Optimizing Model-Based Software Product Line Testing with Graph Transformations

Software Product Lines (SPLs) are increasing in relevance and importance as various domains strive to cope with the challenges of supporting a high degree of variability in modern software systems. Especially the systematic testing of SPLs is non-trivial as a high degree of variability implies a vast number of possible products. As testing every valid product individually quickly becomes infeasible, heuristics are often used to choose a representative subset of products to be tested. MoSo-PoLiTe (Model-Based Software Product Line Testing) is a framework for SPL testing that combines and applies combinatorial (in particular pairwise) and model-based testing to SPL feature models. In this paper, we (1) present MoSo-PoLiTe as a novel case study for graph transformations in general and Story Driven Modelling (SDM) in particular, (2) show why we consider SDMs to be ideal for rapid prototyping optimization strategies in this context, and (3) evaluate our implemented optimizations and quantify the realized improvements for MoSo-PoLiTe.