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Validating model-driven performance pbkp_redictions on random software systems

Abstract:

Software performance pbkp_rediction methods are typically validated by taking an appropriate software system, performing both performance pbkp_redictions and performance measurements for that system, and comparing the results. The validation includes manual actions, which makes it feasible only for a small number of systems. To significantly increase the number of systems on which software performance pbkp_rediction methods can be validated, and thus improve the validation, we propose an approach where the systems are generated together with their models and the validation runs without manual intervention. The approach is described in detail and initial results demonstrating both its benefits and its issues are presented. © 2010 Springer-Verlag.