Model-driven Engineering Approach Research Articles

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

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.

NAVIDRO, a CARES architectural style for configuring drone co-simulation

One primary objective of drone simulation is to evaluate diverse drone configurations and contexts aligned with specific user objectives. The initial challenge for simulator designers involves managing the heterogeneity of drone components, encompassing both software and hardware systems, as well as the drone’s behavior. To facilitate the integration of these diverse models, the Functional Mock-Up Interface (FMI) for co-simulation proposes a generic data-oriented interface. However, an additional challenge lies in simplifying the configuration of co-simulation, necessitating an approach to guide the modeling of parametric features and operational conditions such as failures or environment changes. The article addresses this challenge by introducing CARES, a model-driven engineering and component-based approach for designing drone simulators, integrating the FMI for co-simulation. The proposed models incorporate concepts from component-based software engineering and FMI. The NAVIDRO architectural style is presented for designing and configuring drone co-simulation. CARES utilizes a code generator to produce structural glue code (Java or C++), facilitating the integration of FMI-based domain-specific code. The approach is evaluated through the development of a simulator for navigation functions in an autonomous underwater vehicle, demonstrating its effectiveness in assessing various autonomous underwater vehicle configurations and contexts.

SmAuto: A domain-specific-language for application development in smart environments

A common problem in the development of Internet-of-Things (IoT) and Cyber-Physical System (CPS) applications is the complexity of these domains, due to their hybrid and distributed nature in multiple layers (hardware, network, communication, application etc.). Apart from other issues, this inherent complexity often gives room for implementation errors, which can be in many cases fatal and drive the application and/or the system to undesired states. The current work aspires to alleviate this problem by introducing a low-code approach for building IoT and CPS applications. We argue that, through the proposed approach it is possible to lower development time and risk (errors/bug-related ones) and allow a wide range of end-users to build and monitor applications for state-of-the-art domains, such as smart home and smart industry. In this context, Model-Driven Engineering (MDE) approaches are explored and SmAuto, a Domain-specific Language (DSL) is proposed for creating and executing automation tasks for smart environments. Through SmAuto it is possible to handle the heterogeneity and complexity issues of the IoT and CPS domains, this way allowing end-users are non-technical application experts to build well-designed and properly functioning smart applications. The proposed DSL implements a Sense-Think-Act-Communicate model for smart environments and enables the creation, validation, and dynamic execution of composite automation models in physical, virtual and hybrid environments, while it also enables automated code generation of virtual entities for verification purposes. By using layered abstractions to automate the development process, end-users can concentrate on the real problem instead of dwelling into technical details, thus increasing their productivity. The results of the empirical evaluation and the comparison to existing approaches show that SmAuto can make application development more rigorous, improves productivity of end-users including non-experts, i.e. citizen developers and satisfies several functional and non-functional requirements of modern DSLs, such as tool support, modular deployment, reusability, availability and extensibility.

FloBP: a model-driven approach for developing and executing IoT-enhanced business processes

AbstractThe capability to integrate Internet of Things (IoT) technologies into business processes (BPs) has emerged as a transformative paradigm, offering unprecedented opportunities for organisations to enhance their operational efficiency and productivity. Interacting with the physical world and leveraging real-world data to make more informed business decisions is of greatest interest, and the idea of IoT-enhanced BPs promises to automate and improve business activities and permit them to adapt to the physical environment of execution. Nonetheless, combining these two domains is challenging, and it requires new modelling methods that do not increase notation complexity and provide independent execution between the process and the underlying device technology. In this work, we propose FloBP, a model-driven engineering approach separating concerns between the IoT and BPs, providing a structured and systematic approach to modelling and executing IoT-enhanced BPs. Applying the separation of concerns through an interdisciplinary team is needed to ensure that the approach covers all necessary process aspects, including technological and modelling ones. The FloBP approach is based on modelling tools and a microservices architecture to deploy BPMN models, and it facilitates integration with the physical world, providing flexibility to support multiple IoT device technologies and their evolution. A smart canteen scenario describes and evaluates the approach’s feasibility and its possible adoption by various stakeholders. The performed evaluation concludes that the application of FloBP facilitates the modelling and development of IoT-enhanced BPs by sharing and reusing knowledge among IoT and BP experts.

AIoTML: A Unified Modeling Language for AIoT-Based Cyber–Physical Systems

Due to deeply intertwined physical and hardware/software components together with increasing number of interconnected heterogeneous devices powered by Artificial Intelligence (AI) techniques, the design complexity of Cyber-Physical Systems (CPSs) becomes skyrocketing. Model-Driven Engineering (MDE) methods have been proven to be effective in increasing the productivity of CPS design. However, there is still a lack of MDE approaches that enable design space exploration as well as the code generation for the design of AI IoT (AIoT)-based CPSs. To mitigate the situation, this paper presents a unified modeling language named AIoTML for AIoT-based CPSs, which enables the construction of AI-based components across different modeling levels for the purposes of intelligent sensing and control. By extending the constructs of state-of-the-art Domain-Specific Language (DSL) ThingML, AIoTML can seamlessly unify the modeling of both autonomous executions of AIoT devices and their surrounding physical environment, which facilitates both platform-independent simulation and control optimization for platform-specific CPSs. The compiler developed for AIoTML provides a family of code generators to support the construction of digital twins on various heterogeneous target AIoT platforms. Comprehensive evaluations on two complex real-world designs demonstrate the effectiveness of our AIoTML approach in fast development of AIoT-based CPSs with high control quality.

Applying a model-driven approach to the development of power plant SCADA/EMS software

In this paper, we propose and apply a model-driven approach to develop applications that compose a suite known as Energy Management System (EMS). EMS is an essential tool for the operation of electrical systems. Although models of cyber–physical phenomena are extensively used in planning, designing, and operating power systems, the development of EMS applications often uses such models only as support and documentation, while the main artifact throughout the software development process is source code. In other words, the models are used merely to document and communicate some aspects of a system. The evolution of such a system is expensive and error-prone, requiring specialized personnel, skilled in both software engineering and power systems. Some of the problems arising from this manual process include: (i) substantial implementation effort in interpreting models and manually translating them into programs; (ii) greater risk of introducing software defects; (iii) risk of leaving documentation outdated, or implementation inconsistent with the intended design. Model-Driven Engineering (MDE) is an approach to constructing computing systems in which models are used as primary engineering artifacts, while programs (and other products) are automatically generated from the models, thus addressing the above limitations. MDE approaches have been adopted in several industrial areas, including smart grid automation, in which specialized modeling languages and tools facilitate the production of executable software from high-level models. For SCADA/EMS software development, however, a dedicated approach is still absent. We address these issues by proposing D-SPADES: a Model-Driven Engineering approach tailored to the EMS domain. D-SPADES defines a domain-specific modeling language based on block diagram notation, called EMSML, specifically targeted to describe the behavior of EMS applications. It also defines the associated mapping strategies and harnesses a set of tools for automatically transforming models into source code of applications that can be integrated into existing SCADA platforms. We have applied D-SPADES to model and automatically generate source code for two different applications from the Itaipu power plant: a voltage and reactive power controller and a component for a system-wide special protection scheme. Both applications were validated using a power system simulator and performed satisfactorily. The special protection scheme was deployed to the production system. Hence we demonstrate that D-SPADES is a viable approach to developing mission-critical applications.

Modernizing the Legacy Healthcare System to Decentralize Platform Using Blockchain Technology

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient.

CoDEvo: Column family database evolution using model transformations

In recent years, software applications have been working with NoSQL databases as they have emerged to handle big data more efficiently than traditional databases. The data models of these databases are designed to satisfy the requirements of the software application, which means that the models must evolve when the requirements of the software application change. To avoid mistakes during the design and evolution of these NoSQL models, there are several methodologies that recommend using a conceptual model. This implies that consistency between the conceptual model and the schema must be maintained when either evolving the database or the software application. In this work, we propose CoDEvo, a model-driven engineering approach that uses model transformations to address the evolution of a NoSQL column family DBMS schema when the underlying conceptual model evolves due to software requirement changes, aiming to maintain consistency between the schema and conceptual model. We have addressed this problem by defining transformation rules that determine how to evolve the schema for a specific conceptual model change. To validate these transformations, we applied them to conceptual model changes from 9 open-source software applications, comparing the output schemas from CoDEvo with the schemas that were defined in these applications.

Automated engineering of domain-specific metamorphic testing environments

Testing is essential to improve the correctness of software systems. Metamorphic testing (MT) is an approach especially suited when the system under test lacks oracles, or they are expensive to compute. However, building an MT environment for a particular domain (e.g., cloud simulation, model transformation, machine learning) requires substantial effort. Our goal is to facilitate the construction of MT environments for specific domains. We propose a model-driven engineering approach to automate the construction of MT environments. Starting from a meta-model capturing the domain concepts, and a description of the domain execution environment, our approach produces an MT environment featuring comprehensive support for the MT process. This includes the definition of domain-specific metamorphic relations, their evaluation, detailed reporting of the testing results, and the automated search-based generation of follow-up test cases. Our method is supported by an extensible platform for Eclipse, called Gotten. We demonstrate its effectiveness by creating an MT environment for simulation-based testing of data centres and comparing with existing tools; its suitability to conduct MT processes by replicating previous experiments; and its generality by building another MT environment for video streaming APIs. Gotten is the first platform targeted at reducing the development effort of domain-specific MT environments. The environments created with Gotten facilitate the specification of metamorphic relations, their evaluation, and the generation of new test cases.

Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review.

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.

Code Generation by Example Using Symbolic Machine Learning

Code generation is a key technique for model-driven engineering (MDE) approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language. Specialised code generation languages and tools have been defined; however, the task of manually constructing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code generation language. In this paper, we apply novel symbolic machine learning techniques for learning tree-to-tree mappings of software syntax trees, to automate the development of code generators from source–target example pairs. We evaluate the approach on several code generation tasks, and compare the approach to other code generator construction approaches. The results show that the approach can effectively automate the synthesis of code generators from examples, with relatively small manual effort required compared to existing code generation construction approaches. We also identified that it can be adapted to learn software abstraction and translation algorithms. The paper demonstrates that a symbolic machine learning approach can be applied to assist in the development of code generators and other tools manipulating software syntax trees.

Model Transformation Testing and Debugging: A Survey

Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state-of-the-art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This article presents a survey on testing and debugging model transformations based on the analysis of 140 papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorize the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.

A Model-Driven Methodology to Accelerate Software Engineering in the Internet of Things

The Internet of Things (IoT) aims for connecting. This assumption brings about several software engineering challenges that constitute a serious obstacle to its wider adoption. The main feature of the IoT is genericity w.r.t the variability of software and hardware technologies. Model-driven engineering (MDE) is a paradigm that advocates using models to address software engineering problems. It can help to meet the genericity of the IoT from a software engineering perspective. Existing MDE approaches for the IoT focus only on modeling the internal behavior of things but lack a comprehensive approach dedicated to network modeling. In the present article, we introduce a network-oriented methodology based on MDE to unify the IoT’s heterogeneous concepts. Fundamentally, we avoid the intrinsic heterogeneity of the IoT by separating the network’s specification (i.e., the things, the communication scheme, and its constraints) from its concrete implementation (i.e., the low-level artifacts, such as source code and documentation). Technically, the methodology relies on a model-based domain-specific language (DSL) and a code generator. The former enables the modeling of the network’s specification, and the latter provides a procedure to generate the low-level artifacts from this specification. Our results show that this methodology makes iot’s software engineering more rigorous, helps prevent bugs earlier, and saves time.

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review.

BackgroundAlthough the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.ObjectiveThis study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.MethodsThis research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.ResultsIn total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.ConclusionsSelf-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

Model-driven management of BPMN-based business process families

Business processes can have variants depending on specific business requirements, which lead to the definition of a so-called business process family. Since conventional business process modeling languages, e.g., the Business Process Model and Notation (BPMN), do not explicitly support variants’ specification, several proposals have emerged to deal with it. However, they mainly focus on languages’ definition, while less emphasis is made on providing complete variability management. This article presents a Model-Driven Engineering approach for managing BPMN-based business process families composed of a metamodel for conceptualizing process families, a high-level process for managing them (involving model transformations for the configuration of variants), and tool support for the complete approach. We validated the proposal using a real-world example from a university and an empirical study with real users. Users rated the support tool’s principal functional suitability and usability features as very good. Many improvement opportunities were detected, e.g., version control, collaborative work, and error reporting. We also provide a literature review and thorough evaluation of BPMN-based business process families’ proposals using the VIVACE framework.

Guest editorial to the theme section on AI-enhanced model-driven engineering

This theme section brings together the latest research at the intersection of artificial intelligence (AI) and model-driven engineering (MDE). Over the past years, we have witnessed a substantial rise of AI successfully applied to different domains, including software development and MDE. Dedicated events at the intersection of AI and MDE have been created, too, such as the MDE Intelligence workshop series co-located with the MODELS conference. This theme section covers research contributions integrating AI components into MDE approaches—increasing the current benefits of MDE processes and tools and pushing the limits of “classic” MDE with the goal to provide software and systems engineers with the right techniques to develop the next generation of highly complex model-based systems—and applications of MDE to the development of AI components. In total, nine submissions were accepted in the theme section after a thorough peer-reviewing process.

The systems architecture ontology (SAO): an ontology-based design method for cyber–physical systems

Purpose Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related. Design/methodology/approach Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application. Findings SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies. Originality/value To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

Lean requirements traceability automation enabled by model-driven engineering.

BackgroundThe benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability is a critical, widely accepted practice. However, very often it is not applied for fear of the additional costs associated with manual efforts or the use of additional tools.MethodsThis article presents a “low-cost” mechanism for automating requirements traceability based on the model-driven paradigm and formalized by a metamodel for the creation and monitoring of traces and an integration process for traceability management. This approach can also be useful for information fusion in industry insofar that it facilitates data traceability.ResultsThis article extends an existing model-driven development methodology to incorporate traceability as part of its development tool. The tool has been used successfully by several companies in real software development projects, helping developers to manage ongoing changes in functional requirements. One of those projects is cited as an example in the paper. The authors’ current work leads them to conclude that a model-driven engineering approach, traditionally used only for the automatic generation of code in a software development process, can also be used to successfully automate and integrate traceability management without additional costs. The systematic evaluation of traceability management in industrial projects constitutes a promising area for future work.

Low-code development and model-driven engineering: Two sides of the same coin?

The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.