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Runtime monitoring is essential for ensuring the safe operation and enabling self-adaptive behavior of Cyber-Physical Systems (CPS). It requires the creation of system monitors, instrumentation for data collection, and the definition of constraints. All of these aspects need to evolve to accommodate changes in the system. However, most existing approaches lack support for the automated generation and setup of monitors and constraints for diverse technologies and do not provide adequate support for evolving the monitoring infrastructure. Without this support, constraints and monitors can become stale and become less effective in long-running, rapidly changing CPS. In this ``new and emerging results'' paper we propose a novel framework for model-integrated runtime monitoring. We combine model-driven techniques and runtime monitoring to automatically generate large parts of the monitoring framework and to reduce the maintenance effort necessary when parts of the monitored system change. We build a prototype and evaluate our approach against a system for controlling the flights of unmanned aerial vehicles. 

Unmanned aerial vehicles (UAVs) are becoming increasingly pervasive in everyday life, supporting diverse use cases such as aerial photography, delivery of goods, or disaster reconnaissance and management. UAVs are cyber-physical systems (CPS): they integrate computation (embedded software and control systems) with physical components (the UAVs flying in the physical world). UAVs in particular and CPS in general require monitoring capabilities to detect and possibly mitigate erroneous and safety-critical behavior at runtime. Existing monitoring approaches mostly do not adequately address UAV CPS characteristics such as the high number of dynamically instantiated components, the tight integration of elements, and the massive amounts of data that need to be processed. In this paper we report results of a case study on monitoring in UAVs. We discuss CPS-specific monitoring challenges and present a prototype we implemented by extending REMINDS, a framework for software monitoring so far mainly used in the domain of metallurgical plants. Additionally, we demonstrate the applicability and scalability of our approach by monitoring a real control and management system for UAVs in simulations with up to 30 drones flying in an urban area. 

Traditionally, safety-critical projects have been developed using the waterfall process. However, this makes it costly and challenging to incrementally introduce new features and to certify the modified product for use. As a result, there has been increasing interest in adopting agile development paradigms within the safety-critical domain. This in turn introduces numerous challenges. In this paper we address the specific problems of discovering, analyzing, specifying, and managing safety requirements within the agile Scrum process. We propose SafetyScrum, a methodology that augments the Scrum life-cycle with incrementally applied safety-related activities and introduces the notion of ``safety debt'' for incrementally tracking the current safety status of a project. We demonstrate the viability of SafetyScrum for managing safety stories in an agile development environment by applying it to a project in which our existing Unmanned Aerial Vehicle system is enhanced to support a River-Rescue scenario. 

Research in the area of Cyber-Physical Systems (CPS) is hampered by the lack of available project environments in which to explore open challenges and to propose and rigorously evaluate solutions. In this “New Ideas and Emerging Results” paper we introduce a CPS research incubator – based upon a system, and its associated project environment, for managing and coordinating the flight of small Unmanned Aerial Systems (sUAS). The research incubator provides a new community resource, making available diverse, high-quality project artifacts produced across multiple releases of a safety-critical CPS. It enables researchers to experiment with their own novel solutions within a fully-executable runtime environ- ment that supports both high-fidelity sUAS simulations as well as physical sUAS. Early collaborators from the software engineering community have shown broad and enthusiastic support for the project and its role as a research incubator, and have indicated their intention to leverage the environment to address their own research areas of goal modeling, runtime adaptation, safety-assurance, and software evolution.