On the relation between internal and external feature interactions in feature-oriented product lines

Kolesnikov, Sergiy S.; Roth, Judith; Apel, Sven

doi:10.1145/2660190.2660191

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Even if the variability model is often used for the configuration of code artifacts [8], there are very few metrics available that include the complexity of the variability model when measuring code artifacts [10]. More precisely, we know only one study providing an evaluation for such a measure [21]. Further, we do not know any comparisons of variability-aware code metrics with traditional metrics for single system metrics.…”

Section: Related Workmentioning

confidence: 99%

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

El-Sharkawy,

Krafczyk,

Schmid

2021

Preprint

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Context: Software metrics, as one form of static analyses, is a commonly used approach in software engineering in order to understand the state of a software system, in particular to identify potential areas prone to defects. Family-based techniques extract variability information from code artifacts in Software Product Lines (SPLs) to perform static analysis for all available variants. Many different types of metrics with numerous variants have been defined in literature. When counting all metrics including such variants, easily thousands of metrics can be defined. Computing all of them for large product lines can be an extremely expensive process in terms of performance and resource consumption.Objective: We address these performance and resource challenges while supporting customizable metric suites, which allow running both, single system and variability-aware code metrics.Method: In this paper, we introduce a partial parsing approach used for the efficient measurement of more than 42,000 code metric variations. The approach covers variability information and restricts parsing to the relevant parts of the Abstract Syntax Tree (AST).Conclusions: This partial parsing approach is designed to cover all relevant information to compute a broad variety of variability-aware code metrics on code artifacts containing annotation-based variability, e.g., realized with C-preprocessor statements. It allows for the flexible combination of single system and variability-aware metrics, which is not supported by existing tools. This is achieved by a novel representation of partially parsed product line code artifacts, which is tailored to the computation of the metrics. Our approach consumes considerably less resources, especially when computing many metric variants in parallel. CCS Concepts• General and reference → Metrics; • Software and its engineering → Software product lines; Automated static analysis.

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Section: Related Workmentioning

confidence: 99%