Automated inference of goal-oriented performance prediction functions

Understanding the dependency between performance metrics (such as response time) and software configuration or usage parameters is crucial in improving software quality. However, the size of most modern systems makes it nearly impossible to provide a complete performance model. Hence, we focus on scenario-specific problems where software engineers require practical and efficient approaches to draw conclusions, and we propose an automated, measurement-based model inference method to derive goal-oriented performance prediction functions. For the practicability of the approach it is essential to derive functional dependencies with the least possible amount of data. In this paper, we present different strategies for automated improvement of the prediction model through an adaptive selection of new measurement points based on the accuracy of the prediction model. In order to derive the prediction models, we apply and compare different statistical methods. Finally, we evaluate the different combinations based on case studies using SAP and SPEC benchmarks.