Modeling the MVM-Adapt System by Compositional I/O Abstract State Machines

Silvia Bonfanti, Elvinia Riccobene, Davide Santandrea, Patrizia Scandurra
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Type
Conference paper
Publication
9th International Conference on Rigorous State Based Methods (ABZ'23)

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.

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Reference

% BibTex
@inproceedings{BonfantiRSS23,
  author       = {Silvia Bonfanti and
                  Elvinia Riccobene and
                  Davide Santandrea and
                  Patrizia Scandurra},
  editor       = {Uwe Gl{\"{a}}sser and
                  Jos{\'{e}} Creissac Campos and
                  Dominique M{\'{e}}ry and
                  Philippe A. Palanque},
  title        = {Modeling the MVM-Adapt System by Compositional {I/O} Abstract State
                  Machines},
  booktitle    = {Rigorous State-Based Methods - 9th International Conference, {ABZ}
                  2023, Nancy, France, May 30 - June 2, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14010},
  pages        = {107--115},
  publisher    = {Springer},
  year         = {2023},
  url          = {https://doi.org/10.1007/978-3-031-33163-3\_8},
  doi          = {10.1007/978-3-031-33163-3\_8},
  timestamp    = {Fri, 02 Jun 2023 21:23:53 +0200},
  biburl       = {https://dblp.org/rec/conf/zum/BonfantiRSS23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
ABZ'23

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