Silvia Lizeth Tapia Tarifa

Digital Twins (DTs) are increasingly used to manage systems under fluctuating demand, yet many remain static and cannot adjust their internal models or control policies as the targeted real system changes. We present a self-adaptive DT architecture that supports runtime reconfiguration of resources. The architecture integrates semantic reflection to keep the DT’s runtime model aligned with the structural representation of the real system, lifecycle-based state management to trigger reconfiguration at appropriate times, and penalty-guided optimisation for decision-making under constraints, balancing resilience and operational cost when capacity must be reorganised. We realise the approach in DynResDT, a resilient hospital ward prototype for bed bay allocation of patients. Through simulation with realistic patient-arrival patterns and stress peaks, the DT maintains model consistency, opens and closes overflow capacity when needed, and allocates patients while minimising costly room usage and unnecessary moves. Our results show a practical trade-off between correctness and responsiveness: timely and principled adaptions offset potential overhead through reflection and lifecycle logic. The architectural pattern is applicable beyond healthcare to other dynamic resource-management domains.

With the servitization of business, understanding how users experience services becomes a crucial success factor for companies. Therefore, there is a need to include feedback from user experiences in the software engineering process. Behavioral models of user journeys, describing how users experience their interaction with a service, can provide insights and potentially improve services. In this paper, we investigate techniques that allow the automatic generation of behavioral models from user interactions with a service, recorded in an event log. We first compare two established techniques that generate behavioral models from a given event log: automata learning and process mining. Afterward, we present a novel, hybrid method that combines both automata learning and process mining methods to overcome their limitations. For the existing techniques, we present methods to learn models of user journeys and evaluate the accuracy of the resulting models. We then compare these techniques with our novel method for the automatic extraction of user journey models from the event logs of digital services. We assess the practical applicability of all techniques by evaluating real-world applications. Our results show that process mining techniques rely on expert knowledge, while automata learning techniques depend on the distribution of events in the given event log. We further show that the proposed hybrid technique combines the strengths of both process mining and automata learning, automatically selecting the best method and parameter settings for a given event log to learn very accurate models.

Self-adaptive systems can be realised as layered systems with a feedback loop: a managing system monitors a managed system, updates an internal model, and adjusts the managed system by means of controllers to maintain given requirements. For example, a digital twin coupled with its physical twin constitute such a self-adaptive system. As the managed system shifts between different stages in its lifecycle, these requirements, as well as the associated analysers and controllers, may need to change. The exact triggers for such shifts in a managed system are often hard to predict: they may be difficult to describe or even unknown. However, the shifts can generally be observed once they have occurred, in terms of changes in the system behaviour. This paper proposes an automated method for self-adaptation in self-adaptive systems to address shifts between lifecycle stages in a managed system. Our method is based on declarative descriptions of lifecycle stages for assets in a managed system and their associated counterparts in the managing system. Declarative lifecycle management provides a high-level, flexible method of self-adaptation for self-adaptive systems to reflect disruptive shifts between stages in a managed system.

Service providers may systematically record and analyze how users interact with a service. User journeys model this interaction from the user’s perspective. We can think of user journeys in terms of multi-party event logs in which events are triggered by two independent parties, the user and the service provider, both controlling their share of actions. Multi-party event logs enable the automated construction of weighted two-player games, which can be used to analyze the user journeys. However, the games constructed for complex user journeys may get large, which makes visual understanding challenging. To reduce the size of these games, we develop an analysis technique for a game’s decision boundary , at which the outcome of the game is determined. Decision boundaries identify subgames that are equivalent to the full game with respect to the final outcome of user journeys. The paper formalizes decision boundaries for user journey games with history refinements, and studies properties of decision boundaries that simplify their analysis. The decision boundary analysis from multi-party event logs has been implemented in a modular tool, which can be connected to existing process mining pipelines, and evaluated on benchmark event logs revealing changes in the user journey and the interaction between users and service provider.

Improved autonomy in robotic systems is needed for innovation in, e.g., the marine sector. Autonomous robots that are let loose in hazardous environments, such as underwater, need to handle uncertainties that stem from both their environment and internal state. While self-adaptation is crucial to cope with these uncertainties, bad decisions may cause the robot to get lost or even to cause severe environmental damage. Autonomous, self-adaptive robots that operate in uncontrolled environments full of uncertainties need to be reliable! Since these uncertainties are hard to replicate in test deployments, we need methods to formally analyse self-adaptive robots operating in uncontrolled environments. In this article, we show how feature-oriented techniques can be used to formally model and analyse self-adaptive robotic systems in the presence of such uncertainties. Self-adaptive systems can be organised as two-layered systems with a managed subsystem handling the domain concerns and a managing subsystem implementing the adaptation logic. We consider a case study of an Autonomous Underwater Vehicle (AUV) for pipeline inspection, in which the managed subsystem of the AUV is modelled as a family of systems, where each family member corresponds to a valid configuration of the AUV which can be seen as an operating mode of the AUV’s behaviour. The managing subsystem of the AUV is modelled as a control layer that is capable of dynamically switching between such valid configurations, depending on both environmental and internal uncertainties. These uncertainties are captured in a probabilistic and highly configurable model. Our modelling approach allows us to exploit powerful formal methods for feature-oriented systems, which we illustrate by analysing safety properties, energy consumption, and multi-objective properties, as well as performing parameter synthesis to analyse to what extent environmental conditions affect the AUV. The case study is realised in the probabilistic feature-oriented modelling language and verification tool ProFeat, and in particular exploits family-based probabilistic and parametric model checking.