论文标题
宽敞的配置空间中的懒产品发现
Lazy Product Discovery in Huge Configuration Spaces
论文作者
论文摘要
高度配置的软件系统可以在不同子系统之间具有数千种相互依存的配置选项。在生成的配置空间中，发现某些选定选项的有效产品配置可能是复杂的，并且容易发生错误。可以使用特征模型来组织配置空间，并分散成反映每个子系统的配置选项的较小相互依赖的特征模型。 我们提出了一种具有相互依存特征的大型碎片特征模型中懒产品发现的方法。我们正式化了该方法，并证明了它的健全性和完整性。评估探讨了工业大小的配置空间。结果表明，与标准产品发现相比，懒惰的产品发现具有显着的性能优势，与我们的方法相比，这需要所有片段来分析特征模型。此外，该方法在更高效的基于启发式的发动机时会成功，无法找到有效的配置。
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.
