Software is becoming more and more part of our daily life and makes it easier, e.g., in the areas of communication and infrastructure. Model-driven software development forms the basis for the development of software through the use and combination of different models, which serve as central artifacts in the software development process. In this respect, model-driven software development comprises the process from requirement analysis through design to software implementation. This set of models with their relationships to each other forms a so-called megamodel. Due to the overlapping of the models, inconsistencies occur between the models, which must be removed. Therefore, round-trip engineering is a mechanism for synchronizing models and is the foundation for ensuring consistency between models. Most of the current approaches in this area, however, work with outdated batch-oriented transformation mechanisms, which no longer meet the requirements of more complex, long-living, and ever-changing software. In addition, the creation of megamodels is time-consuming and complex, and they represent unmanageable constructs for a single user. The aim of this thesis is to create a megamodel by means of easy-to-learn mechanisms and to achieve its consistency by removing redundancy on the one hand and by incrementally managing consistency relationships on the other hand. In addition, views must be created on the parts of the megamodel to extract them across internal model boundaries. To achieve these goals, the role concept of Kühn in 2014 is used in the context of model-driven software development, which was developed in the Research Training Group 'Role-based Software Infrastructures for continuous-context-sensitive Systems.' A contribution of this work is a role-based single underlying model approach, which enables the generation of views on heterogeneous models. Besides, an approach for the synchronization of different models has been developed, which enables the role-based single underlying model approach to be extended by new models. The combination of these two approaches creates a runtime-adaptive megamodel approach that can be used in model-driven software development. The resulting approaches will be evaluated based on an example from the literature, which covers all areas of the work. In addition, the model synchronization approach will be evaluated in connection with the Transformation Tool Contest Case from 2019.

We recently showed that incentive motivation improves the precision of the Approximate Number System (ANS) in young adults. To shed light on the development of incentive motivation, the present study investigated whether this effect and its underlying mechanisms may also be observed in younger samples. Specifically, seven-year-old children (n = 23; 12 girls) and 14-year-old adolescents (n = 30; 15 girls) performed a dot comparison task with monetary reward incentives. Both age groups showed higher accuracy in a reward compared to a neutral condition and, similarly, higher processing efficiency as revealed by the drift rate parameter of the EZ-diffusion model. Furthermore, in line with the Incentive Salience Hypothesis, phasic pupil dilations—indicating the activation of the brain’s salience network—were greater in incentivized trials in both age groups. Together these finding suggest that incentive modulation improves numerosity discrimination in children and adolescents by enhancing the perceptual saliency of numerosity information. However, the observed reward anticipation effects were less pronounced in children relative to adolescents. Furthermore, unlike previous findings regarding young adults, the decision thresholds of children and adolescents were not raised by the monetary reward, which may indicate a more protracted development of incentive regulation of response caution than perceptual evidence accumulation.