An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper
Publication date
2021-04-16
Authors
Prasetya, I. S.W.B.
Shirzadehhajimahmood, Samira
Ansari, Saba Gholizadeh
Fernandes, Pedro
Prada, Rui
Editors
Advisors
Supervisors
Document Type
Part of book
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License
taverne
Abstract
This short paper presents an architectural overview of an agent-based framework called iv4XR for automated testing that is currently under development by an H2020 project with the same name. The framework’s intended main use case of is testing the family of Extended Reality (XR) based systems (e.g. 3D games, VR sytems, AR systems), though the approach can indeed be adapted to target other types of interactive systems. The framework is unique in that it is an agent-based system. Agents are inherently reactive, and therefore are arguably a natural match to deal with interactive systems. Moreover, it is also a natural vessel for mounting and combining different AI capabilities, e.g. reasoning, navigation, and learning.
Keywords
Software testing, Three-dimensional displays, Navigation, Extended reality, Interactive systems, Conferences, Games, Taverne, Artificial Intelligence, Software, Safety, Risk, Reliability and Quality
Citation
Prasetya, I S W B, Shirzadehhajimahmood, S, Ansari, S G, Fernandes, P & Prada, R 2021, An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper. in 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)., 9440175, IEEE, pp. 213-217, 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 12/04/21. https://doi.org/10.1109/ICSTW52544.2021.00044, conference