AutoSMP

Pett, Tobias; Thüm, Thomas; Lochau, Malte; Schaefer, Ina

doi:10.1145/3461002.3473073

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Second, we include three CDL 3 , eight KConfig 4 , and one automotive feature model published by Knüppel et al [17]. Third, our dataset contains a BusyBox 5 feature model, which was extracted by Pett et al [101]. Fourth, we consider a feature model from the financial service domain [102,103].…”

Section: Subject Systemsmentioning

confidence: 99%

On the benefits of knowledge compilation for feature-model analyses

Sundermann,

Kuiter,

Heß

et al. 2023

Ann Math Artif Intell

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Feature models are commonly used to specify the valid configurations of product lines. As industrial feature models are typically complex, researchers and practitioners employ various automated analyses to study the configuration spaces. Many of these automated analyses require that numerous complex computations are executed on the same feature model, for example by querying a SAT or SATsolver. With knowledge compilation, feature models can be compiled in a one-time effort to a target language that enables polynomial-time queries for otherwise more complex problems. In this work, we elaborate on the potential of employing knowledge compilation on feature models. First, we gather various feature-model analyses and study their computational complexity with regard to the underlying computational problem and the number of solver queries required for the respective analysis. Second, we collect knowledge-compilation target languages and map feature-model analyses to the languages that make the analysis tractable. Third, we empirically evaluate publicly available knowledge compilers to further inspect the potential benefits of knowledge-compilation target languages.

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Section: Subject Systemsmentioning

confidence: 99%

On the benefits of knowledge compilation for feature-model analyses

Sundermann,

Kuiter,

Heß

et al. 2023

Ann Math Artif Intell

Self Cite

View full text Add to dashboard Cite

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Uniform and scalable sampling of highly configurable systems

et al. 2022

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Many analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These analyses could instead use statistical inference, where a tractable sample accurately predicts results for the entire space. To do so, the laws of statistical inference requires each member of the population to be equally likely to be included in the sample, i.e., the sampling process needs to be “uniform”. SAT-samplers have been developed to generate uniform random samples at a reasonable computational cost. However, there is a lack of experimental validation over colossal spaces to show whether the samplers indeed produce uniform samples or not. This paper (i) proposes a new sampler named , (ii) presents a new statistical test to verify sampler uniformity, and (iii) reports the evaluation of and five other state-of-the-art samplers: , , , , and . Our experimental results show only satisfies both scalability and uniformity.

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