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The concept of digital twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber–physical systems. Despite the various foundations, standards, and platforms in systems engineering, there are ongoing challenges with verification and validation methodology. This study aims to establish a generic framework that addresses the various aspects of digital twinning. The multifaceted nature of the problem requires raising the abstraction level in both the real (actual) and virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the quintuple helix model with the problem and operational domains of a real (actual) twin, the solution and implementation domains of a virtual twin, and the execution domain as the bridge that links them. Verification and validation dimensions follow the meta object facility abstraction layers (instance, model, meta-model, and meta-meta-model) mapping over five helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios. The application of main conceptual framework mechanisms in a real-world example study aids the verification of this research's intentions. The validation is a matter of further research endeavors. [ABSTRACT FROM AUTHOR]

Data-intensive applications aim at discovering valuable knowledge from large amounts of data coming from real-world sources. Typically, workflow languages are used to specify these applications, and their associated engines enable the execution of the specifications. However, as these applications become commonplace, new challenges arise. Existing workflow languages are normally platform-specific, which severely hinders their interoperability with other languages and execution engines. This also limits their reusability outside the platforms for which they were originally defined. Following the Design Science Research methodology, the paper presents SWEL (Scientific Workflow Execution Language). SWEL is a domain-specific modeling language for the specification of data-intensive workflows that follow the model-driven engineering principles, covering the high-level definition of tasks, information sources, platform requirements, and mappings to the target technologies. SWEL is platform-independent, enables collaboration among data scientists across multiple domains and facilitates interoperability. The evaluation results show that SWEL is suitable enough to represent the concepts and mechanisms of commonly used data-intensive workflows. Moreover, SWEL facilitates the development of related technologies such as editors, tools for exchanging knowledge assets between workflow management systems, and tools for collaborative workflow development. [ABSTRACT FROM AUTHOR]

Rapid technological advancements have resulted in increasingly complex software systems, posing challenges during development in terms of time and cost. Adopting domain-specific modeling (DSM) brings numerous benefits to software engineering, including enhanced efficiency, improved maintenance capabilities, higher software quality, reduced development time, and increased potential for cost-effective software solutions through improved reusability. Despite the proven effectiveness of DSM in various domains, a study summarizing recent advancements is hard to find in the state-of-the-art. Therefore, in this article, we present a comprehensive systematic literature review that examines the application of DSM in various domains (4). We selected 99 studies and classified those into four categories, i.e., meta-modeling (42), domain-specific languages (39), UML profiles (9), and general (9) based on the use of DSM approaches. We identified various tools from the selected studies, i.e., 21 existing and 91 proposed or developed. Moreover, model-driven engineering (MDE) techniques, including validation (12), simulation (5), verification (4), and software architectural modeling (3), are presented and analyzed. We further explained the type of model transformation employed in each study, i.e., model-to-text (49) and model-to-model (4). Finally, the regions participating in DSM’s growth are also investigated. It is concluded that Ecore is the leading meta-modeling tool, Xtext is the often-used domain-specific tool, Sirius is graphical, and UPPAAL is the most utilized verification tool identified. Moreover, Validation is a frequently used MDE technique, and Model-to-text transformation with Acceleo is the most utilized transformation type in the selected studies. The comprehensive results of this research provide valuable guidance for DSM researchers and practitioners in choosing a suitable tool and technique that meets their specific requirements.

The emergence of large language models (LLMs) has marked a significant milestone in the evolution of natural language processing. With the expanded use of LLMs in multiple fields, the development of domain-specific pre-trained language models (PLMs) has become a natural progression and requirement. Developing domain-specific PLMs requires careful design, considering not only differences in training methods but also various factors such as the type of training data and hyperparameters. This paper proposes MediBioDeBERTa, a specialized language model (LM) for biomedical applications. First, we present several practical analyses and methods for improving the performance of LMs in specialized domains. As the initial step, we developed SciDeBERTa v2, an LM specialized in the scientific domain. In the SciERC dataset evaluation, SciDeBERTa v2 achieves the state-of-the-art model performance in the named entity recognition (NER) task. We then provide an in-depth analysis of the datasets and training methods used in the biomedical field. Based on these analyses, MediBioDeBERTa, was continually trained on SciDeBERTa v2 to specialize in the biomedical domain. Utilizing the biomedical language understanding and reasoning benchmark (BLURB), we analyzed factors that degrade task performance and proposed additional improvement methods based on intermediate fine-tuning. The results demonstrate improved performance in three categories: named entity recognition (NER), semantic similarity (SS), and question-answering (QnA), as well as in the ChemProt relation extraction (RE) task on BLURB, compared with existing state-of-the-art LMs.

The article proposes an approach to simplify a process of identifying significant concepts for a given domain. The author describes his ONTO6 methodology based on a semiinformal meta-ontology. The stages of applying the ONTO6 methodology are: the development of a meta-ontology instance appropriate for the domain to be informatized; the development of an initial ontology from the meta-ontology instance; and the gradual detailing of domain concepts that appear in the initial ontology - the development of an enriched initial ontology. The transition of ONTO6 methodology to ONTO6 framework by usage of tools - NLP-PIPE, Python custom programs, Protégé plugin Cellfie - is demonstrated. [ABSTRACT FROM AUTHOR]

Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. The main focus is on the domain of the Internet of Things (IoT), where both ML and model-driven SE play a key role. In the context of the need to take a Cyber-Physical System-of-Systems perspective of the targeted architecture, an integrated design environment for both SE and ML sub-systems would best support the optimization and overall efficiency of the implementation of the resulting system. In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation. It transpires that the proposed approach is not only feasible, but may also contribute to the performance leap of software development for smart Cyber-Physical Systems (CPS) which are connected to the IoT, as well as an enhanced user experience of the practitioners who use the proposed modeling solution. [ABSTRACT FROM AUTHOR]

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains' features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach. [ABSTRACT FROM AUTHOR]

Model-driven software development comes in different styles. While standard-based approaches leverage existing language standards (e.g., UML), tooling, and development processes, domain-specific modeling (DSM) requires languages and tool support to be created prior to the actual software development. The design, implementation, and testing of languages and tool support require a wide spectrum of methods and techniques which introduce complexity and new challenges. To tackle these DSM-specific challenges, best practices have been collected from various application domains and published in literature to guide the development and application of DSM solutions. It is the goal of this paper to identify studies reporting best practices on domain-specific modeling. Moreover, a systematic and comprehensive compilation of best practices should act as a starting point to identify literature that facilitates industrial adoption of DSM. To search for literature and classify identified studies, we conduct a systematic mapping study (SMS). Furthermore, we perform an in-depth analysis of the identified studies to answer how practices overlap, complement, or contradict each other. The systematic search resulted in 21 studies reporting 321 best practices. From these 321 practices, we compiled 192 unique best practices. We found that the DSM community created a substantial corpus of best practices for DSM. The large majority of practices (75%) are only reported once. The top best practices by number of reports contain only practices that are reported at least four times. However, the frequency of reports does not necessarily imply a high importance of practice, as the application of practices is always context specific. [ABSTRACT FROM AUTHOR]

Conducting technology-oriented experiments (i.e., experiments in which treatments are applied to objects by a computer-based tool) without proper tool support is often a time-consuming and highly error-prone task. Although many techniques have been proposed to help conducting controlled experiments, none of them simultaneously addresses (1) the executable specification of experiments at a high level of abstraction; (2) automated treatment execution and automated data analysis from the experiment specification; and (3) formal guaranties of the correctness of results according to an experiment specification for technology-oriented experiments. To address these issues, we provide a Domain-Specific Modeling approach to create a Web-based tool (ExpRunA) comprising a Domain-Specific Language named ToExpDSL , execution and analysis script generators, a supporting framework, and a running infrastructure. An experimenter uses ToExpDSL to specify an experiment using experimentation concepts. From this specification, applications corresponding to the underlying treatments are executed, execution results are collected and analyzed, and, finally, the analysis results are presented to the experimenter. We establish the consistency of such results with respect to the experiment specification by formalizing and proving key correctness properties of ExpRunA. We empirically evaluated ExpRunA with respect to automation by replicating three already published experiments; we evaluated the level of abstraction by a qualitative assessment. Our empirical evaluation shows that ToExpDSL is expressive enough to specify three technology-oriented experiments and that ExpRunA can be used to enable sound automation of execution and analysis from the specification of technology-oriented experiments at a high level of abstraction. [ABSTRACT FROM AUTHOR]

In software development, domain-specific languages (DSLs) are often applied for specific or repetitive tasks. For executable DSLs, a model interpreter can be developed to run DSL programs. Nevertheless, it is more widespread to generate code in a general-purpose programming language. A properly chosen DSL expresses the original problem more naturally for both domain and information technology experts, and thus, this approach makes the whole development process, especially requirements engineering and requirements analysis, more efficient and less prone to human errors. There are code generator frameworks and so-called language workbenches available that make the development of code generators for DSLs easier. In this paper, we report on our own code generator framework, called Meta3. Meta3 is based on our code generator development experience. We believe that this experience report will be useful for developers of code generators and language workbenches interested in building more flexible and robust code generators as well as better tools that support the construction of the latter. [ABSTRACT FROM AUTHOR]

This paper presents an approach to handling frequent variations of modeling languages and models. The approach is based on Domain-Specific Modeling and linking of modeling tools with adaptive Run-Time Systems. The applicability of our solution is illustrated on an example of domain-specific languages for robot control. Special attention was given to the following problems: 1) model-level debugging; 2) performing fast transformation of models to native code for various hardware platforms and operating systems; and 3) specification of views and view-based generation of applications for validation of meta-models, models, and generated code. The feedback for automated refinement of models and metamodels is provided by a custom adaptive Run-Time System. For the purpose of synchronizing models, meta-models, and the target Run-Time System, we introduce action reports, which allow model-level debugging. In order to simplify handling of frequent model variations, we have introduced the linguistic concept of a modifier. [ABSTRACT FROM AUTHOR]

Model-driven software engineering methodologies like model-driven engineering aim to improve the productivity of software development by using graph-based models as the main artifacts during development, and generating the source code from these models. The models are usually displayed and edited using a graphical notation. However, they can also be described using a textual notation. This has some advantages and disadvantages compared to the graphical approach. For example, while editing the model, we can better focus on the details instead of a broad overview. Similarly to source code, models evolve rapidly during development. Handling and managing the evolution of models is an important task in model-driven methodologies and is an active research area today. However, there exist few research on text-based modeling approaches, compared to graph-based ones. This paper introduces the text-based modeling research field based on existing literature, and presents the state-of-the-art of the field related to model evolution and management. Our goal is to identify challenges and directions for future research in this field. The main topics covered are model differencing and merging, and the synchronization of the textual and graphical notations. [ABSTRACT FROM AUTHOR]

Epidemic transmission is a common type of public health emergency that is difficult to forecast and often causes substantial harm. Artificial societal models provide a novel approach to the study of public health problems. However, public health emergency management (PHEM) always involves multi-disciplinary and multi-hierarchical models that complicate the work of modeling. Models are also made more complex by the consideration of new requirements and interactions. Therefore, we propose a domain-specific methodology to guide the modeling process in PHEM. By analyzing domain characteristics and modeling requirements, a meta-modeling framework can be constructed, containing the basic elements with which to construct an artificial society to study epidemic transmission. In this paper, the designs of meta-models are discussed in detail, and domain models are implemented by code generation, which enables the support of large-scale, agent-based computational experiments on the KD-ACP platform. Case studies of Ebola are outlined, emergency scenarios are reconstructed based on pre-designed meta-models, and “scenario-response” experiments are presented. This study provides a valuable framework and methodology with which to study complex social problems in PHEM. The proposed method has been verified effectively and efficiently. [ABSTRACT FROM AUTHOR]

Combat system effectiveness simulation (CoSES) plays an irreplaceable role in the effectiveness measurement of combat systems. According to decades of research and practice, composable modeling and multi-domain modeling are recognized as two major modeling requirements in CoSES. Current effectiveness simulation researches attempt to cope with the structural and behavioral complexity of CoSES based on a unified technological space, and they are limited to their existing modeling paradigms and fail to meet these two requirements. In this work, we propose a model framework-based domain-specific composable modeling method to solve this problem. This method builds a common model framework using application invariant knowledge for CoSES, and designs domain-specific modeling infrastructures for subdomains as corresponding extension points of the framework to support the modeling of application variant knowledge. Therefore, this method supports domain-specific modeling in multiple subdomains and the composition of subsystem models across different subdomains based on the model framework. The case study shows that this method raises the modeling abstraction level, supports generative modeling, and promotes model reuse and composability. [ABSTRACT FROM AUTHOR]

The proliferation of information technologies and the diversity of problem domains that heavily rely on software tool applications promote computer-supported cooperative work as a challenging discipline that drives the development process of contemporary and future engineering methods, standards, and tools. Consequently, a particular domain of expertise in engineering and scientific fields has emerged, demanding more advanced skills and deeper domain knowledge. The essential role of Architectural Design (AD) and Urban Planning (UP) is to enable a forward-looking approach to building/facility creation. Construction Engineering (CE) expresses its routine primarily through a transition phase that transforms ideas to sustainable urban artifacts. The CE role appears as a combination of backward and downward looking to the same process/product. All three domains are highly cooperative in the context of Environmental Engineering (EE). Currently available software tools that support the AD, UP and CE domains are far from simple. Several recent software engineering studies suggest that, instead of developing a complex “all in one solution”, the federation or orchestration of several related simple methods and tools seems promising. In this article, we discuss the basic AD, UP and CE domain-cooperation aspects and suggest an Extensible Orchestration Framework (ExOF) model that may support them. To verify the ExOF simulation and orchestration potential, we used its architectural model to orchestrate different software tools when performing the urban blocks daylight illumination simulation for different urban block morphology models. For the exact simulation, we used the normalized sun-exposition data for the city of Novi Sad, Serbia. Also presented is an illustration of the methodology, modeling and orchestration potential of ExOF for the selected case study, together with the results, obtained for typical 3D models of selected urban block morphology patterns. [ABSTRACT FROM AUTHOR]

One key driver of the Linked Data paradigm is the ability to lift data graphs from legacy systems by employing various adapters and RDFizers (e.g., D2RQ for relational databases, XLWrap for spreadsheets). Such approaches aim towards removing boundaries of enterprise data silos by opening them to cross-organizational linking within a 'Web of Data'. An insufficiently tapped source of machine-readable semantics is the underlying graph nature of diagrammatic conceptual models - a kind of information that is richer compared to what is typically lifted from table schemata, especially when a domain-specific modeling language is employed. The paper advocates an approach to Linked Data enrichment based on a diagrammatic model RDFizer originally developed in the context of the ComVantage FP7 research project. A minimal but illustrative example is provided from which arguments will be generalized, leading to a proposed vision of 'conceptual model'-aware information systems. [ABSTRACT FROM AUTHOR]