Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This not only hinders system understanding, but also complicates tracing the influence of configuration options to individual methods.

We propose a white-box approach that models configuration-dependent performance at the method level. This allows us to predict effects of configuration decisions for individual methods, supporting system understanding and performance debugging. It consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration- and performance-sensitive causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of nine real-world Java software systems, we demonstrate that our approach can identify configuration-relevant methods and learn accurate performance-influence models.