HoS-ML: Socio-Technical System ADL Dedicated to Human Vulnerability Identification
Due to the increasing complexity of modern systems, the level of responsibility dedicated to the human operator has grown, particularly in Socio-Technical Systems (STS) where humans are considered as subsystems. Like every system, the human operator can fail by behaving in undesired ways, and consequently have a negative impact on the system. Thus, to improve the resilience of the overall system, it is necessary to manage the vulnerability of humans. In this paper we present an approach to assess human vulnerabilities in an STS through its architecture. We propose a model that describes the STS, based on human characteristics having a significant impact on human vulnerabilities. We define an assessment metric for each characteristic. We propose an approach allowing not only to assess the vulnerability of a specific human in the system, but also to understand how a vulnerability propagates through the system. We implemented this approach with a dedicated architecture description language, called Hos-ML, allowing the architect to deal with STS vulnerabilities.
Paul Perrotin, Nicolas Belloir, Salah Sadou, David Hairion, Antoine Beugnard. HoS-ML: Socio-Technical System ADL Dedicated to Human Vulnerability Identification. 26th International Conference on Engineering of Complex Computer Systems ( ICECCS 2022), Mar 2022, Hiroshima, Japan. ⟨hal-03637271⟩
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