The configuration spaces of modern software systems are often too large to test exhaustively. Combinatorial interaction testing approaches (CIT), such as covering arrays, systematically sample the configuration space and test only the selected configurations. Traditional t-way covering arrays aim to cover all t-way combinations of option settings in a minimum number of configurations. By doing so, they assume that the testing cost of a configuration is the same for all configurations. In this work, we however argue that, in practice, the actual testing cost may differ from one configuration to another and that accounting for these differences can improve the cost-effectiveness of covering arrays. In this work, we first introduce a novel combinatorial object, called a cost-aware covering array. A t-way cost-aware covering array is a t-way covering array that minimizes a given cost function. We then provide a framework for defining the cost function. Finally, we present an algorithm to compute cost-aware covering arrays for a simple, yet important scenario, and empirically evaluate the cost-effectiveness of the proposed approach. The results of our empirical studies suggest that cost-aware covering arrays, depending on the configuration space model used, can greatly reduce the actual cost of testing compared to traditional covering arrays
