Validating Model-Driven Performance Predictions on Random Software Systems

Abstract. Software performance prediction methods are typically validated by taking an appropriate software system, performing both performance predictions 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 prediction 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. Key words: performance modeling, performance validation, MDD 1 Motivation State of the art in model-driven software performance prediction builds on three related factors: the availability of architectural and behavioral software models, the ability to solve performan...

• Type: Conference
• Year: 2010
• Where: QOSA
• Authors: Vlastimil Babka, Petr Tuma, Lubomír Bulej
• Keywords: Performance Models, Performance Prediction Methods, QOSA 2010, Software Engineering, Software Performance Prediction