Model-driven Engineering Research Articles

FUNDAMENTANDO MEGAMODELOS: UMA REVISÃO SISTEMÁTICA

One approach that has gained strength in recent years in software engineering is the ModelDriven Engineering (MDE). The MDE is the most conceptual approach when working with models, and the MDA as putting in place the MDE. The MDE modeling paradigms can be used in simple designs with the use of models, but when working with relations between the models is the so-called megamodel. In other words, megamodel is a template that contains models and relationships between them. The objective of this study is to describe the results of a systematic review, whose objective was to know the state of the art megamodel and verify their use and the contexts in which they are applied. The results show that all selected works use megamodel in different contexts, but there is no consensus about its definition or nomenclature.

Reengineering Web applications into pervasive information systems

In today’s mobile-driven world, organizations face the challenge of adapting legacy Web applications to pervasive information systems, such as mobile applications, ensuring seamless accessibility across devices. This paper presents a systematic, three-phase approach to reengineering Web applications into mobile applications: Reverse-engineering, Transformation, and Forward-engineering. In the reverse-engineering phase, high-level conceptual models of the Web application are generated to capture its architecture and functionality. During the transformation phase, these models are refined using Unified Modeling Language (UML) profiles to align with the requirements of mobile systems. The forward-engineering phase utilizes the Acceleo technology, along with predefined mapping rules and code patterns, to automate the generation of mobile application code. A case study is conducted to demonstrate the effectiveness of this framework, revealing a significant reduction in manual effort throughout the reengineering process. This approach offers a scalable and efficient solution for transforming Web applications into mobile applications, contributing to advancements in model-driven engineering and software reengineering.

Preface to the special issue on model-driven engineering and system analysis and modelling

Preface to the special issue on model-driven engineering and system analysis and modelling

The rCOS framework for multi-dimensional separation of concerns in model-driven engineering

The software industry increasingly turns to Model-Driven Engineering (MDE) to mitigate complexity by automating model creation and transformation. Many organisations are pursuing Integrated Development Platforms (IDPs) to enhance automation in their software development processes within MDE. However, the adoption of MDE and engagement with IDPs remain limited due to concerns over their efficacy. We address these challenges in this review paper by introducing a framework for the formal refinement of component and object systems (rCOS). It provides: (1) a formal theory that consists of a modelling language (named OPL) with a calculus of refinement for object-oriented models and component models; (2) a suite of analysis and design techniques that facilitate abstractions and decompositions, leading to a multidimensional separation of concerns; and (3) an IDP (named rCOS Modeller) that supports modelling, design and verification from requirements elicitation through to coding. By advocating for an rCOS-enabled multidimensional approach to separating concerns, this paper offers a comprehensive solution to the challenges facing MDE and IDPs, paving the way for their successful implementation in practice. By delineating the emerging challenges and prospects associated with integrating formal methods for modelling and designing human-cyber–physical systems (HCPS), we show the potential of extending rCOS for MDE in HCPS.

FORMALIZATION AND ANALYSIS OF UML 2.0 INTERACTION OVERVIEW DIAGRAM USING MAUDE REWRITING LOGIC LANGUAGE

The visual modeling language UML embodies object-oriented design principles.It provides a standard way to visualize the design of a system. It exploits a richset of well-defined graphical notations for creating abstract models. However,the power of UML is lessened through partially specified formal semantics. Indeed, UML notations are semi-formal and do not lead to fully formalized andexecutable semantics. Fortunately, UML diagrams are prone to early formalization. Formal methods are a valuable tool that can help overcome the UMLconstructs’ shortage of firm semantics. It is a powerful way to ascribe precise semantics to the graphical notations used in UML diagrams and models.Our work aims to support the semantics of the UML Interaction Overview Diagram. It introduces an approach to leveraging the strengths of the MaudeRewriting Logic language as a formal specification language. The proposal relies on a model-driven engineering approach. It aims to automate the UMLInteraction Overview Diagram’s mapping to a Maude language specification.The Maude language and its linked tools, including the Maude Model Checker,are used to analyze and verify the resulting Maude specification. Finally, anapplication example shows the feasibility and benefits of the proposed approach.

Automation of Computations in Designing an Integrated Energy System Based on Its Digital Twin

The construction of integrated energy systems (IESs) based on traditional energy systems operating separately provides higher efficiency and reliability of energy supply to consumers. However, IESs are complex structures to design. A digital twin is a tool that allows you to combine all the tools necessary for design in a single information space. Software tools that implement the digital twin of IESs require high computational flexibility, which is due to the need to simulate a variety of equipment and involve a wide range of methods and mathematical models. Automating the construction process of a computing subsystem is a highly efficient solution for overcoming the challenges mentioned above. This paper proposes a methodological approach to automating the construction of the computing subsystem of the digital twin of an IES. The proposed approach involves using modern metaprogramming tools on a software platform to perform automated construction. During construction, the Model-Driven Engineering concept is implemented and knowledge formalized in the form of ontologies is used. The digital twin, obtained as a result of the practical application of the proposed methodological approach, enables computer and mathematical modeling of an IES in virtual space, with exploration of various configurations of its construction.

Model-driven engineering for digital twins: a graph model-based patient simulation application.

Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. A short case study is presented to demonstrate the viability of the proposed simulation architecture. The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.

An architecture for model-based and intelligent automation in DevOps

The increasing complexity of modern systems poses numerous challenges at all stages of system development and operation. Continuous software and system engineering processes, e.g., DevOps, are increasingly adopted and spread across organizations. In parallel, many leading companies have begun to apply artificial intelligence (AI) principles and techniques, including Machine Learning (ML), to improve their products. However, there is no holistic approach that can support and enhance the growing challenges of DevOps. In this paper, we propose a software architecture that provides the foundations of a model-based framework for the development of AI-augmented solutions incorporating methods and tools for continuous software and system engineering and validation. The key characteristic of the proposed architecture is that it allows leveraging the advantages of both AI/ML and Model Driven Engineering (MDE) approaches and techniques in a DevOps context. This architecture has been designed, developed and applied in the context of the European large collaborative project named AIDOaRt. In this paper, we also report on the practical evaluation of this architecture. This evaluation is based on a significant set of technical solutions implemented and applied in the context of different real industrial case studies coming from the AIDOaRt project. Moreover, we analyze the collected results and discuss them according to both architectural and technical challenges we intend to tackle with the proposed architecture.

Systematizing modeler experience (MX) in model-driven engineering success stories

Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of “modeler experience” (MX). MX includes factors such as user experience, motivation, integration, collaboration and versioning, and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling.

Human factors in model-driven engineering: future research goals and initiatives for MDE

Human factors in model-driven engineering: future research goals and initiatives for MDE

On the Usability of a Modeling Language for IoT-Based Public Transportation Systems

Internet of Things (IoT)-based public transportation systems face distinct challenges within the broader realm of IoT. Developers of such systems encounter a notably intricate development environment compared to general IoT systems, which are inherently characterized by elevated levels of complexity and heterogeneity. As successfully applied in other domains, domain-specific modeling languages (DSMLs) can also be employed to facilitate the development of IoT-based public transportation systems and address the challenges mentioned. Hence, in this study, a novel model-driven engineering (MDE) methodology is presented, comprising the steps of using a DSML, called DSML4PT, for the development of a wide-range of IoT-based public transportation applications. Moreover, the usability evaluation of DSML4PT within this MDE methodology during the real applications of IoT-based public transportation systems is also provided, which is missing in similar studies. For this purpose, we investigated the usability of DSML4PT within a systematic evaluation approach in which the features of DSML4PT are assessed both quantitatively and qualitatively in eight different real public transportation applications with the participation of experienced developers. Comparative analysis revealed that approximately 80% of IoT-based public transportation systems could be automatically generated through modeling exclusively employing DSML4PT. In contrast to the conventional software development methodologies, the novel DSML4PT approach also decreased the time required for the development of public transportation applications by almost 50%. In addition, according to a questionnaire-based assessment, the general evaluation rating of the language was measured as 4.44 over 5-point Likert scale. Feedback from the developers corroborated the practicality of this language and its widespread adoption across diverse perspectives.

Leveraging deep reinforcement learning for design space exploration with multi-fidelity surrogate model

Design automation is undergoing a new generation of changes caused by artificial intelligence technologies represented by deep learning and reinforcement learning. Notably, the advantages of deep reinforcement learning in addressing solution optimisation and decision-making tasks with cognitive automation functionality have garnered attention in design. In the context of surrogate model-driven engineering design optimisation, this paper addresses current research challenges such as reliance on domain knowledge for local development, shortcomings in the self-learning and adaptive capabilities of optimisation algorithms for global exploration, etc. Centred around the deep reinforcement learning model, Deep Q-learning, and complemented by self-organising maps and neural network technologies, we propose a methodology considering multi-fidelity simulation data for design space exploration. This approach effectively reduces sampling costs and enables the optimisation model to learn the optimal direction for high-precision predictions and achieve rapid, accurate optimisation. Finally, the effectiveness of the proposed method is comprehensively validated through four typical optimisation scenarios and a case study involving the optimisation of a wheeled robot's suspension swing arm structure. This work will be a crucial reference for applying deep reinforcement learning in simulation-driven engineering design optimisation.

Towards an effective approach for composition of model transformations

Model Driven Engineering (MDE) adoption in the industry suffers from many technical and non-technical problems. One of the significant technical problems lies in the difficulty of building complex transformations from the composition of small and reusable transformations. Another problem resides in developing transformations from scratch in case they are missing. In this paper, we present an approach to how to handle these issues. The approach allows composing reusable transformations to build more complex ones by providing a catalog of prebuilt transformations targeting common architectures, frameworks, and design patterns. To give guidance and simplify the task of developing new transformations, we describe a platform description model of an entire system or a part of it in two views: a UML profile and a set of transformations. We also present three transformation types, each of which handles different abstraction design concerns. Generic transformations are small and reusable to build complex transformations, system-independent transformations are reusable and implement high-level design decisions, and system-specific transformations are not reusable and implement all design decisions needed for a given system. The approach is implemented as a plugin for a UML modeling tool and validated by developing a system that simulates the behavior of a gas station through model transformations built from the composition of reusable transformations.

Agile model-driven re-engineering

AbstractIn this paper we describe an Agile model-driven engineering (MDE) approach, AMDRE, for the re-engineering of legacy systems. The objective is to support the reuse of business-critical functionality from such systems and the porting of legacy code to modernised platforms, together with technical debt reduction to improve the system maintainability and extend its useful life. AMDRE uses a lightweight MDE process which involves the automated abstraction of software systems to UML specifications and the interactive application of refactoring and rearchitecting transformations to remove quality flaws and architectural flaws. We demonstrate the approach on Visual Basic, COBOL and Python legacy codes, including a finance industry case. Significant quality improvements are achieved, and translation accuracy over 80% is demonstrated. In comparison to other MDE re-engineering approaches, AMDRE does not require high MDE skills and should be usable by mainstream software practitioners.

Engineering recommender systems for modelling languages: concept, tool and evaluation

Recommender systems (RSs) are ubiquitous in all sorts of online applications, in areas like shopping, media broadcasting, travel and tourism, among many others. They are also common to help in software engineering tasks, including software modelling, where we are recently witnessing proposals to enrich modelling languages and environments with RSs. Modelling recommenders assist users in building models by suggesting items based on previous solutions to similar problems in the same domain. However, building a RS for a modelling language requires considerable effort and specialised knowledge. To alleviate this problem, we propose an automated, model-driven approach to create RSs for modelling languages. The approach provides a domain-specific language called Droid to configure every aspect of the RS: the type of the recommended modelling elements, the gathering and preprocessing of training data, the recommendation method, and the metrics used to evaluate the created RS. The RS so configured can be deployed as a service, and we offer out-of-the-box integration with Eclipse modelling editors. Moreover, the language is extensible with new data sources and recommendation methods. To assess the usefulness of our proposal, we report on two evaluations. The first one is an offline experiment measuring the precision, completeness and diversity of recommendations generated by several methods. The second is a user study – with 40 participants – to assess the perceived quality of the recommendations. The study also contributes with a novel evaluation methodology and metrics for RSs in model-driven engineering.

ModelXGlue: a benchmarking framework for ML tools in MDE

AbstractThe integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue . This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE.

MOBICAT: a model-driven engineering approach for automatic GUI code generation for Android applications

MOBICAT: a model-driven engineering approach for automatic GUI code generation for Android applications

Modular language product lines: concept, tool and analysis

AbstractModelling languages are intensively used in paradigms like model-driven engineering to automate all tasks of the development process. These languages may have variants, in which case the need arises to deal with language families rather than with individual languages. However, specifying the syntax and semantics of each language variant separately in an enumerative way is costly, hinders reuse across variants, and may yield inconsistent semantics between variants. Hence, we propose a novel, modular and compositional approach to describing product lines of modelling languages. It enables the incremental definition of language families by means of modules comprising meta-model fragments, graph transformation rules, and rule extensions. Language variants are configured by selecting the desired modules, which entails the composition of a language meta-model and a set of rules defining its semantics. This paper describes: a theory for checking well-formedness, instantiability, and consistent semantics of all languages within the family; an implementation as an Eclipse plugin; and an evaluation reporting drastic specification size and analysis time reduction in comparison to an enumerative approach.

The impact of personality and self-efficacy on domain modeling productivity in graphical and textual notations

ContextSoftware development is a complex and human-intensive activity, where human factors can have a significant impact on productivity and quality of results. To address the complexity of software, domain modeling has gained much importance, mainly due to software methodologies such as Model-Driven Engineering and Domain-Driven Design. In particular, domain modeling is an essential task that allows developers to understand and effectively represent the problem domain. However, domain modeling productivity can be affected by several human factors, including developers' personality and self-efficacy. ObjectiveThe study aims to explore the influence of human factors, specifically developers' personality and self-efficacy, on domain modeling productivity in graphical and textual notations. MethodAn empirical controlled study was conducted with 134 third-year computer science students from the University of Alicante, guided by the definition of a theoretical model based on previous studies. The participants were tasked with creating domain models in both graphical and textual notations. The order in which the notations were used was randomized, and the participants were given different system specifications to model. After modeling, 98 participants completed questionnaires assessing their personality, self-efficacy, and notation satisfaction. The design and evaluation of the experiment employed the Goal, Question, and Metrics framework. Data analysis was performed using a stepwise selection method to select the most appropriate regression model. ResultsThe study indicates that personality and self-efficacy have a significant impact on the performance of junior domain model developers. Specifically, it was discovered that while neuroticism had a negative impact on efficiency in both notations, developers' ability belief and use of graphical notation had a positive influence on effectiveness and efficiency in creating domain models. ConclusionsThese findings highlight the importance of considering human factors and notation choice in software development. Developers' personality and self-efficacy emerge as critical considerations for enhancing both productivity and quality in domain modeling.

A maintainable and iterative development approach of critical systems with FoCaLiZe

The life-cycle development of critical systems follows mainly a V-cycle process overlapped with proofs and/or verification techniques. At the first development stage, a critical system must be, precisely, and as completely as possible described. Thus, it is recommended to use a graphical modeling language standard such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL), to which both customers and developers could contribute. Then, the graphical design with its formal specification (requirements) is mapped into a complete formal development environment, using an MDE (Model-driven engineering) approach. The formal environment has to assess the development until the generation of a secure code. In this context, we propose a life-cycle development approach that combines UML/OCL and the FoCaLiZe formal environment for the secure development of critical systems. The proposed approach ensures essential development constraints such as strict boundaries and traceability between development phases. It also ensures a secure maintenance phase using partial transformation techniques from UML/OCL to FoCaLiZe. The latter enhances the transformation from UML/OCL into FoCaLiZe to support incremental development and assist developers in correcting modeling errors. The proposed approach is mostly dedicated to secure and maintainable lifecycle development processes of critical systems. The formal environment assesses the development process until the generation of a secure code. To illustrate the proposed approach, we present the stages of development of a constrained system controlling military objects. The development of this system uses the iterative, incremental, and maintainability stages as described by the development approach, until the generation of secure code.