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.

特征模型通常用于指定产品线的有效配置。由于工业特征模型通常很复杂，研究人员和从业者采用各种自动化分析来研究配置空间。许多这些自动化分析需要在同一特征模型上执行大量复杂的计算，例如通过查询 SAT 或# SATsolver。通过知识编译，可以一次性将特征模型编译为目标语言，从而能够对其他更复杂的问题进行多项式时间查询。在这项工作中，我们详细阐述了在特征模型上使用知识编译的潜力。首先，我们收集各种特征模型分析，并根据底层计算问题和相应分析所需的求解器查询数量来研究它们的计算复杂性。其次，我们收集知识编译目标语言，并将特征模型分析映射到使分析易于处理的语言。第三，我们对公开的知识编译器进行实证评估，以进一步检查知识编译目标语言的潜在好处。