Andrea Leitner

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Model-Based Engineering (MBE) aims at increasing the effectiveness of engineering by using models as key artifacts in the development process. While empirical studies on the use and the effects of MBE in industry exist, there is only little work targeting the embedded systems domain. We contribute to the body of knowledge with a study on the use and the(More)
High development and maintenance costs and a high error rate are the major problems in the development of automation systems, which are mainly caused by bad communication and inefficient reuse methods. To overcome these problems, we propose a more systematic reuse approach. Though systematic reuse approaches such as software product lines are appealing,(More)
The concepts of Software Product Line Engineering (SPLE) have been adapted and applied to enterprise IT systems, in particular the ERP systems of a production company. Based on a 2-layer feature model for the domain of the company's business processes, individual, albeit similar division's ERP system configurations can be derived by feature selection(More)
Model-based engineering (MBE) aims at increasing the effectiveness of engineering by using models as important artifacts in the development process. While empirical studies on the use and the effects of MBE in industry exist, only few of them target the embedded systems domain. We contribute to the body of knowledge with an empirical study on the use and(More)
Model-Based Engineering (MBE) aims at increasing the e↵ectiveness of engineering by using models as key artifacts in the development process. While empirical studies on the use and the e↵ects of MBE in industry generally exist, there is only little work targeting the embedded systems domain. We contribute to the body of knowledge with a study on the use and(More)
Co-simulation is a powerful approach to verify a system design and to support concept decisions early in the automotive development process. Due to the heterogeneous nature of the co-simulation framework there is a lot of potential for variability requiring the systematic handling of it. We identified two main scenarios for variability management(More)