An Empirical Investigation of the Effort of Creating Reusable, Component-Based Models for Performance Prediction

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Executive Summary

Model-based performance prediction methods aim at evaluating the expected response time, throughput, and resource utilisation of a software system at design time, before implementation. Existing performance prediction methods use monolithic, throw-away prediction models or component-based, reusable prediction models. While it is intuitively clear that the development of reusable models requires more effort, the actual higher amount of effort has not been quantified or analyzed systematically yet. To study the effort, the authors conducted a controlled experiment with 19 computer science students who predicted the performance of two example systems applying an established, monolithic method (Software Performance Engineering) as well as their own component-based method (Palladio).

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