Software Ecosystem (SECO) has emerged as a crucial concept, which represents a collaborative and interconnected environment in which a variety of actors engage in developing software systems. SECOs play a key role in the development of Cyber-Physical Systems (CPSs), that present a myriad of challenges, primarily due to the need for real-time responsiveness, reliability, security, and interoperability. The implications of leveraging SECOs for developing CPSs are profound in both research and practice. This paper aims to understand the collaboration between industry and academia within SECOs for the development of CPSs, identifying potential challenges and providing insights and guidelines for the proper management of these collaborations. We conducted a systematic literature review (SLR), complemented by empirical evidence collected through an opinion survey administered to the partners of the European collaborative project AIDOaRt, a concrete example of a SECO, which worked on the development of CPSs. From these findings we discuss the identified challenges, and potential effects on collaboration, in addition to our lessons learned in the AIDOaRt project and SECO.

Digital twins are virtual representations of real-world entities or systems. Their primary goal is to help organizations understand and predict the behaviour and properties of these entities or systems. Additionally, digital twins enhance activities such as monitoring, verification, validation, and testing. However, the inherent complexity of digital twins implies challenges throughout the systems engineering process. This notably includes design, development, and analysis phases, as well as deployment, execution, and maintenance. Moreover, existing approaches, methods, techniques, and tools for modelling, simulating, validating, and monitoring single digital twins must now address the increased complexity in federation scenarios. These scenarios introduce new challenges, such as digital twin identification, shared metadata, cross-digital twin communication and synchronization, and federation governance. The KDT Joint Undertaking MATISSE project tackles these challenges by aiming to provide a model-driven framework for the continuous engineering of federated digital twins. It leverages model-driven engineering techniques and practices as the core enabling technology, with traceability serving as an essential infrastructural service for the digital twins federation. In this paper, we introduce the MATISSE conceptual framework for digital twins, highlighting both the novelty of the project's concept and its technical objectives. As the project is still in its initial phase, we identify key research challenges relevant to the DATE community and propose a preliminary research roadmap. This roadmap addresses traceability and federation mechanisms, the required continuous engineering strategy, and the development of digital twin-based services for verification, validation, prediction, and monitoring. To illustrate our approach, we present two concrete scenarios that demonstrate practical applications of the MATISSE conceptual framework.

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.

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board. (see [https://www.sciencedirect.com/science/article/pii/S0164121221002168] for an example for where to place the statement and how to format it).

Summary Cyber‐physical systems (CPSs) are increasing in developmental complexity. Several emerging technologies, such as Model‐based engineering, DevOps, and Artificial intelligence, are expected to alleviate the associated complexity by introducing more advanced capabilities. The AIDOaRt research project investigates how the aforementioned technologies can assist in developing complex CPSs in various industrial use cases. In this paper, we discuss the experiences of industry and academia collaborating to improve the development of complex CPSs through the experiences in the research project. In particular, the paper presents the results of two working groups that examined the challenges of developing complex CPSs from an industrial and academic perspective when considering the previously mentioned technologies. We present five identified challenge areas from developing complex CPSs and discuss them from the perspective of industry and academia: data, modeling, requirements engineering, continuous software and system engineering, as well as intelligence and automation. Furthermore, we highlight practical experience in collaboration from the project via two explicit use cases and connect them to the challenge areas. Finally, we discuss some lessons learned through the collaborations, which might foster future collaborative efforts.

Abstract Modeling and simulation represent an essential part of overall systems engineering. Complex engineering systems are composed of many heterogeneous components often modeled and simulated employing different languages and environments, and often by different organizations; thus, demands for interoperability are getting increased. Model‐driven engineering (MDE) has been demonstrated to be an advancement in software engineering: existing software in several domains today benefits from abstraction and automation during the system development process. Although these techniques are now highly advanced, many industries may require considerable effort before fully benefiting from them. This article reports on a case study carried out for 12 months within a company in the space industry domain. The goal of this empirical study is to investigate the adoption of MDE in supporting simulation models interoperability. The article identifies factors considered important for MDE adoption as well as obstacles that can be encountered in a real case. Researchers and practitioners may benefit from our findings typically when reusing models across different simulation environments, exchanging simulation models between different stakeholders, and seeking to improve simulation modeling practices.

Internet of Things (IoT) products provide over-the-net capabilities such as remote activation, monitoring, and notifications. An associated mobile app is often provided for more convenient usage of these capabilities. The perceived quality of these companion apps can impact the success of the IoT product. We investigate the perceived quality and prominent issues of smart-home IoT mobile companion apps with the aim of deriving insights to: (i) provide guidance to end users interested in adopting IoT products; (ii) inform companion app developers and IoT producers about characteristics frequently criticized by users; (iii) highlight open research directions. We employ a mixed-methods approach, analyzing both quantitative and qualitative data. We assess the perceived quality of companion apps by quantitatively analyzing the star rating and the sentiment of 1,347,799 Android and 48,498 iOS user reviews. We identify the prominent issues that afflict companion apps by performing a qualitative manual analysis of 1,000 sampled reviews. Our analysis shows that users’ judgment has not improved over the years. A variety of functional and non-functional issues persist, such as difficulties in pairing with the device, software flakiness, poor user interfaces, and presence of issues of a socio-technical impact. Our study highlights several aspects of companion apps that require improvement in order to meet user expectations and identifies future directions.

Microservices are quite widely impacting on the software industry in recent years. Rapid evolution and continuous deployment represent specific benefits of microservice-based systems, but they may have a significant impact on non-functional properties like performance. Despite the obvious relevance of this property, there is still a lack of systematic approaches that explicitly take into account performance issues in the lifecycle of microservice-based systems. In such a context of evolution and re-deployment, Model-Driven Engineering techniques can provide major support to various software engineering activities, and in particular they can allow managing the relationships between a running system and its architectural model. In this paper, we propose a model-driven integrated approach that exploits traceability relationships between the monitored data of a microservice-based running system and its architectural model to derive recommended refactoring actions that lead to performance improvement. The approach has been applied and validated on two microservice-based systems, in the domain of e-commerce and ticket reservation, respectively, whose architectural models have been designed in UML profiled with MARTE.

With the emergence of Cyber-Physical Systems (CPS), the increasing complexity in development and operation demands for an efficient engineering process. In the recent years DevOps promotes closer continuous integration of system development and its operational deployment perspectives. In this context, the use of Artificial Intelligence (AI) is beneficial to improve the system design and integration activities, however, it is still limited despite its high potential. AIDOaRT is a 3 years long H2020-ECSEL European project involving 32 organizations, grouped in clusters from 7 different countries, focusing on AI-augmented automation supporting modelling, coding, testing, monitoring and continuous development of Cyber-Physical Systems (CPS). The project proposes to apply Model-Driven Engineering (MDE) principles and techniques to provide a framework offering proper AI-enhanced methods and related tooling for building trustable CPSs. The framework is intended to work within the DevOps practices combining software development and information technology (IT) operations. In this regard, the project points at enabling AI for IT operations (AIOps) to auto-mate decision making process and complete system development tasks. This paper presents an overview of the project with the aim to discuss context, objectives and the proposed approach.

With the ever-increasing evolution of software systems, their architecture is subject to frequent changes due to multiple reasons, such as new requirements. Appropriate architectural changes driven by non-functional requirements are particularly challenging to identify because they concern quantitative analyses that are usually carried out with specific languages and tools. A considerable number of approaches have been proposed in the last decades to derive non-functional analysis models from architectural ones. However, there is an evident lack of automation in the backward path that brings the analysis results back to the software architecture. In this paper, we propose a model-driven approach to support designers in improving the availability of their software systems through refactoring actions. The proposed framework makes use of bidirectional model transformations to map UML models onto Generalized Stochastic Petri Nets (GSPN) analysis models and vice versa. In particular, after availability analysis, our approach enables the application of model refactoring, possibly based on well-known fault tolerance patterns, aimed at improving the availability of the architectural model. We validated the effectiveness of our approach on an Environmental Control System. Our results show that the approach can generate: (i) an analyzable availability model from a software architecture description, and (ii) valid software architecture models back from availability models. Finally, our results highlight that the application of fault tolerance patterns significantly improves the availability in each considered scenario. The approach integrates bidirectional model transformation and fault tolerance techniques to support the availability-driven refactoring of architectural models. The results of our experiment showed the effectiveness of the approach in improving the software availability of the system.

Model-Driven Engineering techniques may achieve a major support to the software development when they allow to manage relationships between a running system and its architectural model. These relationships can be exploited for different goals, such as the software evolution due to new functional requirements. In this paper, we define and use relationships that work as support to the performance improvement of a running system. In particular, we combine: (i) a bidirectional model transformation framework tailored to define relationships between performance monitoring data and an architectural model, with (ii) a technique for detecting performance antipatterns and for suggesting architectural changes, aimed at removing performance problems identified on the basis of runtime information. The result is an integrated approach that exploits traceability relationships between the monitoring data and the architectural model to derive recommended refactoring solutions for the system performance improvement. The approach has been applied to an e-commerce application based on microservices that has been designed by means of UML software models profiled with MARTE.

Software architecture is nowadays subject to frequent changes due to multiple reasons, such as evolution induced by new requirements. Architectural changes driven by non-functional requirements are particularly difficult to identify, because they attain quantitative analyses that are usually carried out with specific languages and tools. A considerable number of approaches, based on model transformations, have been proposed in the last decades to derive non-functional models from software architectural descriptions. However, there is a clear lack of automation in the backward path that brings the analysis results back to the software architecture. In this paper we address this problem in the context of software availability. We introduce a bidirectional model transformation between UML State Machines (SM), annotated with availability properties, and Generalized Stochastic Petri Nets (GSPN). Such transformation, implemented in the JTL language, is used both to derive a GSPN-based availability model from a SM-based software architecture and, after the analysis, to propagate back on the SM the changes carried out on the GSPN. We demonstrate the effectiveness of our approach on an Environmental Control System to which we apply well-known fault tolerance patterns aimed at improving its software availability.

In Model-Driven Engineering bidirectionality in transformations is regarded as a key mechanism. Recent approaches to non-deterministic transformations have been proposed for dealing with non-bijectivity. Among them, the JTL language is based on a relational model transformation engine which restores consistency by returning all admissible models. This can be regarded as an uncertainty reducing process: the unknown uncertainty at design-time is translated into known uncertainty at run-time by generating multiple choices. Unfortunately, little changes in a model usually correspond to a combinatorial explosion of the solution space. In this paper, we propose to represent the multiple solutions in a intensional manner by adopting a model for uncertainty. The technique is applied to JTL demonstrating the advantages of the proposal.

In Model-Driven Engineering, models are primary artifact manipulated by means of automated transformations. Recently, a notion of uncertainty has been introduced in models permitting modelers to postpone design decisions in case of lack of information. Interestingly, other forms of model uncertainty are induced by bidirectional transformations. In fact, in certain situations more than one admissible solution is in principle possible, despite most of the current languages generate only one model at time, possibly not the desired one. In this paper, the uncertainty due to the solution multiplicity in bidirectional transformations is discussed. In particular, we propose to represent the models in the solution space as concretizations of an uncertain model because there are cases where the responsibility of identifying the solution must be left to the modeler. The problem is illustrated by a round-tripping scenario realized with the JTL transformation language.

Software development is witnessing the increasing need of version management techniques for supporting the evolution of model-based artefacts. In this respect, metamodels can be considered one of the basic concepts of model-driven engineering and are expected to evolve during their life-cycle. As a consequence, models conforming to changed metamodels have to be updated for preserving their well-formedness. This paper deals with the co-adaptation problems by proposing higher-order model transformations which take a difference model recording the metamodel evolution and produce a model transformation able to co-evolve the involved models.