Schmid, LarissaHerrmann, Andrea2025-01-082025-01-0820240720-8928https://dl.gi.de/handle/20.500.12116/45536Modern software is configurable and allows users to set many parameters according to their needs. Due to many non-functional parameters, usually, the same functionality can be achieved with varying performance. Performance models express application performance as functions of input parameters, helping users and developers understand application behavior. Automatic performance modeling approaches can generate performance models automatically from empirical measurements of the software. Current modeling approaches employ heuristics for deciding which configurations to measure, resulting in a trade-off between the cost of measurements and accuracy of the model. To overcome this trade-off, we propose approaches to derive the smallest necessary measurement setup based on results of a system analysis, and to automatically identify performance-irrelevant options. Our evaluation with real-world applications show that we can significantly decrease cost of performance modeling while maintaining accuracy of the resulting models.enperformanceperformance modelmeasurementconfigurationText/Conference Paper