%0 Conference Paper %1 martens2008a %A Martens, Anne %A Becker, Steffen %A Koziolek, Heiko %A Reussner, Ralf %B Proceedings of the 11th International Symposium on Component-Based Software Engineering (CBSE'08), Karlsruhe, Germany %D 2008 %I Springer-Verlag Berlin Heidelberg %K %P 16--31 %R 10.1007/978-3-540-87412-6_3 %T An Empirical Investigation of the Effort of Creating Reusable Models for Performance Prediction %V 5282 %X 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 analysed systematically yet. To study the effort, we 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 our own component-based method (Palladio). The results show that the effort of model creation with Palladio is approximately 1.25 times higher than with SPE in our experimental setting, with the resulting models having comparable prediction accuracy. Therefore, in some cases, the creation of reusable prediction models can already be justified, if they are reused at least once.

@inproceedings{martens2008a, abstract = {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 analysed systematically yet. To study the effort, we 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 our own component-based method (Palladio). The results show that the effort of model creation with Palladio is approximately 1.25 times higher than with SPE in our experimental setting, with the resulting models having comparable prediction accuracy. Therefore, in some cases, the creation of reusable prediction models can already be justified, if they are reused at least once.}, added-at = {2017-08-08T09:12:33.000+0200}, author = {Martens, Anne and Becker, Steffen and Koziolek, Heiko and Reussner, Ralf}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22a19e20265cd32e77c31966c9cd2648f/snowball}, booktitle = {Proceedings of the 11th International Symposium on Component-Based Software Engineering (CBSE'08), Karlsruhe, Germany}, doi = {10.1007/978-3-540-87412-6_3}, editors = {M.R.V. Chaudron and C. Szyperski}, interhash = {aea03005cd68d20289769941e4a788c6}, intrahash = {2a19e20265cd32e77c31966c9cd2648f}, keywords = {}, pages = {16--31}, pdf = {http://sdqweb.ipd.uka.de/publications/pdfs/martens2008a.pdf}, publisher = {Springer-Verlag Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2018-02-15T08:08:34.000+0100}, title = {An Empirical Investigation of the Effort of Creating Reusable Models for Performance Prediction}, volume = 5282, year = 2008 }