarre approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning.
  arXiv  Detail & Related papers  (2020-11-12T06:41:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.