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

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Bibliographic Details
Title: MulTi-Wise Sampling: Trading Uniform T-Wise Feature Interaction Coverage for Smaller Samples
Authors: Tobias Pett, Sebastian Krieter, Thomas Thüm, Ina Schaefer
Source: 28th ACM International Systems and Software Product Line Conference. :47-53
Publication Status: Preprint
Publisher Information: ACM, 2024.
Publication Year: 2024
Subject Terms: ddc:004, Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Machine Learning, DATA processing & computer science, D.2, Machine Learning (cs.LG)
Description: 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.
Document Type: Article
DOI: 10.1145/3646548.3672589
DOI: 10.48550/arxiv.2406.19801
Access URL: http://arxiv.org/abs/2406.19801
Rights: CC BY
URL: https://www.acm.org/publications/policies/copyright_policy#Background
Accession Number: edsair.doi.dedup.....1ba6d0736221f7143f0ae2ed0d1321c1
Database: OpenAIRE
Description
Abstract: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.
DOI:10.1145/3646548.3672589