Highly-configurable software systems can have thousands of interdependent configuration options across different subsystems. In the resulting configuration space, discovering a valid product configuration for some selected options can be complex and error prone. The configuration space can be organized using a feature model, fragmented into smaller interdependent feature models reflecting the configuration options of each subsystem. We propose a method for lazy product discovery in large fragmented feature models with interdependent features. We formalize the method and prove its soundness and completeness. The evaluation explores an industrial-size configuration space. The results show that lazy product discovery has significant performance benefits compared to standard product discovery, which in contrast to our method requires all fragments to be composed to analyze the feature model. Furthermore, the method succeeds when more efficient, heuristics-based engines fail to find a valid configuration.

中文翻译：

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巨大配置空间中的惰性产品发现

高度可配置的软件系统可以在不同的子系统之间拥有数千个相互依赖的配置选项。在生成的配置空间中，为某些选定选项发现有效的产品配置可能很复杂且容易出错。可以使用特征模型组织配置空间，将其分解为更小的相互依赖的特征模型，以反映每个子系统的配置选项。我们提出了一种在具有相互依赖特征的大型碎片特征模型中进行惰性产品发现的方法。我们将方法形式化并证明其合理性和完整性。该评估探索了工业规模的配置空间。结果表明，与标准产品发现相比，惰性产品发现具有显着的性能优势，与我们的方法相反，它需要组合所有片段来分析特征模型。此外，当更高效、基于启发式的引擎无法找到有效配置时，该方法会成功。