Cost Reduction on Testing Evolving Cancer Registry System

Isaku, Erblin; Sartaj, Hassan; Laaber, Christoph; Yue, Tao; Ali, Shaukat; Schwitalla, Thomas; Nygård, Jan F.

doi:10.1109/icsme58846.2023.00065

2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) 2023

DOI: 10.1109/icsme58846.2023.00065

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Cost Reduction on Testing Evolving Cancer Registry System

Erblin Isaku,

Hassan Sartaj,

Christoph Laaber

et al.

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2024

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MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

Sartaj,

Ali,

Gjøby

2024

ACM Trans. Softw. Eng. Methodol.

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Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants, and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City’s health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in Oslo City’s context using five widely-used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT’s ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.

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MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

Sartaj,

Ali,

Gjøby

2024

ACM Trans. Softw. Eng. Methodol.

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show abstract

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Cost Reduction on Testing Evolving Cancer Registry System

Cited by 1 publication

References 29 publications

MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

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