The use of semi-autonomous Unmanned Aerial Vehicles (UAV) to support emergency response scenarios, such as fire surveillance and search and rescue, offers the potential for huge societal benefits. However, designing an effective solution in this complex domain represents a "wicked design" problem, requiring a careful balance between trade-offs associated with drone autonomy versus human control, mission functionality versus safety, and the diverse needs of different stakeholders. This paper focuses on designing for situational awareness (SA) using a scenario-driven, participatory design process. We developed SA cards describing six common design-problems, known as SA demons, and three new demons of importance to our domain. We then used these SA cards to equip domain experts with SA knowledge so that they could more fully engage in the design process. We designed a potentially reusable solution for achieving SA in multi-stakeholder, multi-UAV, emergency response applications.
The full behavior of complex software systems often only emerges during operation. They thus need to be monitored at run time to check that they adhere to their requirements. Diverse runtime monitoring approaches have been developed in various domains and for different purposes. Their sheer number and heterogeneity, however, make it hard to find the right approach for a specific application or purpose. The aim of our research therefore was to develop a comparison framework for runtime monitoring approaches. Our framework is based on an analysis of the literature and existing taxonomies for monitoring languages and patterns. We use examples from existing monitoring approaches to explain the framework. We demonstrate its usefulness by applying it to 32 existing approaches and by comparing 3 selected approaches in the light of different monitoring scenarios. We also discuss perspectives for researchers
The complexity of product line variability models makes it hard to maintain their consistency over time regardless of the modeling approach used. Engineers thus need support for detecting and resolving inconsistencies. We describe experiences of applying a tool-supported approach for incremental consistency checking on variability models. Our approach significantly improves the overall performance and scalability compared to batch-oriented techniques and allows providing immediate feedback to modelers. It is extensible as new consistency constraints can easily be added. Furthermore, the approach is flexible as it is not limited to variability models and it also checks the consistency of the models with the underlying code base of the product line. We report the results of a thorough evaluation based on real-world product line models and discuss lessons learned.
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