# MulTi-Wise Sampling: Trading Uniform T-Wise Feature Interaction Coverage for Smaller Samples

arXiv: https://arxiv.org/abs/2406.19801 · HTML: https://mlsanity.com/paper/2406.19801/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 59 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [A Comparison of 10 Sampling Algorithms for Configurable Systems](https://mlsanity.com/paper/1602.02052/) (2016)
- [Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack](https://mlsanity.com/paper/1710.07980/) (2017)

## Abstract (arXiv)

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 emphmulTiWise. 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.