Efficient Model Learning of Software Product Lines

Model learning aims to construct behavioral models of black-box software, enabling various types of analysis, such as model checking and model-based testing. In the context of Software Product Lines (SPLs), the shared features among products present an opportunity to reuse previously learned models, reducing the overall cost of model learning.

In this talk, we first provide an overview of active model learning algorithms, with a focus on the well-known L* algorithm. We then introduce an adaptive approach to SPL model learning that leverages observations from previously learned products to improve efficiency when learning new ones. Finally, we briefly discuss recent advances in the field.