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Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework,CICADA, for Controller Instability-preventing Configuration Aware Drone Adaptation.CICADAdynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype ofCICADAusing a popular open source sUAS flight control software and experimented with a large number of different configurations in simulation. We then performed a case study with physical drones to determine if our framework will work in practice. Experimental results show thatCICADA’sadaptations reduce controller instability and enable the sUAS to recover from up to 33.8% of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes. These safety-focused adaptations can mitigate unsafe behavior in 52.9% to 64.7% of dangerous configurations. We further show that rule-based approaches can be leveraged to automatically select an appropriate adaptation strategy based on the severity of instability encountered, with up to a 14.2% improvement over direct adaptation. Finally, we introduce a variation of our primary adaptation strategy designed to allow more cautious adaptation with limited configuration information, which gets within 6.7% of our primary adaptation strategy despite not requiring an optimal knowledge base. 

Users of highly-configurable software systems often want to optimize a particular objective such as improving a functional outcome or increasing system performance. One approach is to use an evolutionary algorithm. However, many applications today are data-driven, meaning they depend on inputs or data which can be complex and varied. Hence, a search needs to be run (and re-run) for all inputs, making optimization a heavy-weight and potentially impractical process. In this paper, we explore this issue on a data-driven highly-configurable scientific application. We build an exhaustive database containing 3,000 configurations and 10,000 inputs, leading to almost 100 million records as our oracle, and then run a genetic algorithm individually on each of the 10,000 inputs. We ask if (1) a genetic algorithm can find configurations to improve functional objectives; (2) whether patterns of best configurations over all input data emerge; and (3) if we can we use sampling to approximate the results. We find that the original (default) configuration is best only 34% of the time, while clear patterns emerge of other best configurations. Out of 3,000 possible configurations, only 112 distinct configurations achieve the optimal result at least once across all 10,000 inputs, suggesting the potential for lighter weight optimization approaches. We show that sampling of the input data finds similar patterns at a lower cost.