Knowledge about the roles developers play in a software project is crucial to understanding the project's collaborative dynamics. Developers are often classified according to the dichotomy of core and peripheral roles. Typically, operationalizations based on simple counts of developer activities (e.g., number of commits) are used for this purpose, but there is concern regarding their validity and ability to elicit meaningful insights. To shed light on this issue, we investigate whether commonly used operationalizations of coreperipheral roles produce consistent results, and we validate them with respect to developers' perceptions by surveying 166 developers. Improving over the state of the art, we propose a relational perspective on developer roles, using developer networks to model the organizational structure, and by examining core-peripheral roles in terms of developers' positions and stability within the organizational structure. In a study of 10 substantial open-source projects, we found that the existing and our proposed core-peripheral operationalizations are largely consistent and valid. Furthermore, we demonstrate that a relational perspective can reveal further meaningful insights, such as that core developers exhibit high positional stability, upper positions in the hierarchy, and high levels of coordination with other core developers.

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.

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Feature annotations (e.g., code fragments guarded by #ifdef C-preprocessor directives) control code extensions related to features. Feature annotations have long been said to be undesirable. When maintaining features that control many annotations, there is a high risk of ripple effects. Also, excessive use of feature annotations leads to code clutter, hinder program comprehension and harden maintenance.To prevent such problems, developers should monitor the use of feature annotations, for example, by setting acceptable thresholds. Interestingly, little is known about how to extract thresholds in practice, and which values are representative for feature-related metrics. To address this issue, we analyze the statistical distribution of three feature-related metrics collected from a corpus of 20 well-known and longlived C-preprocessor-based systems from different domains. We consider three metrics: scattering degree of feature conCommunicated by Prof. stants, tangling degree of feature expressions, and nesting depth of preprocessor annotations. Our findings show that feature scattering is highly skewed; in 14 systems (70 %), the scattering distributions match a power law, making averages and standard deviations unreliable limits. Regarding tangling and nesting, the values tend to follow a uniform distribution; although outliers exist, they have little impact on the mean, suggesting that central statistics measures are reliable thresholds for tangling and nesting. Following our findings, we then propose thresholds from our benchmark data, as a basis for further investigations.

Analyzing software product lines is difficult, due to their inherent variability. In the past, several strategies for product-line analysis have been proposed, in particular, product-based, feature-based, and family-based strategies. Despite recent attempts to conceptually and empirically compare different strategies, there is no work that empirically compares all of the three strategies in a controlled setting. We close this gap by extending a compiler for feature-oriented programming with support for product-based, feature-based, and family-based type checking. We present and discuss the results of a comparative performance evaluation that we conducted on a set of 12 feature-oriented, JAVA-based product lines. Most notably, we found that the family-based strategy is superior for all subject product lines: it is substantially faster, it detects all kinds of errors, and provides the most detailed information about them.