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Context: Software metrics, as one form of static analyses, is a commonly usedapproach in software engineering in order to understand the state of a softwaresystem, in particular to identify potential areas prone to defects.Family-based techniques extract variability information from code artifacts inSoftware Product Lines (SPLs) to perform static analysis for all availablevariants. Many different types of metrics with numerous variants have beendefined in literature. When counting all metrics including such variants,easily thousands of metrics can be defined. Computing all of them for largeproduct lines can be an extremely expensive process in terms of performance andresource consumption.  Objective: We address these performance and resource challenges whilesupporting customizable metric suites, which allow running both, single systemand variability-aware code metrics.  Method: In this paper, we introduce a partial parsing approach used for theefficient measurement of more than 42,000 code metric variations. The approachcovers variability information and restricts parsing to the relevant parts ofthe Abstract Syntax Tree (AST).  Conclusions: This partial parsing approach is designed to cover all relevantinformation to compute a broad variety of variability-aware code metrics oncode artifacts containing annotation-based variability, e.g., realized withC-preprocessor statements. It allows for the flexible combination of singlesystem and variability-aware metrics, which is not supported by existing tools.This is achieved by a novel representation of partially parsed product linecode artifacts, which is tailored to the computation of the metrics. Ourapproach consumes considerably less resources, especially when computing manymetric variants in parallel.

Fast Static Analyses of Software Product Lines -- An Example With More Than 42,000 Metrics