DRESS-ML
Lucas Vieira Alves, José Davi Pereira, Natália Aragão, Matheus Chagas, Paulo Henrique M. Maia · 2022
Drones are gaining attention due to its possibility to support wide different types of applications. Since they can operate in different environments, it is possible to encounter uncertainties and exceptional situations, not initially predicted, during the use of drone-based applications. In this realm, self-adaptive strategies have been successfully used to guarantee resilience and continuous execution of such applications despite environment changes. Although some modelling approaches emerged to represent drone concepts, they are limited to model only expected flight plans or include few environmental conditions and drone resources, which restrict considerably their use. To mitigate those problems, this work proposes a domain-specific language, called DRESS-ML, which allows modelling exceptional situations and self-adaptive behaviours for drone-based applications. It relies on the Given-When-Then template used in the Behaviour-driven development (BDD) technique and the some of the main Aspect-oriented Programming concepts. We validate the applicability of our language through a proof of concept regarding an example application that uses a drone to monitor a forest to search for fire spots.