• First empirical comparison of explanation strategies in smart environments: We conduct an empirical study (N = 159) to systematically compare three explanation: no explanation, static explanations (fixed and identical across users and contexts), and context-adapted explanations (dynamically adjusting content based on user preferences and situational factors). To the best of our knowledge, this is the first empirical study in the rule-based smart environments domain to directly contrast static and context-adapted explanations in terms of their impact on task performance, system understanding, usability, and perceived explanation quality. • Explanations boost performance and understanding: We found that providing explanations, whether static or context-adapted, significantly improves user task performance and comprehension of system behavior compared to offering no explanations. • Context-adaptivity is not always superior: We revealed that context-adapted explanations are not universally better than static ones, with their benefits depending on factors such as task complexity and user preferences. The increasing complexity of interactive smart environments presents a significant engineering challenge: their automated, context-aware decisions are often opaque, undermining system usability. While explainability is becoming known as a promising remedy, systematically evaluating different explanation strategies for these systems remains an open problem. This paper presents a rigorous empirical evaluation to address this gap. We conducted a controlled experiment (N = 159) to compare three approaches: no explanation, static explanations, and context-adapted explanations. Our results quantify the significant benefits of explainability on task performance and user understanding of system behavior. Furthermore, we identify a key engineering tradeoff: context-adapted explanations are not universally superior to simpler static implementations, suggesting that a one-size-fits-all approach is suboptimal in this domain. To support our evaluation, we developed a web-based testbed simulating a smart home environment with light gamification elements, enabling reliable human-grounded assessment. Based on our findings, we offer concrete insights and recommendations to guide the design of explainable interactive systems. Our study underscores the importance of tailoring explanations to user needs and contextual factors, contributing to more transparent and user-friendly smart environments.

In this review, we analyze the current state of the art of computational models for in-vehicle User Interface (UI) design. Driver distraction, often caused by drivers performing Non Driving Related Tasks (NDRTs), is a major contributor to vehicle crashes. Accordingly, in-vehicle User Interfaces (UIs) must be evaluated for their distraction potential. Computational models are a promising solution to automate this evaluation, but are not yet widely used, limiting their real-world impact. We systematically review the existing literature on computational models for NDRTs to analyze why current approaches have not yet found their way into practice. We found that while many models are intended for UI evaluation, they focus on small and isolated phenomena that are disconnected from the needs of automotive UI designers. In addition, very few approaches make predictions detailed enough to inform current design processes. Our analysis of the state of the art, the identified research gaps, and the formulated research potentials can guide researchers and practitioners toward computational models that improve the automotive User Interface (UI) design process.

Explainability is crucial for complex systems like pervasive smart environments, as they collect and analyze data from various sensors, follow multiple rules, and control different devices resulting in behavior that is not trivial and, thus, should be explained to the users. The current approaches, however, offer flat, static, and algorithm-focused explanations. User-centric explanations, on the other hand, consider the recipient and context, providing personalized and context-aware explanations. To address this gap, we propose an approach to incorporate user-centric explanations into smart environments. We introduce a conceptual model and a reference architecture for characterizing and generating such explanations. Our work is the first technical solution for generating context-aware and granular explanations in smart environments. Our architecture implementation demonstrates the feasibility of our approach through various scenarios.

To increase trust in systems, engineers strive to create explanations that are as accurate as possible. However, if the system’s accuracy is compromised, providing explanations for its incorrect behavior may inadvertently lead to misleading explanations. This concern is particularly pertinent when the correctness of the system is difficult for users to judge. In an online survey experiment with 162 participants, we analyze the impact of misleading explanations on users’ perceived and demonstrated trust in a system that performs a hardly assessable task in an unreliable manner. Participants who used a system that provided potentially misleading explanations rated their trust significantly higher than participants who saw the system’s prediction alone. They also aligned their initial prediction with the system’s prediction significantly more often. Our findings underscore the importance of exercising caution when generating explanations, especially in tasks that are inherently difficult to evaluate. The paper and supplementary materials are available at https://doi.org/10.17605/osf.io/azu72

Abstract Interoperability stands as a critical hurdle in developing and overseeing distributed and collaborative systems. Thus, it becomes imperative to gain a deep comprehension of the primary obstacles hindering interoperability and the essential criteria that systems must satisfy to achieve it. In light of this objective, in the initial phase of this research, we conducted a survey questionnaire involving stakeholders and practitioners engaged in distributed and collaborative systems. This effort resulted in the identification of eight essential interoperability requirements, along with their corresponding challenges. Then, the second part of our study encompassed a critical review of the literature to assess the effectiveness of prevailing conceptual approaches and associated technologies in addressing the identified requirements. This analysis led to the identification of a set of components that promise to deliver the desired interoperability by addressing the requirements identified earlier. These elements subsequently form the foundation for the third part of our study, a reference architecture for interoperability-fostering frameworks that is proposed in this paper. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them, but also by identifying the key elements of a framework facilitating interoperability in Systems of Systems.

The ability to perform automated conversions between data conforming to different specifications is a key ingredient to achieve interoperability among heterogeneous systemswhich, in turn, is at the basis of the creation of so-called Systems of Systems.These conversions require the definition of mappings between concepts of separate data specifications, which is typically a hard and time-consuming task.In this paper, we present a technique to automatically suggest mappings to users, based on both linguistic and structural similarities between terms.The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests carried out using specifications from the transportation domain.