  Performance-influence models can help stakeholders understand how and where
configuration options and their interactions influence the performance of a
system. With this understanding, stakeholders can debug performance behavior
and make deliberate configuration decisions. Current black-box techniques to
build such models combine various sampling and learning strategies, resulting
in tradeoffs between measurement effort, accuracy, and interpretability. We
present Comprex, a white-box approach to build performance-influence models for
configurable systems, combining insights of local measurements, dynamic taint
analysis to track options in the implementation, compositionality, and
compression of the configuration space, without relying on machine learning to
extrapolate incomplete samples. Our evaluation on 4 widely-used, open-source
projects demonstrates that Comprex builds similarly accurate
performance-influence models to the most accurate and expensive black-box
approach, but at a reduced cost and with additional benefits from interpretable
and local models.
