Almost every sufficiently complex software system today is configurable. Conditional compilation is a simple variability-implementation mechanism that is widely used in open-source projects and industry. Especially, the C preprocessor (cpp) is very popular in practice, but it is also gaining (again) interest in academia. Although there have been several attempts to understand and improve cpp, there is a lack of understanding of how it is used in open-source and industrial systems and whether different usage patterns have emerged. The background is that much research on configurable systems and product lines concentrates on open-source systems, simply because they are available for study in the first place. This leads to the potentially problematic situation that it is unclear whether the results obtained from these studies are transferable to industrial systems. We aim at lowering this gap by compar- * This author published previous work as Janet Feigenspan. ing the use of cpp in open-source projects and industry-especially from the embedded-systems domain-based on a substantial set of subject systems and well-known variability metrics, including size, scattering, and tangling metrics. A key result of our empirical study is that, regarding almost all aspects we studied, the analyzed open-source systems and the considered embedded systems from industry are similar regarding most metrics, including systems that have been developed in industry and made open source at some point. So, our study indicates that, regarding cpp as variability-implementation mechanism, insights, methods, and tools developed based on studies of open-source systems are transferable to industrial systems-at least, with respect to the metrics we considered.

Fork-based development has been widely used both in open source communities and in industry, because it gives developers flexibility to modify their own fork without affecting others. Unfortunately, this mechanism has downsides: When the number of forks becomes large, it is difficult for developers to get or maintain an overview of activities in the forks. Current tools provide little help. We introduce Infox, an approach to automatically identify non-merged features in forks and to generate an overview of active forks in a project. The approach clusters cohesive code fragments using code and networkanalysis techniques and uses information-retrieval techniques to label clusters with keywords. The clustering is effective, with 90 % accuracy on a set of known features. In addition, a human-subject evaluation shows that Infox can provide actionable insight for developers of forks.

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