Ensuring the functional safety of highly configurable systems often requires testing representative subsets of all possible configurations to reduce testing effort and save resources. The ratio of covered t-wise feature interactions (i.e., T-Wise Feature Interaction Coverage) is a common criterion for determining whether a subset of configurations is representative and capable of finding faults. Existing t-wise sampling algorithms uniformly cover t-wise feature interactions for all features, resulting in lengthy execution times and large sample sizes, particularly when large t-wise feature interactions are considered (i.e., high values of t). In this paper, we introduce a novel approach to t-wise feature interaction sampling, questioning the necessity of uniform coverage across all t-wise feature interactions, called \emph{\mulTiWise{}}. Our approach prioritizes between subsets of critical and non-critical features, considering higher t-values for subsets of critical features when generating a t-wise feature interaction sample. We evaluate our approach using subject systems from real-world applications, including \busybox{}, \soletta{}, \fiasco{}, and \uclibc{}. Our results show that sacrificing uniform t-wise feature interaction coverage between all features reduces the time needed to generate a sample and the resulting sample size. Hence, \mulTiWise{} Sampling offers an alternative to existing approaches if knowledge about feature criticality is available.

翻译：确保高度可配置系统的功能安全性通常需要测试所有可能配置的代表性子集，以降低测试工作量并节省资源。所覆盖的t维特征交互的比例（即T维特征交互覆盖率）是判断配置子集是否具有代表性且能够发现故障的常用标准。现有的t维采样算法对所有特征进行均匀的t维特征交互覆盖，导致执行时间过长且样本规模过大，特别是在考虑较大t维特征交互时（即t值较高）。本文提出一种新颖的t维特征交互采样方法，该方法对跨所有t维特征交互是否需要均匀覆盖提出质疑，称为\emph{\mulTiWise{}}。我们的方法在关键特征子集与非关键特征子集之间进行优先级划分，在生成t维特征交互样本时对关键特征子集考虑更高的t值。我们使用来自实际应用的受试系统（包括\busybox{}、\soletta{}、\fiasco{}和\uclibc{}）评估了所提出的方法。结果表明，牺牲所有特征间均匀的t维特征交互覆盖能够减少生成样本所需的时间及最终样本规模。因此，在可获得特征关键性知识的情况下，\mulTiWise{}采样为现有方法提供了一种替代方案。