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Reusing d-DNNFs for Efficient Feature-Model Counting
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AI
DOI：10.1145/3680465.png)
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摘要
En 中文
Feature models are commonly used to specify valid configurations of a product line. In industry, feature models are often complex due to numerous features and constraints. Thus, a multitude of automated analyses have been proposed. Many of those rely on computing the number of valid configurations, which typically depends on solving a #SAT problem, a computationally expensive operation. Even worse, most counting-based analyses require evaluation for multiple features or partial configurations resulting in numerous #SAT computations on the same feature model. Instead of repetitive computations on highly similar formulas, we aim to improve the performance by reusing knowledge between these computations. In this work, we are the first to propose reusing d-DNNFs for performing repetitive counting queries on features and partial configurations. In our experiments, reusing d-DNNFs saved up-to similar to 99.98% compared to repetitive invocations of #SAT solvers even when including compilation times. Overall, our tool ddnnife combined with the d-DNNF compiler d4 appears to be the most promising option when dealing with many repetitive feature-model counting queries.
Keyword:
d-DNNF
feature model
product line
knowledge compilation
model counting
#SAT
期刊
A
IF:
6.2
论文数:
1.2K
被引数:
3.4K
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