%0 Conference Paper %1 WalterHoKo2017-DecisionSupport %A Walter, Jürgen %A van Hoorn, Andre %A Kounev, Samuel %B Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools %D 2017 %K Analytical_and_simulation-based_analysis DECLARE DML DQL Design_of_software_and_systems Meta-models Performance QPME Self-aware-computing Tool descartes t_full %T Automated and Adaptable Decision Support for Software Performance Engineering %X Software performance engineering (SPE) provides a plethora of methods and tooling for measuring, modeling, and evaluating performance properties of software systems. The solution approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. While approaches allow for interchangeability, the choice of an appropriate approach and tooling to solve a given performance concern still relies on expert knowledge. Currently, there is no automated and extensible approach for decision support. In this paper, we present a methodology for the automated selection of performance engineering approaches tailored to user concerns. We decouple the complexity of selecting an SPE approach for a given scenario providing a decision engine and solution approach capability models. This separation allows to easily append additional solution approaches and rating criteria. We demonstrate the applicability by presenting decision engines that compare measurement- and model-based analysis approaches.

@inproceedings{WalterHoKo2017-DecisionSupport, abstract = {Software performance engineering (SPE) provides a plethora of methods and tooling for measuring, modeling, and evaluating performance properties of software systems. The solution approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. While approaches allow for interchangeability, the choice of an appropriate approach and tooling to solve a given performance concern still relies on expert knowledge. Currently, there is no automated and extensible approach for decision support. In this paper, we present a methodology for the automated selection of performance engineering approaches tailored to user concerns. We decouple the complexity of selecting an SPE approach for a given scenario providing a decision engine and solution approach capability models. This separation allows to easily append additional solution approaches and rating criteria. We demonstrate the applicability by presenting decision engines that compare measurement- and model-based analysis approaches.}, added-at = {2020-04-05T23:16:26.000+0200}, author = {Walter, J{\"u}rgen and van Hoorn, Andre and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/25dd912d7ea4bab07fe4516144be62bd8/se-group}, booktitle = {Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools}, interhash = {245d8ef1e5953977f8c15c92074915e2}, intrahash = {5dd912d7ea4bab07fe4516144be62bd8}, keywords = {Analytical_and_simulation-based_analysis DECLARE DML DQL Design_of_software_and_systems Meta-models Performance QPME Self-aware-computing Tool descartes t_full}, month = {12}, timestamp = {2020-10-06T14:17:27.000+0200}, title = {{Automated and Adaptable Decision Support for Software Performance Engineering}}, year = 2017 }