Towards a Modelling Framework with Temporal and Uncertain Data for Adaptive Systems
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Self-Adaptive Systems (SAS) optimise their behaviours or configurations at runtime in response to a modification of their environments or their behaviours. These systems therefore need a deep understanding of the ongoing situation which enables reasoning tasks for adaptation operations. Using the model-driven engineering (MDE) methodology, one can abstract this situation. However, information concerning the system is not always known with absolute confidence. Moreover, in such systems, the monitoring frequency may differ from the delay for reconfiguration actions to have measurable effects.
These characteristics come with a global challenge for software engineers: how to represent uncertain knowledge that can be efficiently queried and to represent ongoing actions in order to improve adaptation processes? To tackle this challenge, this thesis defends the need for a unified modelling framework which includes, besides all traditional elements, temporal and uncertainty as first-class concepts. Therefore, a developer will be able to abstract information related to the adaptation process, the environment as well as the system itself.
Towards this vision, we present two evaluated contributions: a temporal context model and a language for uncertain data. The temporal context model allows abstracting past, ongoing and future actions with their impacts and context. The language, named Ain?tea, integrates data uncertainty as a first-class citizen.