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.

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.

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.

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. However, challenging conditions at construction sites, such as mud splashes and vibrations, can degrade object detection performance by causing sensor occlusions and image blurriness. Traditional adversarial training methods, which enhance model robustness by using perturbed data, are limited in construction environments due to the scarcity of diverse real-world adversarial data and the dynamic nature of construction environments. To overcome these challenges, this paper explores utilizing few-shot learning (FSL) to improve the generalization performance and robustness of object detection models. FSL enables models to adapt quickly using minimal data, reducing the need for large datasets. In addition, we identify an often-overlooked issue: the hyperparameters used in FSL training are typically not optimized for this unique paradigm. To address this, we combine FSL with hyperparameter optimization to enhance model performance across multiple small-scale datasets. Experimental results demonstrate that our approach improves model performance on the ConstScene dataset over the default training paradigm. The code for this study is available at here.

The MODELS conference series is the premier venue for model-driven software and systems engineering covering all aspects of modeling, from languages and methods to tools and applications. MODELS 2023 took place in Västerås, Sweden, from 1 to 6 October, 2023, as the ACM/IEEE 26th International Conference on Model-Driven Engineering Languages and Systems. A total of 122 papers were submitted to the conference. The Foundations track of MODELS 2023 received 83 submissions of which 15 were accepted, while the Practice track received 39 submissions of which 15 were accepted. The combined acceptance rate was 24.6%.

Abstract In software engineering practice, models created for communication and documentation are often informal. This limits the applicability of powerful model-driven engineering mechanisms. Understanding the motivations and use of informal diagrams can improve modelling techniques and tools, by bringing together the benefits of both informal diagramming and modelling using modelling languages and modelling tools. In this paper, we report on an initial exploration effort to investigate the use of informal diagramming in both open-source software repositories and industrial software engineering practices. We carried out a repository mining study on open-source software repositories seeking informal diagrams and classified them according to what they represent and how they are used. Additionally, we describe industrial practices that rely to some extent on informal diagramming, as gathered through unstructured interviews with practitioners. We compare the findings from these data sources and discuss how informal diagrams are used in practice.

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.

In the system development of modern railroad vehicles, engineers frequently use a plethora of diverse notations to specify various systems, subsystems, and their associated concerns. The use of diverse notations introduces complex challenges linked with their management and integration. Conventional practices, which rely on manual revisions and translations, prove to be both time-intensive and cost-prohibitive. In addition, they carry substantial risks of human error, thereby potentially introducing faults into the system. Such practices are deemed inadequate for the railway industry, which is safety-critical in its nature and places paramount importance on the assurance of reliability and data integrity. To address these challenges, we developed a regular expression-based system facilitating the automatic translation of semi-structured texts into structured data, with a particular focus on ensuring data integrity and reliability. We have defined the system capitalizing on the insights and practical experience of our industrial partner, Alstom Rail Sweden AB, and validated it within their development process. The validation demonstrated the practicality of the system in a real-world context and highlighted valuable lessons learned throughout the process. Building on these insights, we applied model-driven engineering principles to generalize the system, providing an automated solution to the data extraction challenge from tender documents in the railway domain.

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.

Modeling is an essential and challenging activity in any engineering environment. It implies some hard-to-train skills such as abstraction and communication. Teachers, project leaders, and tool vendors have a hard time teaching or training their students, co-workers, or users. Gamification refers to the exploitation of gaming mechanisms for serious purposes, like promoting behavioral changes, soliciting participation and engagement in activities, etc. We investigate the introduction of gaming mechanisms in modeling tasks with the primary goal of supporting learning/training. The result has been the realization of a gamified modeling environment named PapyGame. In this article, we present the approach adopted for PapyGame implementation, the details on the gamification elements involved, and the derived conceptual architecture required for applying gamification in any modeling environment. Moreover, to demonstrate the benefits of using PapyGame for learning/training modeling, a set of user experience evaluations have been conducted. Correspondingly, we report the obtained results together with a set of future challenges we consider as critical to make gamified modeling a more effective education/training approach.

Multimodal journey planners are used worldwide to support travelers in planning and executing their journeys. Generated travel plans usually involve local mobility service providers, consider some travelers’ preferences, and provide travelers information about the routes’ current status and expected delays. However, those planners cannot fully consider the special situations of individual cities when providing travel planning services. Specifically, authorities of different cities might define customizable regulations or constraints of movements in the cities (e.g., due to construction works or pandemics). Moreover, with the transformation of traditional cities into smart cities, travel planners could leverage advanced monitoring features. Finally, most planners do not consider relevant information impacting travel plans, for instance, information that might be provided by travelers (e.g., a crowded square) or by mobility service providers (e.g., changing the timetable of a bus). To address the aforementioned shortcomings, in this paper, we propose ROUTE, a framework for customizable smart mobility planners that better serve the needs of travelers, local authorities, and mobility service providers in the dynamic ecosystem of smart cities. ROUTE is composed of an architecture, a process, and a prototype developed to validate the feasibility of the framework. Experiments’ results show that the framework scales well in both centralized and distributed deployment settings.

Abstract In 2014, a new software development approach started to get a foothold: low-code development. Already from its early days, practitioners in software engineering have been showing a rapidly growing interest in low-code development. In 2021 only, the revenue of low-code development technologies reached 13.8 billion USD. Moreover, the business success of low-code development has been sided by a growing interest from the software engineering research community. The model-driven engineering community has shown a particular interest in low-code development due to certain similarities between the two. In this article, we report on the planning, execution, and results of a multi-vocal systematic review on low-code development, with special focus to its relation to model-driven engineering. The review is intended to provide a structured and comprehensive snapshot of low-code development in its peak of inflated expectations technology adoption phase. From an initial set of potentially relevant 720 peer-reviewed publications and 199 grey literature sources, we selected 58 primary studies, which we analysed according to a meticulous data extraction, analysis, and synthesis process. Based on our results, we tend to frame low-code development as a set of methods and/or tools in the context of a broader methodology, often being identified as model-driven engineering.

Abstract This article presents the state of practice of consistency management in thirteen industrial model-based development settings. Our analysis shows a tight coupling between adopting shorter development cycles and increasingly pressing consistency management challenges. We find that practitioners desire to adopt shorter development cycles, but immature modeling practices slow them down. We describe the different patterns that emerge from the various industrial settings. There is an opportunity for researchers to provide practitioners with a migration path toward practices that enable more automated consistency management, and ultimately, continuous model-based development.

With the emergence of Cyber-Physical Systems (CPS), the increasing complexity in development and operation demands for an efficient engineering process. In the recent years DevOps promotes closer continuous integration of system development and its operational deployment perspectives. In this context, the use of Artificial Intelligence (AI) is beneficial to improve the system design and integration activities, however, it is still limited despite its high potential. AIDOaRT is a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AI-augmented automation supporting modelling, coding, testing, monitoring and continuous development of Cyber-Physical Systems (CPS). The project proposes to apply Model-Driven Engineering (MDE) principles and techniques to provide a framework offering proper AI-enhanced methods and related tooling for building trustable CPSs. The framework is intended to work within the DevOps practices combining software development and information technology (IT) operations. In this regard, the project points at enabling AI for IT operations (AIOps) to auto-mate decision making process and complete system development tasks. This paper presents an overview of the project with the aim to discuss context, objectives and the proposed approach.

In this paper, we report on a case study in an industrial setting where code is generated from models, and, for various reasons, that generated code is then manually modified. To enhance the maintainability of both models and code, consistency between them is imperative. A first step towards establishing that consistency is to identify the manual changes that were made to the code after it was generated and deployed. Identifying the delta is not straightforward and requires pre-processing of the artifacts. The main mechanics driving our solution are higher-order transformations, which make the implementation scalable and robust to small changes in the modeling language. We describe the specific industrial setting of the problem, as well as the experiences and lessons learned from developing, implementing, and validating our solution together with our industrial partner.

Abstract Software engineering is decision intensive. Evidence‐based software engineering is suggested for decision‐making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open‐source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision‐making for composite software. This paper presents a roadmap for research required to support evidence‐based decision‐making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5‐year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence‐based decision‐making and those related to supporting the decision‐making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence‐based decision‐making for composite software.

Co-evolution of metamodels and conforming models is a known challenge in model-driven engineering. A variation of co-evolution occurs in model-based software product line engineering, where it is needed to efficiently co-evolve various products together with the single common platform from which they are derived. In this paper, we aim to alleviate manual efforts during this co-evolution process in an industrial setting where Simulink models are partially reused across various products. We propose and implement an approach providing support for the co-evolution of reusable model fragments. A demonstration on a realistic example model shows that our approach yields a correct co-evolution result and is feasible in practice, although practical application challenges remain. Furthermore, we discuss insights from applying the approach within the studied industrial setting.

Abstract Software in modern vehicles consists of multi-criticality functions, where a function can be safety-critical with stringent real-time requirements, less critical from the vehicle operation perspective, but still with real-time requirements, or not critical at all. Next-generation autonomous vehicles will require higher computational power to run multi-criticality functions and such a power can only be provided by parallel computing platforms such as multi-core architectures. However, current model-based software development solutions and related modelling languages have not been designed to effectively deal with challenges specific of multi-core, such as core-interdependency and controlled allocation of software to hardware. In this paper, we report on the evolution of the Rubus Component Model for the modelling, analysis, and development of vehicular software systems with multi-criticality for deployment on multi-core platforms. Our goal is to provide a lightweight and technology-preserving transition from model-based software development for single-core to multi-core. This is achieved by evolving the Rubus Component Model to capture explicit concepts for multi-core and parallel hardware and for expressing variable criticality of software functions. The paper illustrates these contributions through an industrial application in the vehicular domain.

Model-based development and continuous integration each separately are methods to improve the productivity of development of complex modern software systems. We investigate industrial adoption of these two phenomena in combination, i.e., applying continuous integration practices in model-based development projects. Through semi-structured interviews, eleven engineers at three companies with different modelling practices share their views on perceived and experienced impediments to this adoption. We find some cases in which this introduction is undesired and expected to not be beneficial. For other cases, we find and categorize several impediments and discuss how they are dealt with in industrial practice. Model synchronization and tool interoperability are found the most challenging to overcome and the ways in which they are circumvented in practice are detrimental for introducing continuous integration.

Gamification is increasingly used to build solutions for driving the behaviour of target users' populations. Gameful systems are typically exploited to keep users' involvement in certain activities and/or to modify an initial behaviour through game-like elements, such as awarding points, submitting challenges and/or fostering competition and cooperation with other players. Gamification mechanisms are well-defined and composed of different ingredients that have to be correctly amalgamated together; among these we find single/multi-player challenges targeted to reach a certain goal and providing an adequate award for compensation. Since the current approaches are largely based on hand-coding/tuning, when the game grows in its complexity, keeping track of all the mechanisms and maintaining the implementation can become error-prone and tedious activities. In this paper, we describe a multi-level modelling approach for the definition of gamification mechanisms, from their design to their deployment and runtime adaptation. The approach is implemented by means of JetBrains MPS, a text-based meta-modelling framework, and validated using two gameful systems in the Education and Mobility domains.

Over the years, a number of approaches have been proposed on the description of systems and software in terms of multiple views represented by models. This modelling branch, so-called multi-view software and system modelling, praises a differentiated and complex scientific body of knowledge. With this study, we aimed at identifying, classifying, and evaluating existing solutions for multi-view modelling of software and systems. To this end, we conducted a systematic literature review of the existing state of the art related to the topic. More specifically, we selected and analysed 40 research studies among over 8600 entries. We defined a taxonomy for characterising solutions for multi-view modelling and applied it to the selected studies. Lastly, we analysed and discussed the data extracted from the studies. From the analysed data, we made several observations, among which: (i) there is no uniformity nor agreement in the terminology when it comes to multi-view artefact types, (ii) multi-view approaches have not been evaluated in industrial settings and (iii) there is a lack of support for semantic consistency management and the community does not appear to consider this as a priority. The study results provide an exhaustive overview of the state of the art for multi-view software and systems modelling useful for both researchers and practitioners.

Multimodal journey planners have been introduced with the goal to provide travellers with itineraries involving two or more means of transportation to go from one location to another within a city. Most of them take into account user preferences, their habits and are able to notify travellers with real time traffic information, delays, schedules update, etc.. To make urban mobility more sustainable, the journey planners of the future must include: (1) techniques to generate journey alternatives that take into account not only user preferences and needs but also specific city challenges and local mobility operators resources; (2) agile development approaches to make the update of the models and information used by the journey planners a self-adaptive task; (3) techniques for the continuous journeys monitoring able to understand when a current journey is no longer valid and to propose alternatives. In this paper we present the experiences matured during the development of a complete solution for mobility planning based on model-driven engineering techniques. Mobility challenges, resources and remarks are modelled by corresponding languages, which in turn support the automated derivation of a smart journey planner. By means of the introduced automation, it has been possible to reduce the complexity of encoding journey planning policies and to make journey planners more flexible and responsive with respect to adaptation needs.

The choice of which software component to use influences the success of a software system. Only a few empirical studies investigate how the choice of components is conducted in industrial practice. This is important to understand to tailor research solutions to the needs of the industry. Existing studies focus on the choice for off-the-shelf (OTS) components. It is, however, also important to understand the implications of the choice of alternative component sourcing options (CSOs), such as outsourcing versus the use of OTS. Previous research has shown that the choice has major implications on the development process as well as on the ability to evolve the system. The objective of this study is to explore how decision making took place in industry to choose among CSOs. Overall, 22 industrial cases have been studied through a case survey. The results show that the solutions specifically for CSO decisions are deterministic and based on optimization approaches. The non-deterministic solutions proposed for architectural group decision making appear to suit the CSO decision making in industry better. Interestingly, the final decision was perceived negatively in nine cases and positively in seven cases, while in the remaining cases it was perceived as neither positive nor negative.

This paper introduces a decision canvas for capturing architecture decisions in software and systems engineering. The canvas leverages a dedicated taxonomy, denoted GRADE, meant for establishing the basics of the vocabulary for assessing and choosing architectural assets in the development of software-intensive systems. The canvas serves as a template for practitioners to discuss and document architecture decisions, i.e., capture, understand and communicate decisions among decision-makers and to others. It also serves as a way to reflect on past decision-making activities devoted to both tentative and concluding decisions in the development of software-intensive systems. The canvas has been assessed by means of preliminary internal and external evaluations with four scenarios. The results are promising as the canvas fulfills its intended objectives while satisfying most of the needs of the subjects participating in the evaluation.

Models and model transformations, the two core constituents of Model-Driven Engineering, aid in software development by automating, thus taming, error-proneness of tedious engineering activities. In many cases, the result of these automated activities is an overwhelming amount of information. This is the case of one-to-many model transformations that, e.g. in model-based design-space exploration, can potentially generate a massive amount of candidate models (i.e., solution space) from one single source model. In our scenario, from one design model we generate a set of possible implementation models on which timing analysis is run. The aim is to find the best model from a timing perspective. However, multiple implementation models can have equally good analysis results. Therefore, the engineer is expected to investigate the solution space for making a final decision, using criteria which fall outside the analysis' criteria themselves. Since candidate models can be many and very similar to each other, manually finding differences and commonalities is an impractical and error-prone task. In order to provide the engineer with an expressive representation of models' commonalities and differences, we propose the use of modelling with uncertainty. We achieve this by elevating the solution space to a first-class status, adopting a compact notation capable of representing the solution space by means of a single model with uncertainty. Commonalities and differences are thus represented by means of uncertainty points for the engineer to easily grasp them and consistently make her decision without manually inspecting each model individually.

Modern software systems require advanced design support especially capable of mastering rising complexity, as well as of automating as many development tasks as possible. Model-Driven Engineering (MDE) is earning consideration as a solid response to those challenges on account of its support for abstraction and domain specialisation. However, MDE adoption often shatters industrial practice because its novelty opposes the need to preserve vast legacy and to not disband the skills matured in pre-MDE or alternative development solutions. This work presents the CHESS tool environment, a novel approach for cross-domain modelling of industrial complex systems. It leverages on UML profiling and separation of concerns realised through the specification of well-defined design views, each of which addresses a particular aspect of the problem. In this way, extra-functional, functional, and deployment descriptions of the system can be given in a focused manner, avoiding issues pertaining to distinct concerns to interfere with one another.

Satisfaction of Non-Functional Requirements (NFR), is a key factor in successful design of embedded systems. This is mainly due to the constraints and resource limitations in these systems. A design that cannot achieve functionality of the system under these limitations is actually a failure. Therefore, NFRs in design of embedded systems deserve special attention. However, one big issue is that NFRs are interconnected and cannot be considered in isolation; especially that they can have direct impacts on each other such as security and performance. This means that a careful balance and trade-off analysis among NFRs is necessary. In this paper, we focus on this need and identify what information about NFRs is required in order to perform trade-off analysis. We propose and explain our in-progress approach to incorporate this information into system models in order to enable trade-off analysis. Our approach is based on UML profiling method to annotate model elements with necessary information.

We analyze here the topic of integration, in the area of process automation, from sensor/actuator levels to plant management levels. The communication at fieldbus level is based on wireless technology while management applications run in wired control systems, but can also be distributed, communicating via the Internet. This work aims at building a real-life demonstrator at Boliden, a mining and smelting plant located in Boliden, Sweden. A small process control environment is to be deployed at the plant to supervise a tank level control system. Targeted results are an interface between wireless and wired systems, the deployment of a wireless process control environment at Boliden, and the development of the enterprise business management facilities.

RTSSim is a framework for simulating models extracted from complex legacy real-time systems which are task-oriented, run on a single processor and are developed in C. Such RTSSim models describe functional and temporal behavior as well as the resource usage of the system. However, the semantics specification of RTSSim models remains a challenging problem indeed, especially with tractable complexity to obtain a formal model which can be analyzed for instance by a model checking tool. In this paper, we present an approach towards using semantic anchoring for the transformational specification of RTSSim models, by relying on units with well-defined operational semantics and tool support. Specifically, Timed Automata with Tasks (TAT) in TIMES is chosen as the semantic unit with the purpose of anchoring different behavioral concerns of RTSSim models in all aspects. In this respect, model transformations are conducted at the meta-model level allowing the original operational semantics of RTSSim models to be preserved, while at the same time it can be presented in TIMES models in terms of a network of TAT.

Software development is witnessing the increasing need of version management techniques for supporting the evolution of model-based artefacts. In this respect, metamodels can be considered one of the basic concepts of model-driven engineering and are expected to evolve during their life-cycle. As a consequence, models conforming to changed metamodels have to be updated for preserving their well-formedness. This paper deals with the co-adaptation problems by proposing higher-order model transformations which take a difference model recording the metamodel evolution and produce a model transformation able to co-evolve the involved models.