Product Sampling for Product Lines: The Scalability Challenge

Quality assurance for product lines is often infeasible for each product separately. Instead, only a subset of all products (i.e., a sample) is considered during testing such that at least the coverage of certain feature interactions is guaranteed. While pair-wise interaction sampling only covers all interactions between two features, its generalization to t-wise interaction sampling ensures coverage for all interactions among t features. However, sampling large product lines poses a challenge, as today's algorithms tend to run out of memory, do not terminate, or produce samples, which are too large to be tested. To initiate a community effort, we provide a set of large real-world feature models with up-to 19 thousand features, which are supposed to be sampled. The performance of sampling approaches is evaluated based on the CPU time and memory consumed to retrieve a sample, the sample size for a given coverage (i.e. the value of t) and whether the sample achieves full t-wise coverage. A well-performing sampling algorithm achieves full t-wise coverage, while minimizing the other properties as best as possible.