article_kapova.bib

@article{burger2014b,
  affiliation = {Karlsruhe Institute of Technology},
  author = {Erik Burger and J{\"o}rg Hen{\ss} and Martin K{\"u}ster and Steffen Kruse and Lucia Happe},
  doi = {10.1007/s10270-014-0413-5},
  editor = {Robert France and Bernhard Rumpe},
  issn = {1619-1374},
  journal = {Software \& Systems Modeling},
  pages = {472--496},
  pdf = {https://sdqweb.ipd.kit.edu/publications/pdfs/burger2014b.pdf},
  publisher = {Springer Berlin / Heidelberg},
  title = {{View-Based Model-Driven Software Development with ModelJoin}},
  volume = {15},
  number = {2},
  year = {2014},
  tags = {Vitruv}
}

@article{gouvea2012a,
  abstract = {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 task 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 and software reliability results based on the evaluation of the system architecture.},
  author = {Daniel Dominguez Gouv\^ea and Cyro Muniz and Gilson Pinto and Alberto Avritzer and Rosa Maria Meri {Le\~{a}o} and Edmundo de Souza e Silva and Morganna Carmem Diniz and Luca Berardinelli and Julius C. B. Leite and Daniel {Moss\'e} and Yuanfang Cai and Michael Dalton and Lucia Happe and Anne Koziolek},
  doi = {10.1007/s10270-012-0264-x},
  issn = {1619-1366},
  journal = {Journal of Software and Systems Modeling},
  keywords = {Performance; Reliability; Adaptability},
  note = {Special Issue on Performance Modeling},
  pages = {1--23},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/gouvea2012a.pdf},
  publisher = {Springer-Verlag},
  title = {Experience with Model-based Performance, Reliability and Adaptability Assessment of a Complex Industrial Architecture},
  year = {2012},
  tags = {peer-reviewed}
}

@article{happe_lucia2013a,
  author = {Happe, Lucia and Buhnova, Barbora and Reussner, Ralf},
  doi = {10.1007/s10270-013-0336-6},
  issn = {1619-1366},
  journal = {Software \& Systems Modeling},
  keywords = {Stateful components; Performance prediction; Prediction accuracy},
  pages = {1319--1343},
  publisher = {Springer-Verlag},
  title = {Stateful component-based performance models},
  url = {http://dx.doi.org/10.1007/s10270-013-0336-6},
  year = {2013},
  volume = {13},
  number = {4},
  pages = {1319--1343},
  abstract = {The accuracy of performance-prediction models is crucial for widespread adoption of performance prediction in industry. One of the essential accuracy-influencing aspects of software systems is the dependence of system behaviour on a configuration, context or history related state of the system, typically reflected with a (persistent) system attribute. Even in the domain of component-based software engineering, the presence of state-reflecting attributes (the so-called internal states) is a natural ingredient of the systems, implying the existence of stateful services, stateful components and stateful systems as such. Currently, there is no consensus on the definition or method to include state-related information in component-based prediction models. Besides the task to identify and localise different types of stateful information across component-based software architecture, the issue is to balance the expressiveness and complexity of prediction models via an effective abstraction of state modelling. In this paper, we identify and classify stateful information in component-based software systems, study the performance impact of the individual state categories, and discuss the costs of their modelling in terms of the increased model size. The observations are formulated into a set of heuristics-guiding software engineers in state modelling. Finally, practical effect of state modelling on software performance is evaluated on a real-world case study, the SPECjms2007 Benchmark. The observed deviation of measurements and predictions was significantly decreased by more precise models of stateful dependencies.}
}

@article{koziolek2016a,
  title = {Assessing survivability to support power grid investment decisions},
  journal = {Reliability Engineering & System Safety},
  volume = {155},
  number = {},
  pages = {30 - 43},
  year = {2016},
  note = {},
  issn = {0951-8320},
  doi = {http://dx.doi.org/10.1016/j.ress.2016.05.015},
  url = {http://www.sciencedirect.com/science/article/pii/S095183201630076X},
  author = {Anne Koziolek and Alberto Avritzer and Sindhu Suresh and Daniel S. Menasché and Morganna Diniz and Edmundo de Souza e Silva and Rosa M. Leão and Kishor Trivedi and Lucia Happe},
  keywords = {Survivability},
  keywords = {Survivability},
  keywords = {Survivability},
  keywords = {Survivability},
  keywords = {Survivability},
  abstract = {Abstract The reliability of power grids has been subject of study for the past few decades. Traditionally, detailed models are used to assess how the system behaves after failures. Such models, based on power flow analysis and detailed simulations, yield accurate characterizations of the system under study. However, they fall short on scalability. In this paper, we propose an efficient and scalable approach to assess the survivability of power systems. Our approach takes into account the phased-recovery of the system after a failure occurs. The proposed phased-recovery model yields metrics such as the expected accumulated energy not supplied between failure and full recovery. Leveraging the predictive power of the model, we use it as part of an optimization framework to assist in investment decisions. Given a budget and an initial circuit to be upgraded, we propose heuristics to sample the solution space in a principled way accounting for survivability-related metrics. We have evaluated the feasibility of this approach by applying it to the design of a benchmark distribution automation circuit. Our empirical results indicate that the combination of survivability and power flow analysis can provide meaningful investment decision support for power systems engineers.},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/koziolek2016a.pdf},
  tags = {peer-reviewed}
}

@article{menasche2014a,
  abstract = {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.},
  author = {Menasch\'{e}, Daniel Sadoc and Avritzer, Alberto and Suresh, Sindhu and Le\~{a}o, Rosa M. and de Souza e Silva, Edmundo and Diniz, Morganna and Trivedi, Kishor and Happe, Lucia and Koziolek, Anne},
  doi = {10.1002/cpe.3241},
  issn = {1532-0634},
  journal = {Concurrency and Computation: Practice and Experience},
  keywords = {survivability, transient analysis, smart grid, fault tolerance, demand response, reliability metrics, FDIR},
  number = {12},
  pages = {1949--1974},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/menasche2014a.pdf},
  title = {Assessing survivability of smart grid distribution network designs accounting for multiple failures},
  url = {http://dx.doi.org/10.1002/cpe.3241},
  volume = {26},
  year = {2014},
  tags = {peer-reviewed}
}

@article{happe2020a,
  title = {Effective measures to foster girls' interest in secondary computer science education},
  author = {Happe, Lucia and Buhnova, Barbora and Koziolek, Anne and Wagner, Ingo},
  journal = {Education and Information Technologies},
  pages = {1--19},
  isbn = {1573-7608},
  year = {2020},
  month = {November},
  volume = {1},
  number = {1},
  url = {https://rdcu.be/ceUOT},
  doi = {10.1007/s10639-020-10379-x},
  publisher = {Springer},
  abstract = {The interest of girls in computing drops early during primary and secondary education, with minimal recovery in later education stages. In combination with the growing shortage of qualified computer science personnel, this is becoming a major issue, and also a target of numerous studies that examine measures, interventions, and strategies to boost girls' commitment to computing. Yet, the results of existing studies are difficult to navigate, and hence are being very rarely employed in classrooms. In this paper, we summarize the existing body of knowledge on the effective interventions to recruit and retain girls in computer science education, intending to equip educators with a comprehensive and easy-to-navigate map of interventions recommended in the existing literature. To this end, we perform an aggregated umbrella literature review of 11 existing reviews on the topic, together accumulating joined knowledge from over 800 publications, and formulate the findings in a map of 22 concrete interventions structured in six groups according to their phase and purpose.},
  tags = {peer-reviewed}
}