Different approaches for testing body sensor network applications
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| Název: | Different approaches for testing body sensor network applications |
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| Autoři: | Silva, Samira, Diniz Caldas, Ricardo, 1994, Pelliccione, Patrizio, 1975, Bertolino, Antonia |
| Zdroj: | Journal of Systems and Software Paper_JSS. 223 |
| Témata: | Body sensor networks, Model-based testing, Discrete-time Markov chain, Combinatorial testing |
| Popis: | Body Sensor Networks (BSNs) offer a cost-effective way to monitor patients’ health and detect potential risks. Despite the growing interest attracted by BSNs, there is a lack of testing approaches for them. Testing a Body Sensor Network (BSN) is challenging due to its evolving nature, the complexity of sensor scenarios and their fusion, the potential necessity of third-party testing for certification, and the need to prioritize critical failures given limited resources. This paper addresses these challenges by proposing three BSN testing approaches: PASTA, ValComb, and TransCov. These approaches share common characteristics, which are described through a general framework called GATE4BSN. PASTA simulates patients with sensors and models sensor trends using a Discrete Time Markov Chain (DTMC). ValComb explores various health conditions by considering all sensor risk level combinations, while TransCov ensures full coverage of DTMC transitions. We empirically evaluate these approaches, comparing them with a baseline approach in terms of failure detection. The results demonstrate that PASTA, ValComb, and TransCov uncover previously undetected failures in an open-source BSN and outperform the baseline approach. Statistical analysis reveals that PASTA is the most effective, while ValComb is 76 times faster than PASTA and nearly as effective. |
| Popis souboru: | electronic |
| Přístupová URL adresa: | https://research.chalmers.se/publication/545147 https://research.chalmers.se/publication/545147/file/545147_Fulltext.pdf |
| Databáze: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | Body Sensor Networks (BSNs) offer a cost-effective way to monitor patients’ health and detect potential risks. Despite the growing interest attracted by BSNs, there is a lack of testing approaches for them. Testing a Body Sensor Network (BSN) is challenging due to its evolving nature, the complexity of sensor scenarios and their fusion, the potential necessity of third-party testing for certification, and the need to prioritize critical failures given limited resources. This paper addresses these challenges by proposing three BSN testing approaches: PASTA, ValComb, and TransCov. These approaches share common characteristics, which are described through a general framework called GATE4BSN. PASTA simulates patients with sensors and models sensor trends using a Discrete Time Markov Chain (DTMC). ValComb explores various health conditions by considering all sensor risk level combinations, while TransCov ensures full coverage of DTMC transitions. We empirically evaluate these approaches, comparing them with a baseline approach in terms of failure detection. The results demonstrate that PASTA, ValComb, and TransCov uncover previously undetected failures in an open-source BSN and outperform the baseline approach. Statistical analysis reveals that PASTA is the most effective, while ValComb is 76 times faster than PASTA and nearly as effective. |
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| ISSN: | 01641212 |
| DOI: | 10.1016/j.jss.2025.112336 |