To avoid design-related performance problems, model-driven performance prediction methods analyse the response times, throughputs, and resource utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions.
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