Validating Cyber-Physical Systems requires co-simulation of continuous physical dynamics alongside discrete control logic. However, standard fixed-step coupling often limits accuracy at the interface. Discretization errors distort signal behaviour between updates, and asynchronous events, such as safety threshold violations, are frequently detected with critical delays. In this paper we address these limitations by extending the FERAL simulation framework with a derivative-aware communication interface and an adaptive rollback mechanism. Our approach enables subsystems to extrapolate inputs continuously using First-Order Hold and allows the simulator to retroactively correct timing errors by re-simulating critical windows with high precision. We validated this methodology using an Adaptive Emergency Brake case study. Although the standard baseline simulation failed to prevent a collision due to an 891 ms detection delay, our proposed method achieved a safe stop with a response latency of < 1 ms, effectively eliminating the synchronization error. Performance analysis confirms that this 99.8\% accuracy improvement is achieved with only a marginal computational overhead, utilizing the hierarchical architecture of the framework to execute targeted rollbacks of only the affected components to the exact time of the event.

The increasing scale, distribution, and interconnection of software-intensive systems continue to change the landscape of software engineering research and practice, bringing the diffusion of two similar paradigms: Systems-of-Systems (SoS) and Software Ecosystems (SECO). SoS are characterized by the integration of operationally and managerially independent systems that join together to accomplish a common mission. SoS are central to domains such as transportation, healthcare, defense, smart cities, and industrial automation where heterogeneous systems must cooperate, exchange information, and adapt to evolving missions. On the other hand, SECO describe environments in which a platform and its surrounding network of developers, partners, and organizations co-create software offerings. In SECO, technical artifacts interact with economic and social processes. Modern digital platforms, mobile operating systems, cloud services, and enterprise platforms leverage the capabilities of their ecosystems. This editorial brings together perspectives that explore the shared challenges and complementary insights of SoS and SECO research, aiming to foster a richer understanding of complex software-intensive systems and highlight new opportunities for collaboration across communities. From the long-running, successful series of the International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS), co-located with the IEEE/ACM International Conference on Software Engineering (ICSE), we present this special issue of the Journal of Systems and Software on the topics of SESoS 2024 in Lisbon, Portugal. From a total of 18 submissions, 7 articles were accepted in this special issue. The articles in this collection address fundamental questions of SoS and SECO, including the evolution of functional relations and experimentation practices, the key factors affecting developer experience, also related to women’s inclusion, as well as the adoption of GenAI-driven approaches for vulnerability fixing. These articles offer updates on current advances of SoS and SECO engineering to researchers and practitioners, highlighting opportunities for future research.

ABSTRACT For more than a decade, software engineering for systems‐of‐systems (SoS) and software ecosystems (SECO) has been largely investigated in order to cope with complexity in software‐intensive systems. SoS research addresses several aspects related to software system architecture comprising a set of constituent systems that relate to each other to perform missions. As such, SoS have key characteristics such as operational and managerial independence, distribution, emergent behavior, and evolutionary development. Full interoperability and dynamic architecture become critical challenges in this context. On the hand, SECO research refers to modeling and analysis of a socio‐technical network of actors and artifacts formed on top of common technological platforms, in which business factors directly influence software maintenance and evolution. Software sustainability and diversity as well as quality attributes that affect the SECO platform health represent challenges in the field. From the long‐running, successful series of the International Workshop on Software Engineering for systems‐of‐systems and Software Ecosystems (SESoS), co‐located with the IEEE/ACM International Conference on Software Engineering (ICSE), we present this special issue on the topics in the Journal of Software: Evolution and Process from SESoS 2023 in Melbourne, Australia. Four articles were accepted and published in this special issue, covering a longitudinal analysis of SoS research, as well as strategic patterns, services, and trust in SECO. These articles provide researchers and practitioners with advances in the state of the art and point out opportunities for further research.

The concept of the Digital Twin (DT) and its implementation as Asset Administration Shell (AAS) is one of the key technologies for implementing Industry 4.0. By utilizing the AAS, use cases can be implemented with high interoperability. These use cases include optimizations and improvements of physical systems, like predictive maintenance or other artificial intelligence applications. For these use cases, historical data is key. However, there exists no guidance on how to handle historical data with the AAS. Furthermore, no integrated architecture exists that enables seamless storage and retrieval of historical data using the unified AAS meta-model and interface. Thus, we bridge this gap between use case and unified implementation by presenting multiple blueprints for data storage and retrieval motivated by use cases from Industry 4.0, healthcare, and civil construction. These data storage blueprints range from a mandatory change in the AAS infrastructure to augmentations of the existing AAS concepts. Furthermore, we showcase how the data model of the AAS can be utilized for a unified retrieval of historical data. In consequence, practitioners can quickly realize use cases that require historical data by tailoring and implementing the presented blueprints. Additionally, researchers can extend the presented guidance to further use cases, possibly from other domains.

Many Industry 4.0 use cases require the integration of live data, e.g., from sensors and devices. However, the large number of legacy fieldbus protocols and proprietary data formats turns this integration into an effort-consuming task. As the number of digital twins in a factory increases rapidly, data source integration has to scale well. Until now, little guidance is available on how to implement this integration in a scalable and reusable manner for Industry 4.0. To close this gap, we define five architecture blueprints based on our experience in various Industry 4.0 projects. These blueprints detail various integration scenarios differentiated by key attributes like frequency of data consumption and data production. In these architecture blueprints, two core components, the Updater and the Delegator, are identified. By providing and evaluating our open-source implementation of these two components, we show the feasibility of the defined blueprints. Utilizing the provided open-source components and the defined architecture blueprints will benefit practitioners as well as researchers when it comes to data integration with digital twins.

The increasing importance of automation and smart capabilities for factories and other industrial systems has led to the concept of Industry 4.0 (I4.0). This concept aims at creating systems that improve the vertical and horizontal integration of production through (i) comprehensive and intelligent automation of industrial processes, (ii) informed and decentralized real-time decision making, and (iii) stringent quality requirements that can be monitored at any time. The I4.0 infrastructure, supported in many cases by robots, sensors, and algorithms, demands highly skilled workers able to continuously monitor the quality of both the items to be produced and the underlying production processes. While the first attempts to develop smart factories and enhance the digital transformation of companies are under way, we need adequate methods to support the identification and specification of quality attributes that are relevant to I4.0 systems. Our main contribution is to provide a refined version of the ISO 25010 quality model specifically tailored to those qualities demanded by I4.0 needs. This model aims to provide actionable support for I4.0 software engineers that are concerned with quality issues. We developed our model based on an exhaustive analysis of similar proposals using the design science method as well as expertise from seasoned engineers in the domain. We further evaluate our model by applying it to two important I4.0 reference architectures further clarifying its application.

Abstract Traditionally, the quality of a software or system architecture has been evaluated in the early stages of the development process using architecture quality evaluation methods. Emergent approaches like Industry 4.0 require continuous monitoring of both run‐time and development‐time quality properties, in contrast to traditional systems where quality is evaluated at specific milestones using techniques such as project reviews. Considering the dynamics and minimum down‐time imposed by the industrial production domain, it must also be ensured that Industry 4.0 system evaluations are continuously performed with high confidence and with as much automation as possible, using simulations, for instance. In this regard, there is a need to develop new methods for continuously monitoring and evaluating the quality properties of software‐based systems for Industry 4.0, which must be supported by automated quality evaluation techniques. In this research we analyze traditional architecture evaluation methods and Industry 4.0 scenarios, and propose an approach based on Digital Twins and simulations to continuously evaluate runtime quality aspects of the architecture and systems of industrial production plants. The evaluation is based on the instantiation of our approach for a concrete demand of an automation plant in the automotive domain.

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In order to enable changeable production of Industry 4.0 applications, a production system should respond to unpredictable changes quickly and adequately. This requires process planning to be performed based on the real time operating conditions and dynamic changes to be handled with cognitive skills. To meet this demand, we present a process planning approach using digital twins and reinforcement learning to derive near-optimal process plans. The digital twins enable access to real-time information about the production system. They also constitute the environment for training the agent of the reinforcement learning method. The environment works as a virtual plant, containing the attributes of the product and resources, and uses simulation models of the resources to calculate the reward for an action in terms of reinforcement learning. Reinforcement learning enables our approach to derive process plans via trial and error. Besides the virtual plant, our approach has a planner, which plays the role of the agent to derive near-optimal plans by trying different actions in the virtual plant, and observes the rewards. We apply the Q-learning algorithm to derive near optimal process plans. The evaluation results show that our approach is able to derive near-optimal process plans for different problem sizes. The evaluation also demonstrated the planner's ability to identify by itself which action to take in which situation. Consequently, no modeling of the preconditions and effects of the actions is necessary.

One of the first tasks in engineering embedded systems is the selection of the hardware; more specifically, the MicroController Units (MCUs). This selection is driven by business, technical, organizational, and legal constraints. Often, the hardware is delivered together with proprietary environments in which the software to be deployed has to be developed. This imposes architecturally significant constraints that are usually communicated inappropriately (in terms of time and format) to the engineering team. Examples are the usage of proprietary data types and programming language constructs (e.g., macros in C) and limitations to the reuse of existing software assets. To overcome this challenge, we propose an approach that has two main constituents: (i) the Embedded System Checklist, which aims at guiding the selection of MCUs according to the architecture constraints; and (ii) the Embedded System ESDT Dependency (SED) Architecture View, which connects the software design view with the technical view, describing hardware-related development environment limitations and their influences on the software architecture. We evaluated the application of these concepts in two industrial projects and show that making these dependencies transparent saves a lot of effort during software system development.

Concurrent software based on a shared-memory model is predominant in industrial applications that cannot afford to execute complex message-passing libraries. However, direct access to shared memory creates implicit dependencies between concurrently executing components. Therefore, the development and maintenance of such software is hard. In this paper, we argue the need to manage, at the architectural level, the implicitly high coupling between concurrent components that share memory. We suggest an approach that verifies architectural specifications against the implementation and finds potential mismatches. While static analysis approaches can be complete and verify all possible mismatches, they are often imprecise, leading to a large number of false warnings, especially in concurrent software. Instead, we built our approach, using dynamic analysis, on top of one of the most well-known algorithms for detecting data races, Eraser Lockset, and extended its model to support features required for the verification process. Since Lockset operates on the execution traces, test cases that produce these traces must ensure proper coverage. Therefore, we argue the need to use test cases conforming to the strict modified condi-tion/decision coverage criteria (MC/DC). Our version of Lockset takes advantage of the fact that possible shared memory locations are known in advance. We further improved its precision by considering atomic operations as a synchronization mechanism. The approach was evaluated on industrial AUTOSAR drivers that execute concurrently.

The growing usage of Domain Specific Modeling Languages (DSML) for architecture view frameworks induces a need for automatic verification of non-functional model properties like completeness and consistency. However, we argue that the high demand for tailored architecture view frameworks is not complemented by appropriate constraint specification facilities. OCL is a common language for defining modeling constraints, but industry user reports indicate that despite its accuracy, it is too complex to be adopted in industrial scale. Approaches that were proposed to simplify the use of OCL either operate on technical formalisms or lack tool support to express new, or more complex types of constraints that can be validated automatically on the model. To address this challenge, we present a constraint modeling framework for the specification and validation of constraints on DSMLs. A Constraint Modeling Language (CML) created based on this framework provides a high level constraint specification en- vironment by using extensible template implementations to enable the automatic validation in computer aided software engineering (CASE) tools. We evaluate the approach in different industry projects and observe that using the proposed framework enhances understandability and effectiveness of constraint specification.

We have investigated approaches in the literature to assess the quality of the software architectures of medical devices, and have found evidence that there is a lack of methodologies for evaluating the software architecture design aspects of medical devices that might affect system safety. Such evidences were identified when evaluating the software architecture of the FDA Generic Infusion Pump searching for architectural evaluation approaches available in the literature. In order to fill this gap, we propose a set of quality questions that focus on analyzing software architecture design aspects of medical devices aiming safety. We show arguments on why reference projects such as the FDA Generic Infusion Pump system must satisfy our new quality questions. The quality questions were integrated into a quality model commissioned by the Brazilian Health Ministery for the certification of medical devices.

With the advent of new computing paradigms, such as Ubiquitous Computing, Ambient Intelligence, and Cyber Physical Systems, promising application domains like Ambient Assisted Living (AAL) and Car2Car have emerged. One key concern in these application domains is that systems are required to dynamically adapt in reaction to changes within the system or its environment. As a consequence, it is indispensable to incorporate corresponding facilities within systems, yielding adequate adaptation behavior to appropriately react on such dynamic changes. The definition of adaptation behavior is, however, a complex task in its own. In order to help engineers working on adaptive systems, we propose in this paper a generic testbed that allows specifying adaptation strategies and evaluating them in a runtime context to improve the adaptation behavior. Our approach further introduces basic visualization features as a means for the engineers to better evaluate the complex dynamic behavior resulting from runtime adaptation.