Towards the Systematic Testing of Virtual Reality Programs

Andrade, Sónia Cristina da Silva; Nunes, Fátima L. S.; Delamaro, Márcio Eduardo

doi:10.1109/svr.2019.00044

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…The individual results of the statistical tests presented for all objects of the four applications investigated can be found in the labpackage of the experiment [43], which also contains all the information regarding the raw results, spreadsheets and auxiliary scripts used to consolidate the results presented in this paper.…”

Section: Resultsmentioning

confidence: 99%

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Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

de Andrade,

Nunes,

Delamaro

2023

Software Testing Verif & Rel

Self Cite

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SummaryDespite the rapid growth and popularization of virtual reality (VR) applications, which have enabled new concepts for handling and solving existing problems through VR in various domains, practices related to software engineering have not kept up with this growth. Recent studies indicate that one of the topics that is still little explored in this area is software testing, as VR applications can be built for practically any type of purpose, making it difficult to generalize knowledge to be applied. In this paper, we present an approach that combines metamorphic testing, agent‐based testing and machine learning to test VR applications, focusing on finding collision and camera‐related faults. Our approach proposes the use of metamorphic relations to detect faults in collision and camera components in VR applications, as well as the use of intelligent agents for the automatic generation of test data. To evaluate the proposed approach, we conducted an experimental study on four VR applications, and the results showed an of the solution ranging from 93% to 69%, depending on the complexity of the application tested. We also discussed the feasibility of extending the approach to identify other types of faults in VR applications. In conclusion, we discussed important trends and opportunities that can benefit both academics and practitioners.

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Section: Resultsmentioning

confidence: 99%

“…We have observed in previous studies [6,7] that the practice of automated software testing is still underdeveloped in this field. The testing activity is predominantly carried out using manual activities or by receiving feedback from endusers, which can be a risky strategy depending on the objective or criticality of the application [8].…”

Section: Introductionmentioning

confidence: 99%

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

de Andrade,

Nunes,

Delamaro

2023

Software Testing Verif & Rel

Self Cite

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“…We discussed the main challenges of software testing practice in the VR domain [11]. Some of the critical issues related to the quality of these systems were pointed out and possible solutions were also discussed that could be used and adapted to deal with such issues.…”

Section: Software Testing Practice In the Vr Domainmentioning

confidence: 99%

A fault-based testing approach for VR applications

Andrade¹,

Nunes²,

Delamaro³

2019

Revista CI

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Technologies such as Virtual Reality (VR) have emerged, allowing the development of three-dimensional environments with real-time interaction. VR systems lack similarities between traditional programs, which makes it ineffective to apply traditional software testing criteria in then. Considering this motivation and the acceptance between researchers and engineers that quality is an essential factor in software development, in this paper we examine software testing practices available for the VR domain and present the possibilities for improvement to provide an automated software testing approach that can contribute to the quality assurance of VR applications.

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“…By utilizing a person's motion trajectories as a unique signature, we can implement task-oriented behavioral biometric identification of users interacting in VR environments, which allows for seamless continuous authentication. Behavioral biometrics methods based on deep learning perform better when fed whole or almost full user trajectories, but worse when fed short segments starting from the beginning of the job [9]. Both academics and businesses are showing an interest in the cutting-edge VR applications that are currently under development.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

Manukonda

2024

IJCE

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One of the most anticipated features of 5G and subsequent networks is wireless virtual reality (VR), which promises to transform human interaction via its immersive experiences and game-changing capabilities. Wireless virtual reality systems, and VR games in particular, are notoriously slow due to rendering issues. But most academics don't care about data correlation or real-time rendering. Using mobile edge computing (MEC) and mmWave-enabled wireless networks, we provide an adaptive VR system that enables high-quality wireless VR. By using this architecture, VR rendering operations may be adaptively offloaded to MEC servers in real-time, resulting in even greater performance advantages via caching.The limited processing power of VR devices, the need for a high quality of experience (QoE), and the small latency in VR activities make it difficult to connect wireless VR consumers to high-quality VR content in real-time. To solve these problems, we provide a wireless VR network that is enabled by MEC. This network makes use of recurrent neural networks (RNNs) to provide real-time predictions about each user's field of vision (FoV). It is feasible to simultaneously move the rendering of virtual reality material to the memory of the MEC server. To improve the long-term VR users' quality of experience (QoE) while staying within the VR interaction latency limitation, we provide decoupling deep reinforcement learning algorithms that are both centrally and distributedly run, taking into consideration the connection between requests' fields of vision and their locations. When compared with rendering on VR headsets, our proposed MEC rendering techniques and DRL algorithms considerably improve VR users' long-term experience quality and reduce VR interaction latency, according to the simulation results.

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Towards the Systematic Testing of Virtual Reality Programs

Cited by 7 publications

References 30 publications

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

A fault-based testing approach for VR applications

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

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