T-Wise Sampling Operations on Binary Decision Diagrams (Bachelor’s Thesis Summary)

Testing configurable systems is challenging, as faults often arise from interactions between multiple features. Therefore, quality assurance requires to test configurations including such interactions. 𝑇-Wise sampling techniques generate configuration sets to cover each valid interaction among 𝑡 features in at least one configuration. However, state-of-the-art techniques fail to scale to large systems. Furthermore, the variety of sampling techniques results in a variety of samples, raising the question of which sample to use for testing. This thesis proposes leveraging binary decision diagrams (BDDs) to improve sampling methods as well as a fast to compute metric for comparing the quality of samples. Moreover, this thesis presents the first exact 2-wise interaction counting algorithm that successfully scales to the infamous system automotive02_v4.