  Context: Changing a software application with many build-time configuration
settings may introduce unexpected side-effects. For example, a change intended
to be specific to a platform (e.g., Windows) or product configuration (e.g.,
community editions) might impact other platforms or configurations. Moreover, a
change intended to apply to a set of platforms or configurations may be
unintentionally limited to a subset. Indeed, understanding the exposure of
source code changes is an important risk mitigation step in change-based
development approaches. Objective: In this experiment, we seek to evaluate
DiPiDi, a prototype implementation of our approach to assess the exposure of
source code changes by statically analyzing build specifications. We focus our
evaluation on the effectiveness and efficiency of developers when assessing the
exposure of source code changes. Method: We will measure the effectiveness and
efficiency of developers when performing five tasks in which they must identify
the deliverable(s) and conditions under which a change will propagate. We will
assign participants into three groups: without explicit tool support, supported
by existing impact analysis tools, and supported by DiPiDi.
