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.
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