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Many modern software systems are highly configurable, allowing the user totune them for performance and more. Current performance modeling approaches aimat finding performance-optimal configurations by building performance models ina black-box manner. While these models provide accurate estimates, they cannotpinpoint causes of observed performance behavior to specific code regions. Thisdoes not only hinder system understanding, but it also complicates tracing theinfluence of configuration options to individual methods.  We propose a white-box approach that models configuration-dependentperformance behavior at the method level. This allows us to predict theinfluence of configuration decisions on individual methods, supporting systemunderstanding and performance debugging. The approach consists of two steps:First, we use a coarse-grained profiler and learn performance-influence modelsfor all methods, potentially identifying some methods that are highlyconfiguration- and performance-sensitive, causing inaccurate predictions.Second, we re-measure these methods with a fine-grained profiler and learn moreaccurate models, at higher cost, though. By means of 9 real-world Java softwaresystems, we demonstrate that our approach can efficiently identifyconfiguration-relevant methods and learn accurate performance-influence models.

White-Box Performance-Influence Models: A Profiling and Learning Approach