  Stakeholders of configurable systems are often interested in knowing how
configuration options influence the performance of a system to facilitate, for
example, the debugging and optimization processes of these systems. Several
black-box approaches can be used to obtain this information, but they either
sample a large number of configurations to make accurate predictions or miss
important performance-influencing interactions when sampling few
configurations. Furthermore, black-box approaches cannot pinpoint the parts of
a system that are responsible for performance differences among configurations.
This article proposes ConfigCrusher, a white-box performance analysis that
inspects the implementation of a system to guide the performance analysis,
exploiting several insights of configurable systems in the process.
ConfigCrusher employs a static data-flow analysis to identify how configuration
options may influence control-flow statements and instruments code regions,
corresponding to these statements, to dynamically analyze the influence of
configuration options on the regions' performance. Our evaluation on 10
configurable systems shows the feasibility of our white-box approach to more
efficiently build performance-influence models that are similar to or more
accurate than current state of the art approaches. Overall, we showcase the
benefits of white-box performance analyses and their potential to outperform
black-box approaches and provide additional information for analyzing
configurable systems.

Software Engineering