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The collaborative nature of model-driven software engineering introduces significant challenges in safeguarding the confidentiality and integrity of the collaborative model. Existing access control mechanisms often rely on transient, virtual views lacking persistence and fine-grained permissions, making them unsuitable for scenarios requiring offline collaboration and leading to potential security breaches and user frustration. This work describes a dual-layered approach leveraging role-based access control policies to enhance security in collaborative modeling environments. The first layer utilizes multi-view modeling techniques to create materialized view models tailored to specific user roles, thereby restricting unnecessary access to the entire model. The second layer refines access at the individual element level within these view models, establishing fine-grained permissions enforced by model editors. This proactive enforcement prevents unauthorized actions before they occur, improving user experience and efficiency. The proposed approach, implemented as an Eclipse plugin and demonstrated through an illustrative example, ensures the confidentiality and integrity of shared model data by granting stakeholders access only to information relevant to their specific responsibilities and expertise. By filtering out irrelevant data, the approach also mitigates information overload, enabling stakeholders to concentrate on task-relevant aspects of the model, thereby potentially improving collaborative efficiency and effectiveness. 

Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamo del and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice.

Software-intensive systems are characterized by intricate interactions, distributed structures, and ongoing evolution posing notable challenges for traceability, change analysis, and decision support. Modeling and versioning practices are widely used to manage this complexity; however, in practice, they are often fragmented, limiting the value that organizations can derive from them. To address this challenge, we map modeling and versioning practices to the benefits they enable, providing a structured framework for practitioners to assess their current practices and identify opportunities for incremental improvement. Building on findings from our previous interview study with multiple companies, together with insights from the literature, we identify four levels of modeling formality, five levels of versioning sophistication, and four categories of benefits, summarized in two mapping tables. Our contributions include a practitioner-oriented roadmap that helps teams reflect on current modeling and versioning practices, anticipate achievable benefits, and identify incremental adjustments that can add value with minimal overhead. An initial validation with industry representatives confirms the practical relevance of the roadmap and highlights its potential to guide structured reflection and improvement of modeling and versioning practices. 

Automotive systems require rigorous methods to address their complexity. EAST-ADL has emerged as a promising architecture description language for specifying requirements, from the vehicle level down to software implementation. However, due to the inherent versatility of EAST-ADL, defining specific and fine-grained architectural features of automotive systems becomes challenging without addressing its intricacies. These challenges often result in several important aspects being overlooked. In this paper, we propose a two-step process consisting of: (1) separately specifying specific architectural features using domain-specific languages, and (2) injecting these features into an EAST-ADL core model through model transformation. Our conclusions demonstrate that domain-specific languages provide a succinct means of targeting specific modeling aspects while maintaining equivalence to the implementation in EAST-ADL.

Collaborative technologies are continuously evolving to address existing problems and introduce innovative features for enhancing collaboration in the landscape of model-driven software engineering (MDSE). Different collaborative MDSE technologies (CMTs) provide different solutions to facilitate collaboration, making it hard for practitioners to choose the technology that best suits their needs. This study aims to investigate the landscape of CMTs and to provide a list of recommended technologies tailored to specific use case scenarios in the context of MDSE. We compiled a comprehensive list of CMTs using a systematic search complemented with snowballing, investigating both academic and grey literature. The technologies were selected through a set of inclusion and exclusion criteria and eventually analyzed through an in-depth analysis focusing on model management, collaboration, and communication. The findings of our study reveal that the current landscape of CMTs is characterized by a relatively narrow range of capabilities offered by different technologies. Consequently, practitioners often have to become proficient in combining several different technologies in order to meet their needs. While various CMTs offer distinct collaboration approaches, the current landscape could be richer in terms of capabilities. Our research provides a comprehensive description of recommended CMTs, enabling practitioners to make informed decisions and improve collaboration in their MDSE processes.

The explosion of smart, interconnected IoT devices is driving demand for higher performance, real-time processing, and self-* capabilities (self-adaptation, self-reconfiguration, self-healing) across heterogeneous platforms. CPUs, GPUs, and FPGAs now provide exceptional compute power but greatly complicate software toolchains. We envision AI-COMPILER, an LLM-driven, domain-specific framework that automates co-compilation and optimization of heterogeneous applications in smart cities, smart transportation, and real-time analytics. Tackling multi-objective goals-power efficiency, reliability, and dynamic reconfiguration-AI-COMPILER aims to transform how IoT and edge infrastructures are developed, deployed, and maintained. We examine critical issues such as generalization to new hardware, human-AI collaborative tuning, and semantic coherence across multiple DSLs, arguing that AI-COMPILER can usher in more agile, resilient IoT ecosystems through flexible, context-aware code generation and adaptation.

This paper introduces the Hazard and Threat Ontology, a hybrid ontology designed to illustrate safety hazards and security threats in complex systems of systems. Hazard Ontology and Combined Security Ontology are two ontologies with extensive terminology and complementary methodologies. They allow us to develop a hybrid approach that enables safety and security experts to analyze complex systems thoroughly. Combining these ontologies enhances the depth and scope of experts' analysis and decision-making process, and several tangible benefits are associated with using a hybrid approach across different industrial sectors. In this paper, an industrial use case illustrates the practical utility of the Hazard and Threat Ontology. Our approach facilitates the identification of hazards and threats, providing actionable insights into how to mitigate them. Consequently, assets and personnel can be protected, downtime can be reduced, and operational resilience can be enhanced.

Model-Driven Engineering (MDE) has been widely adopted across various industrial sectors due to its ability to manage the complexity of modern engineering products. However, traditional modeling languages and tools are often limited to a single, specific concrete syntax, which poses challenges for the diverse stakeholders involved in the modeling process.. To address these limitations, the emerging field of blended modeling introduces the use of multiple concrete syntaxes, and in some cases, even multiple abstract syntaxes, for representing the same information. In this expert perspective, we present generalized, technology-agnostic concepts developed within a European research and development project focused on blended modeling. Specifically, we contribute a standardized terminology and ontology for blended modeling, along with a methodology for creating blended modeling environments. These concepts were developed through collaboration between academic and industrial partners, who aligned on the motivations and benefits of this approach. The insights gained from this project are not only relevant to blended MDE but also can be applied to traditional MDE practices.

ROS2 is an increasingly popular middleware framework for developing robotic applications. A ROS2 application is composed of nodes that run concurrently, coordinate with each other through asynchronous interfaces, and can be deployed in a distributed manner. Rebeca is an actor-based language for modelling asynchronous and concurrent systems. Timed Rebeca adds timing features to deal with timing requirements of real-time systems. The similarities in concurrency and message-based asynchronous interactions of reactive nodes, and the ability of modelling timed behaviours justify using Timed Rebeca models to assist the development and verification of ROS2 applications. Model-based development and model-checking techniques allow faster prototyping and earlier detection of system errors before developing the entire real system. However, there are challenges in bridging the gap between continuous behaviours of robotic systems and discrete states in a model for automatic verification, and between complex robotic computations and inequivalent programming facilities in a modelling language like Timed Rebeca. We investigated the problem systematically and have succeeded in modelling a realistic multiple autonomous mobile robots (AMR) system using Timed Rebeca, creating corresponding ROS2 demo code, and showing the match between the model and the program. Based on experiments, we demonstrated the value of the model in development and automatic formal verification of correctness properties (target reachability, collision freedom, and deadlock freedom). Our model-checking results show that certain system problems are not always detected through simulation. The modelling principles, modelling and implementation techniques that are created and used in this work can be generalized for many other cases.