A feature commonality-based search strategy to find high $$t$$ -wise covering solutions in feature models

Abstract

t-wise coverage is one of the most important techniques used to test configurations of software for finding bugs. It ensures that interactions between features of a Software Product Line (SPL) are tested. The size of SPLs (of thousands of features) makes the problem of finding a good test suite computationally expensive, as the number of t-wise combinations grows exponentially. In this article, we leverage Constraint Programming’s search strategies to generate test suites with a high coverage of configurations. We analyse the behaviour of the default random search strategy, and then we propose an improvement based on the commonalities (frequency) of the features. We experimentally compare to uniform sampling and state of the art sampling approaches. We show that our new search strategy outperforms all the other approaches and has the fastest running time.