Assessing the exposure of software changes

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. In this paper, we present DiPiDi, a new approach to assess the exposure of source code changes under different build-time configuration settings by statically analyzing build specifications. To evaluate our approach, we produce a prototype implementation of DiPiDi for the CMake build system. We measure the effectiveness and efficiency of developers when performing five tasks in which they must identify the deliverable(s) and conditions under which a source code change will propagate. We assign participants into three groups: without explicit tool support, supported by existing impact analysis tools, and supported by DiPiDi. While our study does not have the statistical power to make generalized quantitative claims, we manually analyze the full distribution of our study’s results and show that DiPiDi results in a net benefit for its users. Through our experimental evaluation, we show that DiPiDi results in a 36 average percentage points improvement in F 1 -score when identifying impacted deliverables and a reduction of 0.62 units of distance when ranking impacted patches. Furthermore, DiPiDi results in a 42% average task time reduction for our participants when compared to a competing impact analysis approach. DiPiDi’s improvements to both effectiveness and efficiency are especially prevalent in complex programs with many compile-time configurations.