Efficient Product-Line Testing Using Cluster-Based Product Prioritization

Abstract

A software product-line comprises a set of products that share a common set of features. These features can be reused to customize a product to satisfy specific needs of certain customers or markets. As the number of possible products increases exponentially for new features, testing all products is infeasible. Existing testing approaches reduce their effort by restricting the number of products (sampling) and improve their effectiveness by considering the order of tests (prioritization). In this paper, we propose a cluster-based prioritization technique to sample similar products with respect to the feature selection. We evaluate our approach using feature models of different sizes and show that cluster-based prioritization can enhance the effectiveness of product-line testing.