Explainability is a critical enabler of trust, usability, and regulatory compliance in artificial intelligence (AI) systems. In Hybrid Intelligence (HI) systems, where human expertise and AI collaborate to make decisions, the need for explainability becomes even more pronounced. However, the complexity of explainability requirements varies significantly depending on the role AI plays within the system. From human-dominant systems, where AI provides supportive insights, to AI-dominant systems, where AI assumes autonomous decision-making, the nature and depth of explanations required shift across this spectrum. Despite its importance, there is a lack of systematic approaches for tailoring explainability to meet stakeholder needs across these varying levels of AI complexity. This paper identifies the key components that shape explainability in HI systems, addressing the distinct roles and interactions of human and AI agents, as well as the diverse needs of stakeholders. In addition, the paper introduces a conceptual framework to assist designers in integrating explain-ability as a Non-Functional Requirement into HI systems. The framework leverages Goal-Oriented Requirements Engineering to facilitate the incorporation of suitable explainability NFRs into the design of HI systems, enabling designers to address challenges such as enhancing transparency, fostering trust, and meeting diverse stakeholder expectations. This research provides a first approach for advancing the design of adaptable, accountable, and trustworthy HI systems, contributing valuable insights to the fields of requirements engineering, human-AI collaboration, and regulatory compliance.

Abstract Today, a considerable number of Architectural Languages (ALs) have been proposed for specifying and analyzing the architecture of software systems. Despite the popularity of different ALs, how ALs influence software system maintainability has not received much attention. One of the most important tasks in software maintenance is requirements traceability. Requirements traceability establishes links between requirements and other software artifacts, facilitating system maintenance. In this paper, we analyze the influence of ALs on requirements traceability. Taking into account the ALs used by the industry, we analyze how ALs influence traceability among requirements and architecture models. We conducted an evaluation with our industrial partner CAF. The results show significant differences in AL performance. We also analyze the results in terms of AL concepts, requirements model elements, and AL type in order to understand the performance differences. General‐Purpose/Research Languages achieve the best results for all of the performance indicators, providing a mean precision value of 0.51, a recall value of 0.38, a combined F‐measure of 0.40, and an Matthews Correlation Coefficient value of 0.33. Those ALs that influence engineers to use more generic and domain‐independent terms to specify their architectures obtain the best results during requirements traceability. Our results have the potential to help AL designers to improve their languages and also to help practitioners make a more informed decision about whether or not a given AL meets their traceability needs.

Abstract Autonomous capabilities are required in AmI environments in order to adapt systems to new environmental conditions and situations. However, keeping the human in the loop and in control of such systems is still necessary because of the diversity of systems, domains, environments, context situations, and social and legal constraints, which makes full autonomy a utopia within the short or medium term. Human-system integration introduces an important number of challenges and problems that have to be solved. On the one hand, humans should interact with systems even in those situations where their attentional, cognitive, and physical resources are limited in order to perform the interaction. On the other hand, systems must avoid overwhelming the user with unnecessary actions. Therefore, appropriate user-centered methods for AmI development should be used to help designers analyze and design human-in-the-loop interactions in AmI environments. This paper presents a user-centered design method that defines a process with a set of tools and techniques that supports the process steps in order to systematically design, prototype, and validate human-in-the-loop (HiL) solutions. The process starts with the definition of the HiL design, which defines how the system cooperates with the human. This HiL design is built using a conceptual framework that focuses on achieving human-system interactions that get human attention and avoid obtrusiveness. Then, we provide a software infrastructure to generate a prototype based on the HiL design and validate it by having end-users use a web simulator. The feedback data generated during the prototype user validation is gathered and used by a machine learning tool that infers the user’s needs and preferences. Finally, these inferences are used to automatically enhance the human-in-the-loop designs and prototypes. We have validated the proposed method through a twofold perspective: an experiment to analyze the perception of interaction designers regarding their acceptance of the design method and another experiment to evaluate the usefulness of the “smart” prototyping technique. The results obtained point out the acceptability of the proposed method by designers and the useful adaptations provided by the “smart” prototyping technique to achieve a HiL design that adapts well to users’ preferences and needs.

Model Fragment Location (MFL) aims at identifying model elements that are relevant to a requirement, feature, or bug. Many MFL approaches have been introduced in the last few years to address the identification of the model elements that correspond to a specific functionality. However, there is a lack of detail when the measurements about the search space (models) and the measurements about the solution to be found (model fragment) are reported. Generally, the only reported measure is the model size. In this paper, we propose using five measurements (size, volume, density, multiplicity, and dispersion) to report the location problems. These measurements are the result of analyzing 1,308 MFLs in a family of industrial models over the last four years. Using two MFL approaches, we emphasize the importance of these measurements in order to compare results. Our work not only proposes improving the reporting of the location problem, but it also provides real measurements of location problems that are useful to other researchers in the design of synthetic location problems.

Web service compositions run in complex computing infrastructures where arising events may affect the quality of the system. However, crucial Web service compositions cannot be stopped to apply changes to deal with problematic events. Therefore, the trend is moving towards context-aware Web service compositions, which use context information as a basis for autonomic changes. Under the closed-world assumption, the context and possible adaptations are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts. In this paper, we leverage models at runtime to guide the dynamic evolution of context-aware Web service compositions to deal with unexpected events in the open world. In order to manage uncertainty, a model that abstracts the Web service composition, self-evolves to preserve requirements. The evolved model guides changes in the underlying WS-BPEL composition schema. A prototype and an evaluation demonstrate the feasibility of our approach.

[EN] One of the most important goals of pervasive systems is to help users in their daily life by automating their behaviour patterns. To achieve this, pervasive services must be dynamically coordinated, executed and adapted to context according to user behaviour patterns. In this work, we propose a model-driven solution to meet this challenge. We propose a task model and a context ontology to design context-adaptive coordination of services at a high level of abstraction. This design facilitates the coordination analysis at design time and is also reused at runtime. We propose a software architecture that interprets the models at runtime in order to coordinate the service execution that is required to support user behaviour patterns. This coordination is done in a context-adaptive way and decoupled from service implementation. This approach makes the models the only representation of service coordination, which facilitates the maintenance and evolution of the executed service coordination after deployment.

Modeling Software Kit (MOSKitt) is a FREE CASE tool, built on Eclipse, which is being developed by the Conselleria de Infraestructuras, Territorio y Medio Ambiente. Its main goal is to provide the necessary tools for supporting a complete Model Driven Software Development (MDSD) Process. To achieve such support MOSKitt has been endowed with a set of graphical model editors (such as BPMN, UML, User Interface Design or Data Base Design) and Model Transformations (M2M and M2T) that turn it not just as a Modeling Tool but also as a powerful environment to automate the full development process. In addition, MOSKitt's plugin architecture allows using MOSKitt as a real CAME environment. This feature enables specifying and implementing different software development processes based on the specific requirements of software company life cycles and processes.

Web services run in complex environments where it is desirable to be able to count on mechanisms to adapt them according to contextual changes. Manual adaptations are unfeasible in intricate service compositions that are built with highly dynamic Web services. However, the principles of Autonomic Computing could support their self-adaptation. In this paper, we propose a method for designing and implementing context-aware autonomous Web services in system families. It is based on Software Product Line (SPL) engineering to support their systematic reuse in mass production environments. Autonomic behavior is achieved by our Model-based Reconfiguration Engine for Web services (MoRE-WS). MoRE-WS leverages variability models at runtime as easy-to-understand and semantically rich adaptation policies for decision-making while the architecture of a Dynamic SPL (DSPL) allows a flexible service recomposition. A case study illustrates the applicability of our method.

Model-driven development has become more and more important in the last few years. In the context of web application development, many web Engineering methods that propose model-driven development processes have appeared. However, earlier stages of these processes are seldom considered and few of these methods rigorously face the problems of specifying web application requirements and translating them into the proper conceptual model. However, it is widely recognized that requirements engineering activities are essential to obtain quality software products. This article surveys Model-driven web engineering methods in a comparative study and analyzes the techniques proposed for specifying functional, data and navigational requirements as well as the mechanisms provided for automatically translating these requirements into conceptual models. Our main goal is to provide a critical view of the support that is provided by these methods for handling web application requirements in order to show their current limitations and strengths.

Mobile devices play a key role in reducing the gap between the physical and the digital world. The use of automatic identification capabilities can improve the user participation in business processes. However, the solutions available today for connecting physical elements with digital services are

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. This paper introduces an approach for the design of pervasive SPLs that is based on model driven development (MDD) and Variability Modeling principles. Variability models are interpreted at run-time to reconfigure pervasive systems according to fluctuations in the environment. This approach helps to improve Pervasive SPLs to produce software that adapts itself in an autonomic way. We have developed an adaptive pervasive system for smart homes to validate this approach.

This paper introduces a method for specifying the functional requirements of pervasive systems. These requirements specifications can be mapped into PervML models, which enables (1) the early generation of prototypes to validate the requirements and (2) the definition of well defined transformations that provide traceability mechanisms for going from requirements to implementation and vice-versa