Setting up and configuring model-driven engineering (MDE) tools is not straightforward because the MDE tooling landscape is highly fragmented and because many MDE tools are research prototypes with limited documentation. This creates significant accidental complexity for learners of MDE, who have to overcome installation and configuration hurdles before they can even begin to focus on the core MDE concepts they should be learning. This is further complicated by the complexity of modern MDE tools, which can overwhelm new learners, making it difficult for them to work out what they should do next to achieve a given goal. To address these challenges, we have developed a web-based playground platform that enables learners to engage with MDE learning activities without the need to install anything. The playground metaphor allows teachers to expose only those functionalities directly required for the completion of a particular learning activity. We present the general architecture of the platform, our approach to the declarative integration of new MDE tools, and the way in which teachers can flexibly and declaratively define new MDE learning activities. We have used our platform in a range of different contexts, from live tutorials and 10-week university courses, to developing documentation webpages for MDE tools. We describe examples of such uses, showcasing the flexible configurability of the platform for different types of activities and contexts.

Understanding and maintaining software systems often requires extracting high-level abstractions, such as domain models, from source code. MDRE-LLM addresses this challenge by integrating Large Language Models (LLMs) with traditional Model-Driven Reverse Engineering (MDRE) techniques, offering an innovative approach to automate and enhance domain model recovery. The tool supports flexible granularity strategies and validates LLM -generated models against deterministic baselines. MDRE-LLM addresses diverse use cases, including analyzing legacy systems with minimal documentation, rapidly compre-hending large-scale codebases, and validating LLM performance in reverse engineering tasks. These capabilities have the potential to improve software analysis and refactoring while advance AI-driven research and education by fostering systematic experimentation and collaboration. The tool and a webcast are available at https://zenodo.org/uploads/14072106.

Abstract Low-code software development promises rapid delivery of software cloud applications by employing domain-specific languages (DSLs), requiring minimal traditional coding. Model-driven engineering (MDE) provides tools, modelling notations and practices suited for engineering such DSLs, both from a syntactic and semantic perspective. However, low-code software development is heavily reliant on software reuse. It is imperative to provide safe mechanisms that guarantee valid semantic reuse of structural components and their behaviour, most often in a stepwise manner. This article presents a semantic reuse technique based on model subtyping over metamodels to manage correct model-driven engineering of DSLs. Model subtyping is generalized to structural semantics by considering OCL constraints. Moreover, model subtyping is generalized to behavioural semantics by considering specifications of model transformation operations, which may encode operational or translational semantics. Model subtyping facilitates structural and behavioural refinement. It has been implemented atop a bounded model checker, realizing a semi-decidable procedure for verifying that DSL elements are safely reused. The algorithm finds semantic witnesses of inconsistencies when refinement principles are not satisfied, fostering a correct stepwise engineering of DSLs. Moreover, the algorithm produces an extension metamodel that permits the as-is reuse of implementations of model transformation operation specifications. Finally, the versatility of the model subtyping technique is illustrated with common use cases extracted from the research literature.

Abstract With the increasing presence of cyber-physical systems (CPSs), like autonomous vehicle systems and digital twins, the future of software engineering is predicated on the importance of designing and developing data-centric software systems that can adapt intelligently at run time. CPSs consist of complex heterogeneous software components. Model-driven engineering advocates using software models to tame such complexity, capturing the relevant design concerns of such systems at different levels of abstraction. Yet most of the existing CPSs are engineered without considering MDE practices and tools, facing fundamental challenges when working with data: monitoring the program data at run time, syncing updates between program and model, dealing with heterogeneous data sources, and representing such observed data at run time to facilitate automated analysis. In this work, we introduce the notion of view models to explicitly represent parts of the domain knowledge implicitly embedded in the source code of a software system. This notion is equipped with a scalable bidirectional syncing mechanism that extracts view model instances from program snapshots at run time. The syncing mechanism is proposed from a conceptual point of view, independently of specific implementations and supports incremental view model update and view model maintenance. We show how this syncing mechanism is flexible enough to facilitate the non-intrusive adoption of MDE technology over existing MDE-agnostic heterogeneous data-centric systems. We study the run-time cost implied by the EMF-Syncer , the tool implementing this syncing mechanism for Java applications and view models atop the eclipse modeling framework (EMF) when executing data analytic and transformation tasks over large volumes of data in the presence of data updates at run time. An empirical evaluation of the EMF-Syncer has been conducted with an industry-targeted benchmark for decision support systems, analyzing performance and scalability. The novel syncing mechanism enables new opportunities to adopt MDE technology in heterogeneous data-centric systems.

Abstract To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.

Abstract When model transformations are used to implement consistency relations between very large models, incrementality plays a cornerstone role in detecting and resolving inconsistencies efficiently when models are updated. Given a directed consistency relation between two models, the problem studied in this work consists in propagating model changes from a source model to a target model in order to ensure consistency while minimizing computational costs. The mechanism that enforces such consistency is called consistency maintainer and, in this context, its scalability is a required non-functional requirement. State-of-the-art model transformation engines with support for incrementality normally rely on an observer pattern for linking model changes, also known as deltas, to the application of model transformation rules, in so-called dependencies, at run time. These model changes can then be propagated along an already executed model transformation. Only a few approaches to model transformation provide domain-specific languages for representing and storing model changes in order to enable their use in asynchronous, event-based execution environments. The principal contribution of this work is the design of a forward change propagation mechanism for incremental execution of model transformations, which decouples dependency tracking from change propagation using two innovations. First, the observer pattern-based model is replaced with dependency injection, decoupling domain models from consistency maintainers. Second, a standardized representation of model changes is reused, enabling interoperability with EMF-compliant tools, both for defining model changes and for processing them asynchronously. This procedure has been implemented in a model transformation engine, whose performance has been evaluated experimentally using the VIATRA CPS benchmark. In the experiments performed, the new transformation engine shows gains in the form of several orders of magnitude in the initial phase of the incremental execution of the benchmark model transformation and change propagation is performed in real time for those model sizes that are processable by other tools and, in addition, is able to process much larger models.

When model transformations are used to implement consistency relations between very large models (VLMs), incrementality plays a cornerstone role in the realization of practical consistency maintainers. State-of-the-art model transformation engines with support for incrementality normally rely on a publish-subscribe model for linking model updates − deltas − to the application of model transformation rules, in so called dependencies, at run time. These deltas can then be propagated along an already executed model transformation. A small number of such engines use domain-specific languages (DSLs) for representing model deltas offline in order to enable their use in asynchronous, event-based execution environments. The principal contribution of this work is the design of a forward delta propagation mechanism for incremental execution of model transformations, which decouples dependency tracking from delta propagation using two innovations. First, the publish-subscribe model is replaced with dependency injection, physically decoupling domain models from consistency maintainers. Second, a standardized representation of model deltas is reused, facilitating interoperability with EMF-compliant tools, both for defining deltas and for processing them asynchronously. This procedure has been implemented in a model transformation engine, whose performance has been evaluated empirically using the VIATRA CPS benchmark. In the experiments performed, the new transformation engine shows gains in the form of several orders of magnitude in the initial phase of the incremental execution of the benchmark model transformation and delta propagation is performed in real time, independently of the size of the models involved, whereas the up-to-now best-performant approach is dependent.

Domain models are the most important asset in widely accepted software development approaches, like Domain-Driven Design (DDD), yet those models are still implicitly represented in programs. Model-Driven Engineering (MDE) regards those models as representable entities that are amenable to automated analysis and processing, facilitating quality assurance while increasing productivity in software development processes. Although this connection is not new, very few approaches facilitate adoption of MDE tooling without compromising existing value, their data. Moreover, switching to MDE tooling usually involves re-engineering core parts of an application, hindering backward compatibility and, thereby, continuous integration, while requiring an up-front investment in training in specialized modeling frameworks. In those approaches that overcome the previous problem, there is no clear indication - from a quantitative point of view - of the extent to which adopting state-of-the-art MDE practices and tooling is feasible or advantageous. In this work, we advocate a code-first approach to modeling through an approach for applying MDE techniques and tools to existing object-oriented software applications that fully preserves the semantics of the original application, which need not be modified. Our approach consists both of a semi-automated method for specifying explicit view models out of existing object-oriented applications and of a conservative extension mechanism that enables the use of such view models at run time, where view model queries are resolved on demand and view model updates are propagated incrementally to the original application. This mechanism enables an iterative, flexible application of MDE tooling to software applications, where metamodels and models do not exist explicitly. An evaluation of this extension mechanism, implemented for Java applications and for view models atop the Eclipse Modeling Framework (EMF), has been conducted with an industry-targeted benchmark for decision support systems, analyzing performance and scalability of the synchronization mechanism. Backward propagation of large updates over very large views is instant.

Model transformation (MT) of very large models (VLMs), with millions of elements, is a challenging cornerstone for applying Model-Driven Engineering (MDE) technology in industry. Recent research efforts that tackle this problem have been directed at distributing MT on the Cloud, either directly, by managing clusters explicitly, or indirectly, via external NoSQL data stores. In this paper, we draw attention back to improving efficiency of model transformations that use EMF natively and that run on non-distributed environments, showing that substantial performance gains can still be reaped on that ground.

In model-driven engineering (MDE), models abstract the relevant features of software artefacts and model management operations, including model transformations, act on them automating large tasks of the development process. Flexible reuse of such operations is an important factor to improve productivity when developing and maintaining MDE solutions. In this work, we revisit the traditional notion of object subtyping based on subsumption, discarded by other approaches to model subtyping. We refine a type system for object-oriented programming, with multiple inheritance, to support model types in order to analyse its advantages and limitations with respect to reuse in MDE. Specifically, we extend type expressions with referential constraints and with OCL constraints. Our approach has been validated with a tool that extracts model types from (EMF) metamodels, paired with their OCL constraints, automatically and that exploits the extended subtyping relation to reuse model management operations. We show that structural model subtyping is expressive enough to support variants of model subtyping, including multiple, partial and dynamic model subtyping. The tool has received the ACM badge "Artifacts Evaluated - Functional".

Abstract In model-driven development, software artifacts are represented as models in order to improve productivity, quality, and cost effectiveness. In this area, the meta-object facility (MOF) standard plays a crucial role as a generic framework within which a wide range of modeling languages can be defined. The MOF standard aims at offering a good basis for model-driven development, providing some of the building concepts that are needed: what is a model, what is a metamodel, what is reflection in the MOF framework, and so on. However, most of these concepts are not yet fully formally defined in the current MOF standard. In this paper we define a reflective, algebraic, executable framework for precise metamodeling based on membership equational logic (mel) that supports the MOF standard. Our framework provides a formal semantics of the following notions:metamodel,model, andconformanceof a model to its metamodel. Furthermore, by using the Maude language, which directly supportsmelspecifications, this formal semantics isexecutable. This executable semantics has been integrated within the Eclipse modeling framework as a plugin tool called MOMENT2. In this way, formal analyses, such as semantic consistency checks, model checking of invariants and LTL model checking, become available within Eclipse to provide formal support for model-driven development processes.