YASA: yet another sampling algorithm

Configurable systems allow users to derive customized software variants with behavior and functionalities tailored to individual needs. Developers of these configurable systems need to ensure that each configured software variant works as intended. Thus, software testing becomes highly relevant, but also highly expensive due to large configuration spaces that grow exponentially in the number of features. To this end, sampling techniques, such as t-wise interaction sampling, are used to generate a small yet representative subset of configurations, which can be tested even with a limited amount of resources. However, even state-of-the-art t-wise interaction sampling techniques do not scale well for systems with large configuration spaces. In this paper, we introduce the configurable technique YASA that aims to be more efficient than other existing techniques and enables control over trading-off sampling time and sample size. The general algorithm of YASA is based on the existing technique IPOG, but introduces several improvements and options to adapt the sampling procedure to a given configurable system. We evaluate our approach in terms of sampling time and sample size by comparing it to existing t-wise interaction sampling techniques. We find that YASA performs well even for large-scale system and is also able to produce smaller samples than existing techniques.