The field of computer science (CS) is facing a crucial challenge in broadening participation and embracing diversity, especially among underrepresented gender groups. The presented interdisciplinary educational program is an efficient response to this challenge, designed to catalyze diversity in CS through engagement with complex, interest-driven problems. This paper outlines the program's structure, elucidates the pedagogical underpinnings, and reflects on the emergent challenges and opportunities. We delve into how the fusion of CS with other academic disciplines can allure a more varied demographic, emphasizing the engagement of female high school students-a demographic pivotally positioned yet significantly untapped in CS. Through a systematic survey analysis, we measure the program's efficacy in increasing interest in CS and in cultivating an appreciation for its application in addressing real-world, cross-disciplinary challenges. Our findings affirm the program's success in bridging the engagement gap by leveraging students' intrinsic interests, thus charting alternative pathways into the CS field. These insights underscore the critical role of interdisciplinary approaches, establishing anew standard for transformative CS educational methods.

This study explores the perspectives of high school computer science (CS) teachers on students' interest in the subject. Using structured interviews, we identified factors that may influence students' interest in CS, such as curriculum design, teaching methods, and the use of materials and technology in the classroom. The findings reveal the importance of making CS relevant and exciting to students to increase engagement and understanding and promote acceptance of the subject in society. Additionally, the study highlights the challenges and benefits of interdisciplinary teaching of CS and the value of pre-designed teaching materials in supporting this approach. The presented study provides valuable insights for educators and policymakers looking to promote and sustain students' interest in CS. Overall, the study emphasizes the crucial role of CS education (CSEd) in preparing students for success in a digital world.

In computer science education (CSEd) it is a well-known challenge to create learning environments in which everyone can experience equal opportunities to identify themselves with the subject, get involved, and feel engaged. Especially for underrepresented groups such as girls or not computer enthusiasts, CSEd seems to lack sufficient opportunities at its current state. In this paper, we present a novel approach of using interdisciplinary online courses in the context of bee mortality and discuss the possibilities of such courses to enhance diverse learning in CSEd. We report summarized findings from a one-year period, including 16 workshops where over 160 secondary school students (aged 10-16) have participated in our online courses. Pre-test-post-test surveys have been conducted to gain insights into students' perceptions and attitude changes. The results show the potential of such interdisciplinary approaches to spark interest in computer science (CS) and to raise positive feelings toward programming. Particularly striking are the results from differentiated analyses of students grouped by characteristics such as low initial self-efficacy, coding aversion, or less computer affinity. We found multiple significant effects of our courses to impact students of those groups positively. Our results clearly indicate the potential of interdisciplinary CSEd to address a more diverse audience, especially traditionally underrepresented groups.

Despite the crucial importance of metamodeling for Model-Driven Engineering (MDE), there is still little discussion about the quality of metamodel design and its consequences in model-driven development processes. Presumably, the quality of metamodel design strongly affects the models and transformations that conform to these metamodels. However, so far surprisingly few work has been done to validate the characterization of metamodel quality. A proper characterization is essential to automate quality improvements for metamodels such as metamodel refactorings. In this paper, we present an empirical study to sharpen the understanding of the perception of metamodel quality. In the study, 24 participants created metamodels of two different domains and evaluated the metamodels in a peer review process according to an evaluation sheet. The results show that the perceived quality was mainly driven by the metamodels completeness, correctness and modularity while other quality attributes could be neglected.

This paper provides a summary of the First International Workshop on Model Based Design for Cyber- Physical Systems (MB4CP 2015) in conjunction with DSN 2015 conference in Rio de Janeiro, Brazil.

This paper discusses the challenges and opportunities of Software Performance Engineering (SPE) research in smart-grid (SG) environments. We envision to use SPE techniques to optimise the quality of information and communications technology (ICT) applications, and thus optimise the quality of the overall SG. The overall process of Monitoring, Analysing, Planning, and Executing (MAPE) is discussed to highlight the current open issues of the domain and the expected benefits.

SUMMARY Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage, and intelligent features such as automated fault detection, isolation, and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index, accounting for the fact that after a failure, the energy demand and supply will vary over time during a multi‐step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation, and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In case demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. Under specific independence assumptions, we show how the proposed methodology can be adapted to account for multiple failures. We have evaluated the presented model using data from a real power distribution grid, and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network. Copyright © 2014 John Wiley & Sons, Ltd.

Performance problems pose a significant risk to software vendors. If left undetected, they can lead to lost customers, increased operational costs, and damaged reputation. Despite all efforts, software engineers cannot fully prevent performance problems being introduced into an application. Detecting and resolving such problems as early as possible with minimal effort is still an open challenge in software performance engineering. In this paper, we present a novel approach for Performance Problem Diagnostics (PPD) that systematically searches for well-known performance problems (also called performance antipatterns) within an application. PPD automatically isolates the problem's root cause, hence facilitating problem solving. We applied PPD to a well established transactional web e-Commerce benchmark (TPC-W) in two deployment scenarios. PPD automatically identified four performance problems in the benchmark implementation and its deployment environment. By fixing the problems, we increased the maximum throughput of the benchmark from 1800 requests per second to more than 3500.

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams, smart grids require a unified and holistic approach taking into consideration the interplay of distributed generation, distribution automation topology, intelligent features, and others. In this paper, we use transient survivability metrics to create better distribution automation network designs. Our approach combines survivability analysis and power flow analysis to assess the survivability of the distribution power grid network. Additionally, we present an initial approach to automatically optimize available investment decisions with respect to survivability and investment costs. We have evaluated the feasibility of this approach by applying it to the design of a real distribution automation circuit. Our empirical results indicate that the combination of survivability analysis and power flow can provide meaningful investment decision support for power systems engineers.

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response.

Engineering high quality software is a tough task. In order to know whether a certain quality attribute has been achieved or degraded, it has to be quantified by analysis or measured. However, determining what to quantify and how these quantities are related to each other is the difficult part. Early analysis of the quality attributes of a software system on the basis of the system's planned architecture allows informed decisions on design trade-offs. Such decisions can be later validated by measurements on the running system.

Typically, to provide accurate predictions, a performance model has to include low-level details such as used communication infrastructure, or connectors and influence of the underlying middleware platform. In order to profit from the research on inter-component communication and connector design, performance prediction approaches need to include models of different kinds of connectors. It is not always feasible to model complex connectors with all their details. The choice of suitable abstraction filter, which reduces the amount of detailed information needed with respect to the model purpose, is crucial to decrease modelling effort. We propose an approach by which an abstract connector model can be augmented with selected adaptations and enhancements using model completions to result in a more detailed connector model. As the purpose of our models is performance prediction, we designed a suitable abstraction filter based on the Pipes & Filters pattern to produce performance models of connectors. Thus, we need to characterize only a small set of compositional and reusable transformations. The selection of applied transformations is then based on the feature-oriented design of the connector's completion.

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of tasks execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system's architect. Finally, we have developed models of hardware and software reliability. We report on hardware reliability results based on the evaluation of the system architecture. As a topic for future work, we report on an approach that we recommend be applied to evaluate the system under study software reliability.

For successful and effective software development the ability to predict impact of design decisions in early development stages is crucial. Typically, to provide accurate predictions the models have to include low-level details such as used design patterns (e.g., concurrency design patterns) and underlying middleware platform. These details influence Quality of Service (QoS) metrics, thus are essential for accurate prediction of extra-functional properties such as performance and reliability. Existing approaches do not consider the relation of actual implementations and performance models used for prediction. Furthermore, they neglect the broad variety of implementations and middleware platforms, possible configurations, and varying usage scenarios. To allow more accurate performance predictions, we extend classical performance engineering by automated model refinements based on a library of reusable performance completions.