Despite intensified recycling efforts, global material consumption has more than tripled over the past five decades, while the global circularity rate has declined. This widening gap reveals that incremental improvements are insufficient and that a transition toward circular economy must be structural. This transition is constrained by fragmented data ecosystems, limited transparency among stakeholders, and conventional digital twin architectures that struggle to represent the dynamic, multi-loop nature of circular supply networks. To address these challenges, this position paper discusses a conceptual Federated Digital Twin framework that integrates federated system architectures to protect data sovereignty, intelligent agents to enable real-time negotiation and synchronization, and knowledge graphs to ensure semantic interoperability across distributed actors. The framework serves as a decision-support backbone for circular manufacturing, aiming for a coordinated management and systemic coherence of value-retention strategies and production planning for circular supply chains.

This study introduces a novel framework that brings together two main Quantum Programming methodologies, gate-based Quantum Computing and Quantum Annealing, by applying the Model-Driven Engineering principles. This aims to enhance the adaptability, design and scalability of quantum programs, facilitating their design and operation across diverse computing platforms. A notable achievement of this research is the development of a mapping method for programs between gate-based quantum computers and quantum annealers which can lead to the automatic transformation of these programs. Specifically, this method is applied to the Variational Quantum Eigensolver Algorithm and Quantum Anneling Ising Model, targeting ground state solutions. Finding ground-state solutions is crucial for a wide range of scientific applications, ranging from simulating chemistry lab experiments to medical applications, such as vaccine development. The success of this application demonstrates Model-Driven Engineering for Quantum Programming frameworks's practical viability and sets a clear path for quantum Computing's broader use in solving intricate problems.

This paper introduces a domain-specific modelling language (DSML) called DSML4JaCaMo to develop belief-desireintention (BDI) agents.The DSML's design covers aspects of Jason, Cartago, and Moise from viewpoints that follow the metamodelling approach.In this way, the DSML4JaCaMo enables graphical modelling of JaCaMo's multi-agent systems (MASs), providing comprehensive support for defining agents' beliefs, desires, and intentions (BDI) using Jason, specifying artifacts and their operations with Cartago, and outlining organizational structures and norms via Moise.The DSML's operational semantics ensure seamless integration of these components, facilitating automatic code generation and artifact construction for creating a JaCaMo-based system.The graphical syntax contributes to ease of use, making it accessible for novice and experienced developers.This work aims to enhance the JaCaMo ecosystem by offering a model-driven approach to provide abstraction on MAS development as well as facilitating design and implementation.

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They have identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the Machine Learning (ML) and data science-related concerns of data scientists and data engineers are yet to be included in existing architecture frameworks. We interviewed 65 experts from around 25 organizations in over ten countries to devise and validate the proposed framework that addresses the mentioned shortcoming.

Software engineering of network-centric Artificial Intelligence (AI) and Internet of Things (IoT) enabled Cyber-Physical Systems (CPS) and services, involves complex design and validation challenges. In this paper, we propose a novel approach, based on the model-driven software engineering paradigm, in particular the domain-specific modeling methodology. We focus on a sub-discipline of AI, namely Machine Learning (ML) and propose the delegation of data analytics and ML to the IoT edge. This way, we may increase the service quality of ML, for example, its availability and performance, regardless of the network conditions, as well as maintaining the privacy, security and sustainability. We let practitioners assign ML tasks to heterogeneous edge devices, including highly resource-constrained embedded microcontrollers with main memories in the order of Kilobytes, and energy consumption in the order of milliwatts. This is known as Tiny ML. Furthermore, we show how software models with different levels of abstraction, namely platform-independent and platform-specific models can be used in the software development process. Finally, we validate the proposed approach using a case study addressing the predictive maintenance of a hydraulics system with various networked sensors and actuators.

Abstract 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.

To cope with the challenges of constructing Cyberphysical Production Systems (CPPS), many studies propose benefiting from agent systems.However, industrial processes should be mostly emulated while agent-based solutions are integrating with CPPS since it is not always possible to apply cyber-based solutions to these systems directly.The target system can be miniaturised while sustaining its functionality.Hence, in this paper, we introduce an agent-based industrial production line and discuss the system development using Lego technology while providing integration of software agents as well as focusing on low-level requirements.In this way, a CPPS is emulated while agents control the system.

One of the main aims in the layered software architecture is to divide the code into different layers so that each layer contains related modules and serves its upper layers. Although layered software architecture is matured now; many legacy information systems do not benefit from the advantages of this architecture and their code for the process/business and data access are mostly in a single layer. In many legacy systems, due to the integration of the code in one layer, changes to the software and its maintenance are mostly difficult. In addition, the big size of a single layer causes the load concentration and turns the server into a bottleneck where all requests must be executed on it. In order to eliminate these deficiencies, this paper presents a refactoring mechanism for the automatic separation of the business and data access layers by detecting the data access code based on a series of patterns in the input code and transferring it to a new layer. For this purpose, we introduce a code scanner which detects the target points based on these patterns and hence automatically makes the changes required for the layered architecture. According to the experimental evaluation results, the performance of the system is increased for the layer separated software using the proposed approach. Furthermore, it is examined that the application of the proposed approach provides additional benefits considering the qualitative criteria such as loosely coupling and tightly coherency.

In this paper, we introduce a tool-supported reverse engineering methodology, called RE4TinyOS to create or update application models from TinyOS programs for the construction of Wireless Sensor Networks.Integrating with an existing modeldriven engineering (MDE) environment, use of RE4TinyOS enables the model-code synchronization where any modification made in the TinyOS application code can be reflected into the application model and vice versa.Conducted case studies exemplified this model-code synchronization as well as the capability of creating application models completely from already existing TinyOS applications without models, which is crucial to integrate the implementations of the third party TinyOS applications into the MDE processes.Evaluation results showed that RE4TinyOS succeeded in the reverse engineering of all main parts of two well-known TinyOS applications taken from the official TinyOS Github repository and generated models were able to be visually processed in the MDE environment for further modifications.

Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently.In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms.A case study is presented as an example of possible improvements to make a model-based testing approach more efficient.We demonstrate layered ESG models as an example of our approach to consider the scalability problem.We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied.

Compliance monitoring and surveillance is an important task in hospitals and health centers as the health-care associated infections can have serious consequences such as increasing mortality and accelerating morbidity. Monitoring and surveillance by human observers have its own difficulties such as loss of observer concentration, human error build-up, and professional deformation. In this work, we have implemented and compared the performance of two Internet of Things based approaches for monitoring the hand hygiene of medical staff during patient visiting. We used ESP modules as base stations and smart-phones as mobile nodes and estimated the distances using Bluetooth RSSI values to locate the medical staff in a patient's room. In the proximity-based solution, we compared the RSSI from a mobile node measured on different ESP nodes/modules and utilized the assumption stating that the mobile node is closest to an ESP node which yields the highest RSSI value. In the trilateration based approach, we used the RSSI values to estimate the distance of mobile nodes to each ESP node and we used a trilateration algorithm to locate the mobile node in the room. Our experiments showed that the proximity-based solution recorded 20% incorrect location visiting while the percentage for the trilateration based solution was 8%. This indicates that the trilateration based approach is more reliable than the proximity-based solution in this application domain.

Development of software agents according to belief–desire–intention (BDI) model usually becomes challenging due to autonomy, distributedness, and openness of multi‐agent systems (MAS). Hence, here, a domain‐specific modelling language (DSML), called DSML4BDI, is introduced to support development of BDI agents. The syntax of the language provides the design of agent components required for the construction of the system according to the specifications of BDI architecture. The implementation of designed MAS on Jason BDI platform is also possible via model‐to‐text transformations built in the DSML. The comparative evaluation results showed that a significant amount of artefacts required for the exact MAS implementation can be automatically achieved by employing DSML4BDI. Moreover, time needed for developing a BDI agent system from scratch can be reduced to one‐third in the case of using DSML4BDI. Finally, qualitative assessment, based on the developers’ feedback, exposed how DSML4BDI facilitates development of BDI agents.

In this paper, we present our approach which aims at improving the mechanism of constructing language semantics over the interoperability of domain-specific modeling languages (DSMLs) developed for Multi-agent Systems (MAS) and hence providing a more efficient way of extension for the executability of modeled agent systems on various underlying agent platforms.Differentiating from the existing MAS DSML studies, our proposal is based on determining entity mappings and building horizontal model transformations between the metamodels of MAS DSMLs which are in the same abstraction level.The applicability of the approach is demonstrated in the paper by constructing horizontal transformations between two full-fledged agent DSMLs, called SEA_ML and DSML4MAS.Use of these transformations has enabled SEA_ML instance models now to be executable on new agent platforms and that feature has been provided with less effort comparing with the implementation of needed transformations between SEA_ML and those new agent platforms from scratch.

Development of agent systems is without question a complex task when autonomous, reactive and proactive characteristics of agents are considered. Furthermore, internal agent behavior model and interaction within the agent organizations become even more complex and hard to implement when new requirements and interactions for new agent environments such as the Semantic Web are taken into account. We believe that the use of both domain specific modeling and a Domain-specific Modeling Language (DSML) may provide the required abstraction and support a more fruitful methodology for the development of Multi-agent Systems (MASs) especially when they are working on the Semantic Web environment. Although syntax definition based on a metamodel is an essential part of a modeling language, an additional and required part would be the determination and implementation of DSML constraints that constitute the (formal) semantics which cannot be defined solely with a metamodel. Hence, in this paper, formal semantics of a MAS DSML called Semantic Web enabled Multi-agent Systems (SEA_ML) is introduced. SEA_ML is a modeling language for agent systems that specifically takes into account the interactions of semantic web agents with semantic web services. What is more, SEA_ML also supports the modeling of semantic agents from their internals to MAS perspective. Based on the defined abstract and concrete syntax definitions, we first give the formal representation of SEA_ML's semantics and then discuss its use on MAS validation. In order to define and implement semantics of SEA_ML, we employ Alloy language which is declarative and has a strong description capability originating from both relational and first-order logic in order to easily define complex structures and behaviors of these systems. Differentiating from similar contributions of other researchers on formal semantics definition for MAS development languages, SEA_ML's semantics, presented in this paper, defines both static and dynamic aspects of the interaction between software agents and semantic web services, in addition to the definition of the semantics already required for agent internals and MAS communication. Implementation with Alloy makes definition of SEA_ML's semantics to include relations and sets with a simple notation for MAS model definitions. We discuss how the automatic analysis and hence checking of SEA_ML models can be realized with the defined semantics. Design of an agent-based electronic barter system is exemplified in order to give some flavor of the use of SEA_ML's formal semantics. Lessons learned during the development of such a MAS DSML semantics are also reported in this paper.

SUMMARY Stock trading is one of the key items in an economy and estimating its behavior and taking the best decision in it are among the most challenging issues. Solutions based on intelligent agent systems are proposed to cope with those challenges. Agents in a multiagent system (MAS) can share a common goal or they can pursue their own interests. That nature of MASs exactly fits the requirements of a free market economy. Although existing studies include noteworthy proposals on agent‐based market simulation and researchers discuss theoretical design issues of agent‐based stock exchange systems, unfortunately only a very few of the studies consider exact development and implementation of multiagent stock trading systems within the software engineering perspective and guides to the software engineers for constructing such software systems starting from scratch. To fill this gap, in this paper, we discuss the development of a multiagent‐based stock trading system by taking into consideration software design according to a well‐defined agent oriented software engineering methodology and implementation with a widely‐used MAS software development framework. Each participant in the system is first designed as belief–desire–intention agents with their facts, goals, and plans, and then belief–desire–intention reasoning and behavioral structure of the designed agents are implemented. Lessons learned during design and development within the software engineering perspective and evaluation of the implemented multiagent stock exchange system are also reported. Copyright © 2011 John Wiley & Sons, Ltd.