With the increasing complexity and scale of software-intensive systems, model-based system development requires composable system models and composition operators. In line with such a vision, this paper describes our experience in modeling the behavior of the MVM-Adapt, an adaptive version of the Mechanical Ventilator Milano that has been designed, certified, and deployed during the COVID-19 pandemic for treating pneumonia. To keep the complexity of the requirements and models under control, we exploited a compositional modeling technique for discrete-event systems based on Abstract State Machines (ASMs). Essentially, separate ASMs represent the behavior of interacting subsystems of the MVM with their new adaptive functionalities; they can communicate with each other through I/O events, and co-operate by a precise orchestration schema.
(2023). Modeling the MVM-Adapt System by Compositional I/O Abstract State Machines . Retrieved from https://hdl.handle.net/10446/250709
Modeling the MVM-Adapt System by Compositional I/O Abstract State Machines
Bonfanti, Silvia;Scandurra, Patrizia
2023-01-01
Abstract
With the increasing complexity and scale of software-intensive systems, model-based system development requires composable system models and composition operators. In line with such a vision, this paper describes our experience in modeling the behavior of the MVM-Adapt, an adaptive version of the Mechanical Ventilator Milano that has been designed, certified, and deployed during the COVID-19 pandemic for treating pneumonia. To keep the complexity of the requirements and models under control, we exploited a compositional modeling technique for discrete-event systems based on Abstract State Machines (ASMs). Essentially, separate ASMs represent the behavior of interacting subsystems of the MVM with their new adaptive functionalities; they can communicate with each other through I/O events, and co-operate by a precise orchestration schema.File | Dimensione del file | Formato | |
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ABZ2023.pdf
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Descrizione: This is a post-peer-review, pre-copyedit version of an article published in Rigorous State-Based Methods. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-33163-3_8
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