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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. 

Model transformation is a core mechanism in Model-Driven Engineering (MDE), but its use requires expertise in metamodels, transformation languages, execution engines, runtime configuration, and diagnostics. Although LLMs can generate and revise transformation artifacts, direct prompt-to-output generation does not by itself provide an executable MDE workflow grounded in metamodel conformance and transformation rules execution.

This paper proposes agentic model transformation: a service-mediated workflow in which an LLM agent generates candidate transformation specifications and coordinates MDE services for metamodel access, example retrieval, checking, execution, diagnostics, and repair. Model Context Protocol is used to expose these services to the agent. When execution succeeds, target artifacts are produced through the configured transformation infrastructure, serialized by the target modeling environment, and loadable against the registered target metamodel.

Early results from adopting the proposed framework for the UML-to-Java and SysMLv2-to-AAS cases show that constructing and repairing executable transformations is possible. The results also show that executable success and metamodel conformance are only first checks; structural completeness, mapping coverage, and semantic adequacy require additional MDE services. The framework, therefore, shifts LLM support for model transformation from one-off artifact generation towards service-supported transformation development, where specifications, diagnostics, and execution traces can be inspected, reused, and extended.

Large language models are increasingly embedded in software development, yet most evaluations still treat them as one-shot generators for isolated tasks such as code completion or refactoring. In real workflows, however, artifacts such as application programming interfaces, data models, and database schemas co-evolve, and changes must propagate across representations without breaking consistency. When propagation fails, developers incur extra validation, retries, and manual repair, which increases latency and infrastructure cost and undermines sustainable operation. In this study, we ask whether large language models can preserve cross-artifact consistency under change in a round-trip workflow. We apply a controlled edit to one artifact, translate it to its coupled counterpart, and translate it back, then check whether the intended edit persists without drift (i.e., unintended semantic changes or syntactic invalidity). We instantiate this question by synchronizing class-oriented data models with relational database schemas. Using a curated dataset of paired models and schemas and a suite of controlled edit operations, we evaluate four large language model, GPT-5, Qwen3-Next-80B-A3B, DeepSeek V3, and Gemini 2.5, under a unified, reproducible protocol that measures (i) edit persistence, (ii) structural validity (parsability/loadability), and (iii) run-to-run consistency over repeated executions. Our results show that the models handle small, routine edits reliably, but they struggle when edits require structural reorganization. Gemini 2.5 is the most consistent across runs; DeepSeek V3 often preserves the intended semantics but occasionally produces unparsable outputs; Qwen3-Next-80B-A3B exhibits high variance; and GPT-5 often recognizes the change but fails to propagate it coherently through the coupled representation. We contribute a reproducible benchmark and evaluation framework for assessing LLM reliability under artifact co-evolution, together with empirical evidence of current limitations. Overall, the findings reveal a gap between detecting a change and propagating it coherently, underscoring the need for structural validation and human oversight to achieve dependable and cost-efficient LLM-assisted software evolution.

One of the most transformative developments today is the integration of generative artificial intelligence into the development of critical software-intensive cyber-physical systems. From autonomous vehicles to industrial robotics, these systems are entering a new era shaped by artificial intelligence-driven development and automation. In this paper, we consider software engineering, artificial intelligence, artificial intelligence ethics, and social aspects, to explore how such technologies can be harnessed safely, transparently, and with human values at the center. Our contributions include a vision for software engineering, guiding the engineering of future trustworthy safety-critical cyber-physical systems under the influence of generative artificial intelligence. We critically analyze how three established certification principles can be leveraged to cope with the societal and technical tensions introduced by generative artificial intelligence adoption, and propose a research and practice agenda to ensure that future cyber-physical systems development and operations cycle remain trustworthy, both from a system (hardware and software) and from a societal perspective. 

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. 

Informal diagrams created in general-purpose diagramming tools are widely used in software architecture because they are quick to produce and easy to share. However, the lack of constraints in such tools often yields inconsistent notations and ad-hoc conventions, which in turn invite misinterpretation when diagrams are read outside their original context. Dedicated modeling languages and environments can mitigate these issues but are frequently resisted due to steep learning curves and disruptive adoption costs. Building on the flexible modeling paradigm, this paper proposes an approach to the lightweight formalization of informal diagrams. Grounded in observed industrial challenges and prior work on flexible modeling, we derive a set of design principles and instantiate them in an approach realized as a Draw.io plugin. We propose an approach that addresses challenges from industrial settings by enabling practitioners to introduce structure incrementally into the informal diagrams they already create, thereby helping resolve notational inconsistency and clarify meaning. Moreover, we show how our approach satisfies the guiding flexible modeling principles by introducing these model-like benefits without compromising the accessibility and speed of informal diagramming as valued in practice. The contribution enhances clarity and consistency within familiar workflows and lays the groundwork for subsequent capabilities such as rapid dissemination of conventions, enterprise-level aggregation, and additional quality checks should organizations choose to adopt them. © 2026, Science and Technology Publications, Lda. All rights reserved.

In this article, we discuss how we can facilitate the growing need for early validation and verification (V&V) of system behaviour in Model-Based Systems Engineering (MBSyE). Several aspects, such as reducing cost and time to market, push companies towards integration of V&V methods earlier in development to support effective decision-making. One foundational methodology seeing increased attention in industry is the use of MBSyE, which brings benefits of models with well-defined syntax and semantics to support V&V activities, rather than relying on natural language text documentation. Despite their promise, industrial adoption of these practices is still challenging. This article presents a vision for readily available early V&V. We present a summary of the literature on early V&V in MBSyE and position existing challenges regarding potential solutions and future investigations towards this vision. We elaborate our vision by means of challenges with a specific emphasis on early V&V of system behaviour. We identify three specific challenge areas: Creating and managing Models, Organisational systems engineering aspects, and early V&V Methods. Finally, we outline a road-map to address these categories of challenges, in which we propose the transfer of established best practices from the software engineering domain to support emerging technologies in the systems engineering domain. 

The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions. This paper introduces a new semantic segmentation dataset specifically tailored for construction sites, taking into account the diverse challenges posed by adverse weather and environmental conditions. The dataset is designed to enhance the training and evaluation of object detection models, fostering their adaptability and reliability in real-world construction applications. Our dataset comprises annotated images captured under a wide range of different weather conditions, including but not limited to sunny days, rainy periods, foggy atmospheres, and low-light situations. Additionally, environmental factors such as the existence of dirt/mud on the camera lens are integrated into the dataset through actual captures and synthetic generation to simulate the complex conditions prevalent in construction sites. We also generate synthetic images of the annotations including precise semantic segmentation masks for various objects commonly found in construction environments, such as wheel loader machines, personnel, cars, and structural elements. To demonstrate the dataset’s utility, we evaluate state-of-the-art object detection algorithms on our proposed benchmark. The results highlight the dataset’s success in adversarial training models across diverse conditions, showcasing its efficacy compared to existing datasets that lack such environmental variability.

Models and metamodels change, entailing efforts to keep related artifacts consistent, i.e., to reflect the implications of the changes on them. In order to assess these implications, the changes or evolution steps themselves are, in most cases, of highest interest, compared to the states of the models or metamodels. While the states can be used to derive the changes, some information on the actual changes might get lost, e.g., whether an empty class has been renamed or deleted and re-added. The use of deltas to describe changes is not limited to models and metamodels, but is also employed in other research areas. To get an overview of the used concepts and how they compare, we did a scoping literature review in the field of computer science, focused on modeling and related fields. We compared the different approaches in regard to how they model the change, their ability to model atomic or composite changes, their completeness in modeling all possible changes, as well as their purpose. This overview allows for more efficient concept re-use across domains in regard to the modeling of changes and the different use cases realized with them.url: https://doi.ieeecomputersociety.org/10.1109/MODELS-C68889.2025.00065