Knowledge evolution in autonomic software product lines
Proceedings of the 15th International Software Product Line Conference, Volume 2
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This paper discusses on-going work in adaptive architecturesconcerning automatic adaptation rule derivation.Adaptation is rule-action based but deriving rules thatmeet the adaptation goals are tedious and error prone. Wepresent an approach that uses model-driven derivation andtraining for automatically deriving adaptation rules, andexemplify this in an environment for scientific computing.