In the evolving educational landscape of the 21st century, innovative pedagogical methods like the Flipped Classroom (FC) and Personalized Learning (PL) have increased renown.The FC methodology revolutionizes traditional teaching by moving initial concept exposure outside the classroom, allowing in-class time for interactive and practical activities.This approach increases a dynamic learning environment, enhancing critical thinking, problem-solving, and collaboration skills.Successful FC implementation involves comprehensive educational experiences utilizing digital resources and active classroom interactions.PL adapts teaching to individual student needs, recognizing diverse learning speeds and cognitive styles.By leveraging technology, PL provides customized educational experiences that increase learner autonomy and motivation, leading to deeper understanding and engagement.A case study on teaching C programming using a Personalized Flipped Classroom (PFC) approach illustrates the practical application of these methodologies.The course design includes structured planning, multimedia resources, and continuous evaluation, promoting effective learning.Students engage with instructional videos and practical exercises, promoting autonomy and active participation.The course covers fundamental programming concepts, with a thematic progression that balances foundational understanding and advanced topics.Despite challenges like increased workload and digital competency gaps, the PFC approach demonstrates significant potential in enhancing student performance and skill development.

This contribution delves into the incorporation of Sustainable Development Goals (SDGs) into the Computer Engineering curriculum.The study addresses challenges associated with integrating SDGs as cross-cutting content in higher education.Employing a Problem-Based Learning approach, semester projects are aligned with SDGs, and their impact is systematically assessed.Key research questions guide the study, evaluating students' knowledge, perceptions, and the alignment of projects with SDGs.The methodology integrates SDG-focused learning units within the existing Problem-Based Learning structure.New activities, including initial training, initial assessment, impact assessment, and SDG learning assessment, guide and evaluate students' integration of SDGs.Results demonstrate successful implementation across three years of the Computer Engineering degree.The study involves a significant number of subjects, students, and teams, ensuring a comprehensive evaluation.A rubric assesses SDG impact, emphasizing justification and direct positive contributions to SDG objectives.

In many domains, engineers build simulation models (e.g., Simulink) before developing code to simulate the behavior of complex systems (e.g., Cyber-Physical Systems). Those models are commonly heavy to simulate which makes it difficult to execute the entire test suite. Furthermore, it is often difficult to measure white-box coverage of test cases when employing such models. In addition, the historical data related to failures might not be available. This paper proposes a cost-effective approach for test case selection that relies on black-box data related to inputs and outputs of the system. The approach defines in total five effectiveness measures and one cost measure followed by deriving in total 15 objective combinations and integrating them within Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). We empirically evaluated our approach with all these 15 combinations using four case studies by employing mutation testing to assess the fault revealing capability. The results demonstrated that our approach managed to improve Random Search by 26% on average in terms of the Hypervolume quality indicator.

The test case generation and prioritization of industrial cyber-physical systems face critical challenges, and simulation-based testing is one of the most commonly used techniques for testing these complex systems. However, simulation models of industrial CPSs are usually very complex, and executing the simulations becomes computationally expensive, which often make it infeasible to execute all the test cases. To address these challenges, this paper proposes a multi-objective test generation and prioritization approach for testing industrial CPSs by defining a fitness function with four objectives and designing different crossover and mutation operators. We empirically evaluated our fitness function and designed operators along with five multi-objective search algorithms [e.g., nondominated sorting genetic algorithm (NSGA-II)] using four case studies. The evaluation results demonstrated that NSGA-II achieved significantly better performance than the other algorithms and managed to improve random search for on average 43.80% for each objective and 49.25% for the quality indicator hypervolume.

The test case generation of Cyber-Physical Systems (CPSs) face critical challenges that traditional methods such as Model-Based Testing cannot deal with. As a result, simulation-based testing is one of the most commonly used techniques for testing CPSs despite sometimes being computationally too expensive. This paper proposes a search-based approach which is implemented on top of Non-dominated Sorting Genetic Algorithm II (NSGA-II), the most commonly applied multi-objective search algorithm for cost-effectively generating executable test cases in order to test CPSs. With the aim of guiding the generation of the optimal set of so-called reactive test cases, the approach formally defines three cost-effectiveness measures: requirements coverage, test case similarity and test execution time. Furthermore, we design one crossover operator and three mutation operators (i.e., mutation at test suite level named Mu TS, mutation at test case level named Mu TC and mutation at both levels named Mu BO) for test case generation. We evaluate our approach by comparing with Random Search (RS) using four case studies (one of them is an industrial system). Moreover, we evaluate the three mutation operators using the four case studies. The results of the experiment (with a rigorous statistical analysis) indicated that our approach in conjunction with the crossover operator operation and three mutation operators significantly outperformed RS. In general, Mu BO achieved the best performance among the three mutation operators and managed to improve on average the test execution time by 14%, the requirements coverage by 34%, and the test similarity by 75% as compared with RS.

With buildings accounting for nearly 40 percent of global energy consumption, the improvement of energy efficiency in buildings and districts is a clear opportunity in the fight against climate change. This has led engineering practitioners as well as researchers to propose solutions for smart energy control in buildings. Simulation-based methods permits the early validation of engineering solutions for these smart energy control systems. However, since these solutions involve different engineering disciplines (e.g., software engineering, electrical engineering, etc.), different simulation tools might be employed. We propose a tool that interconnects EnergyPlus, one of the leading open-source tools for building energy simulation, with Crescendo, a tool for designing and modelling cyber-physical systems using formal methods. A preliminary evaluation suggests that the proposed solution enables the simulation between these two tools in an efficient manner.