Software engineering education must be guided by developments in software engineering and aim for professional and educational development of students. This position paper explores the historical and conceptual evolution of software engineering as a discipline central to modern integrated design, software systems, and process science, tracing the trajectory from early structured models, through waterfall, to Agile and AI-augmented paradigms, and looks at how changes in application mix and modes of use, technological complexity, and business and enterprise needs have shaped development methodologies. Special attention is paid to the role of generative AI on the software engineering process and the role of the software engineer, altering workflows, collaboration, and enterprise architectures, and further enabling automation, digital transformation, and autonomous systems, shifting responsibilities and skill sets upwards. The article then considers corresponding shifts in software engineering education, highlighting the need for curricula that intertwine process thinking, systems theory, and ethical engagement, integrate with generative AI technologies, and place greater emphasis on “soft skills”, in courses that combine development of professional expertise with conceptual capabilities and an enhanced capacity for life-long learning. It then presents a set of requirements, options, and guidelines for software engineering education in the era of generative AI.

This Extended Editorial has been compiled by the members of the Editorial Board to celebrate the 25th anniversary of the establishment of the Journal of Integrated Design and Process Science, which operates as the Transactions of the Society for Process and Design Science. The paper divides in three parts. The first part provides a detailed overview of the preliminaries, the objectives, and the periods of operation. It also includes a summary of the current application-orientated professional fields of interests, which are: (i) convergence mechanisms of creative scientific disciplines, (ii) convergence of artificial intelligence, team and health science, (iii) convergence concerning next-generation cyber-physical systems, and (iv) convergence in design and engineering education. The second part includes invited papers, which exemplify domains within the four fields of interest, and also represent good examples of science communication. Short synopses of the contents of these representative papers are included. The third part takes the major changes in scientific research and the academic publication arena into consideration, circumscribes the mission and vision as formulated by the current Editorial Board, and elaborates on the planned strategic exploration and utilization domains of interest.

The design intent and the performance targets of projects may sometimes fail to match a building’s actual post-occupancy performance. The mismatch of intended and actual building performance can be attributed to multifarious reasons. This study focuses on the role of project decisions made during design as one of the reasons of shortfall. The aim of the study is to unveil the design decision-making process of a state-of-the-art research building through the analysis of project’s available set of IDP (Integrated Design Process) documentation. To understand the relationship and correlation between the energy performance gap and the decision-making process of the case building, we investigated the design decisions’ effect on the actual performance. The particular emphasis is on the decisions that were based on assumptions rather than measured actual test data for the proposed innovative building systems. The designed heat recovery system, which was dependent on recovered heat from the neighboring research building, had a significant effect on the building’s poor energy performance. We investigated collected project data from coordination meetings, thoroughly analyzed project documentation, and quantified the building’s actual energy performance data. The analysis of the project information shows the ripple effect of decisions that were made based on assumptions, that triggered shortfalls in the building’s overall actual performance. Our qualitative analysis indicates that the poor system performance during operations was related with the design decisions that were not based on the measurement of the actual performance of the existing systems in the neighboring building. The performance of the heat recovery from the neighboring building as a highly dependent Energy Conservation Measure (ECM) analyzed through collected documents and data. The ambiguity of the available heat potential from the neighboring building and related testing issues defined on an explanatory timeline of process coding. The conclusion includes recommendations for the design decision-making process for innovative system integrations for high-performance buildings, and underlines the importance of IDP for complex buildings.

Socio-cyber-physical systems (SCPSs) are cyber-physical systems with social concerns. Many emerging SCPSs, often qualified as “smart”, need such concerns to be addressed not only at design time but also at runtime, often by adapting dynamically to surrounding contexts, to keep providing optimal value to users. A comprehensive requirements and design modeling approach is needed to incorporate social concerns (e.g., using goal modeling) into SCPS development activities. This paper introduces an optimization method that provides design-time and runtime solutions for self-adaptive SCPSs while supporting the validation of their design models. The method helps satisfying the goals of the SCPS and its stakeholders by monitoring the system’s environment and qualities, while enforcing correctness constraints specified in a feature model. We integrate arithmetic functions generated automatically from goal and feature models to build a combined goal-feature model and synchronize the values of the features shared between i) the objective function represented by goal functions, and ii) the constraints represented by feature functions. The goal-feature model is solved by an optimization tool (IBM CPLEX) in order to calculate optimal adaptation solutions for common situations at design time. Runtime optimization is also used by the system for adapting to situations unanticipated during design. We use a Smart Home Management System case study to assess how well the method can be used to manage selection among alternatives according to monitored environmental conditions while solving emergent conflicts. Further experiments on the use of the method for runtime adaptation show good performance for realistic models and good scalability overall. Some remaining challenges and limitations exist, including the availability of quantitative models as inputs.

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

Existing formal languages for the specification of self-adaptive cyber-physical systems focus on re-configuring the system-to-be depending on its current context, to satisfy the user’s requirements, that is by dynamically composing the software’s structure and behavior. While these approaches specify context-sensitive requirements, they rarely consider their run-time dynamic and scalable nature. The State-Constraint Transition (SCT) modeling language, introduced in this paper, provides an answer to the problems linked to the specification of dynamic requirements by introducing the concept of configuration states, in which requirements are translated into constraints. The expressiveness of existing approaches is thus extended, combining the ease of use of well-established notations, notably those based on characteristics, and those based on Finite-state Machines (FSM), with the computational power and expressiveness of the constraint programming approach. The paper briefly presents the results of the preliminary evaluation, which assesses the expressiveness, scalability, and domain independence of the SCT language.

<p>Cyber-physical systems (CPSs) represent practical examples of the integration of bits and atoms in human and social contexts and contribute to the integration of neurons and genes into system implementation. Many experts believe that a deeper theoretical understanding of system adaptation will lead to the development of autonomous systems and adjustable autonomy. In terms of the introduction of changes, adaptation can be made during idle time or runtime. In addition, adaptation can be externally initiated and internally initiated. In terms of intentionality indispensable, planned, or self-decided adaptations are distinguished. Adaptations are planned during the design time, runtime, or in both. Internally initiated adaptation is self-adaptation, a form of system operation in which the goals and rules of adaptation are not provided by external controllers. Internally initiated control intertwines the logics of application functions and adaptation functions. This approach is based on programming language features, such as conditional expressions, parameterization, and exceptions in software (SW) systems.</p>

In engineering, design decisions in one domain exhibit multiple consequences in other domains. These consequences result from the often more or less hidden coupling between the different design domains. In order to examine these consequences, models need to be created. In practice, this is challenging due to the exchange of data between different engineering domains, since different software applications are often used and the effort involved with manual model creation. In this paper, we explore the use of graph-based design languages in a Model-Based Systems Engineering (MBSE) approach to link the digital factory with building design. We also show that the use of a common formal representation based on the Unified Modeling Language (UML) supports the interoperability between the two domains. Finally, we demonstrate how the engineering knowledge for the preliminary design of a factory building can be formally described using graph-based design languages and how the production line of the digital factory can then be used as an input to automatically create valid preliminary designs for the factory building.1

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns <GA, A*> and <PSO, A*>. GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster.

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.