Research into Practice – Reality and Gaps: 6th International by Jeffrey N. Magee (auth.), George T. Heineman, Jan Kofron,

By Jeffrey N. Magee (auth.), George T. Heineman, Jan Kofron, Frantisek Plasil (eds.)

This e-book constitutes the completely refereed court cases of the sixth foreign convention, QoSA 2010, held in Prague, Czech Republic in June 2010. The eleven revised lengthy papers have been chosen from 32 submissions and are prepared in topical sections on Model-Driven research, caliber of provider Adaption in addition to Case reports and event studies.

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Additional info for Research into Practice – Reality and Gaps: 6th International Conference on the Quality of Software Architectures, QoSA 2010, Prague, Czech Republic, June 23 - 25, 2010. Proceedings

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We extend the work presented in [6] with the propagation of the usage profile throughout a component-based software architecture, as well as the availability of the underlying hardware resources. We use the Palladio Component Model (PCM) [7] as a design-oriented modelling language for component-based software architectures, and extend the PCM with capabilities for reliability prediction. Besides the inclusion of multiple influence factors to component reliability, our approach bears the advantage of providing a modelling language closely aligned with software architecture concepts (instead of Markov chains, which are then generated automatically).

In: WOSP, pp. 17–24 (2002) 3. : Model-Based Performance Prediction in Software Development: A Survey. IEEE Trans. on Software Engineering, 295–310 (2004) 4. : Creating effective load models for performance testing with incomplete empirical data. In: WSE, pp. 51–59 (2004) 5. : An overview of genetic algorithms: Part 1, fundamentals (1993) 6. : The Palladio component model for model-driven performance prediction. Journal of Systems and Software, 3–22 (2009) Statistical Inference of Software Performance Models 35 7.

An extensive discussion on the challenges of statistical inference for software performance models can be found in [12]. 6 Conclusions In this paper, we proposed statistical inferencing of performance models based on measurements only. Our approach focuses on the observable data and does not consider the structure of the system. 3. The performance metrics considered include the delivery time of messages, throughput, and utilization of different resources. We used MARS as well as genetic optimization to infer their dependency on message size and arrival rates.

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