Feature Subset Selection for Learning Huge Configuration Spaces: The case of Linux Kernel Size

International audienceLinux kernels are used in a wide variety of appliances, many of them having strong requirements on the kernel size due to constraints such as limited memory or instant boot. With more than nine thousands of configuration options to choose from, developers and users of Linux actually spend significant effort to document, understand, and eventually tune (combinations of) options for meeting a kernel size. In this paper, we describe a large-scale endeavour automating this task and predicting a given Linux kernel binary size out of unmeasured configurations. We first experiment that state-of-theart solutions specifically made for configurable systems such as performance-influence models cannot cope with that number of options, suggesting that software product line techniques may need to be adapted to such huge configuration spaces. We then show that tree-based feature selection can learn a model achieving low prediction errors over a reduced set of options. The resulting model, trained on 95 854 kernel configurations, is fast to compute, simple to interpret and even outperforms the accuracy of learning without feature selection