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To contribute to multiconcern assurance, we focus on system design and present a high-level process that builds on top of the synergy between qualitative and quantitative dependability analysis techniques, which have been used for mono- as well as multiconcern analysis.

Multi-parameter monitoring systems in Intensive Care Units (ICUs) monitor the clinical condition of critical state patients. These Systems of Systems (SoS) comprise a set of Constituent Systems (CS) to measure parameters such as heart rate, respiratory frequency, and temperature. Due to the critical nature and relevance of ICUs, such SoS shall be as reliable as possible. That is especially true in emergencies, as the COVID-19 outbreak that resulted in the burden of health care systems. We developed a modular and parametric model to perform reliability analysis and to provide insights to assist the management of multi-parameter monitoring systems used in ICUs, also considering maintenance. First, we modeled a multi-parameter monitoring system for ICUs using the CHESS methodology and modeling language. Afterward, we performed a reliability analysis using the CHESS state-based analysis plugin for different scenarios. We identified that the main power supply and the battery are the CS that present the most negative impacts on reliability. In emergencies, reduced time ranges of planned maintenance, when applied during a short period, showed to be promising strategies.

Model-based evaluation is extensively used to estimate performance and reliability of dependable systems. Traditionally, those systems were small and self-contained, and the main challenge for model-based evaluation has been the efficiency of the solution process. Recently, the problem of specifying and maintaining complex models has increasingly gained attention, as modern systems are characterized by many components and complex interactions. Components share similarities, but also exhibit variations in their behavior due to different configurations or roles in the system. From the modeling perspective, variations lead to replicating and altering a small set of base models multiple times. Variability is taken into account only informally, by defining a sample model and explaining its possible variations. In this paper we address the problem of including variability in performability models, focusing on Stochastic Activity Networks (SANs). We introduce the formal definition of Stochastic Activity Networks Templates (SAN-T), a formalism based on SANs with the addition of variability aspects. Differently from other approaches, parameters can also affect the structure of the model, like the number of cases of activities. We apply the SAN-T formalism to the modeling of the backbone network of an environmental monitoring infrastructure. In particular, we show how existing SAN models from the literature can be generalized using the newly introduced formalism.

A dramatic shift in system complexity is occurring, bringing monolithic system designs to be progressively replaced by modular approaches. In the latest years, this trend has been emphasized by the system of systems (SoS) concept, in which a complex system or application is the result of the integration of many independent, autonomous constituent systems (CS), brought together in order to satisfy a global goal under certain rules of engagement. The overall behavior of the SoS, emerging from such complex interactions and dependencies, poses several threats in terms of dependability, timeliness, and security, due to the challenging operating and environmental conditions caused by mobility, wireless connectivity, and the use of off-the-shelf components. Referring to our experience in mobile safety-critical applications gained from three different research projects, in this paper, we illustrate the challenges and benefits posed by the adoption of an SoS approach in designing, developing, and maintaining mobile safety-critical applications, and we report on some possible solutions.

With the advent of recent technological advances, more demanding tele-immersive applications have started to emerge. In the World Opera application, artists from different opera houses across the globe can participate in a single united performance, and interact almost as if they were co-located. One of the main design challenges in this application domain is to assess to what extent the inevitable failures of some of the numerous and complex hardware, software, and network components affect the quality of experience for the user. This challenge cannot be addressed by traditional system-centric methods for dependability evaluation, which do not take personalized user perspective into account when considering meaningful and acceptable degradation of services. In this paper, we propose a novel method to assess the quality of experience in presence of failures, based on a new metric called perceived reliability . The method takes the human perspective into account and allows considering factors such as human perception of video and audio, characteristics of the audience, as well as performance elements and artistic content. This method can help system designers and engineers compare architectural variants and determine the dependability budget. We show the feasibility of our method by applying it to a World Opera performance. To this end, we construct a SAN-based model and run simulations in the Mobius framework. The obtained results provide useful guidelines for system engineers towards improving the quality of experience of World Opera performances despite the presence of failures.

Session management in distributed Internet services is traditionally based on username and password, explicit logouts and mechanisms of user session expiration using classic timeouts. Emerging biometric solutions allow substituting username and password with biometric data during session establishment, but in such an approach still a single verification is deemed sufficient, and the identity of a user is considered immutable during the entire session. Additionally, the length of the session timeout may impact on the usability of the service and consequent client satisfaction. This paper explores promising alternatives offered by applying biometrics in the management of sessions. A secure protocol is defined for perpetual authentication through continuous user verification. The protocol determines adaptive timeouts based on the quality, frequency and type of biometric data transparently acquired from the user. The functional behavior of the protocol is illustrated through Matlab simulations, while model-based quantitative analysis is carried out to assess the ability of the protocol to contrast security attacks exercised by different kinds of attackers. Finally, the current prototype for PCs and Android smartphones is discussed.

In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation actions. Inherent challenges exists to identify good and timely decision strategies to improve resilience of end-node services. In this paper we present a framework, called ODDR (Observation, Diagnosis, Decision, Remediation), for improving resilience of network based services through integration of self-adaptive monitoring services, network diagnosis, decision actions, and finally execution (and monitoring) of remediation actions. We detail the motivations to the ODDR design, then we present its architecture, and finally we describe our current activities towards the realization and assessment of the framework services and the main results currently achieved.

The injection of software faults in source code requires accurate knowledge of the programming language, both to craft faults and to identify injection locations. As such, fault injection and code mutation tools are typically tailored for a specific language and have limited extensibility. In this paper we present a model-driven approach to craft and inject software faults in source code. While its concrete application is presented for Java, the workflow we propose does not depend on a specific programming language. Following Model-Driven Engineering principles, the faults and the criteria to select injection locations are described using structured, machine-readable specifications based on a domain-specific language. Then, automated transformations craft artifacts based on OCL and Java, which represent the faults to be injected and are able to select the candidate injection locations. Finally, artifacts are executed against the target source code, performing the injection in the desired locations. We devise a supporting tool and exercise the approach injecting 13 different kinds of software faults in the Java source code of six different projects.

Cyber-physical systems are characterized by strong interactions between their physical and computation parts. The increasing complexity of such systems, now used in numerous application domains (e.g., aeronautics, healthcare), in conjunction with hard to predict surrounding environments or the use of non-traditional middleware and with the presence of non-deterministic or non-explainable software outputs, tend to make traditional Verification and Validation (V&V) techniques ineffective. This paper presents the H2020 ADVANCE project, which aims precisely at addressing the Verification and Validation challenges that the next-generation of cyber-physical systems bring, by exploring techniques, methods and tools for achieving the technical objective of improving the overall efficiency and effectiveness of the V&V process. From a strategic perspective, the goal of the project is to create an international network of expertise on the topic of V&V of cyber-physical systems.