(Semantic) Feature Model Differences with (Q)SAT

Feature models evolve in multiple iterations over time. When modellers change a model, they enact syntactical changes in order to produce specific semantic differences between model iterations. Many tools have been developed to analyze such syntactical differences, but the changing semantics of models were harder to assess. Tools for semantic differences between feature model iterations rely on Binary Decision Diagrams (BDDs) or encode each change into SAT, the former leading to BDD scaling issues and the latter requiring editor support or other specialized tooling. We contribute the first concise formalization of feature models and their semantic differences into propositional logic and use it to efficiently and scalably classify semantic differences using SAT solvers. We then extend our definition into QSAT in order to quantify the full list of semantic differences between feature models and enumerate them using QBF tools, without needing specialized feature model solvers. We implement a semantic difference classifier using our UVL processing pipeline based on Booleguru (instead of the more widely used FeatureIDE) and evaluate it on industrial feature model instances in the standardized UVL format. We also evaluate our QSAT-based semantic difference enumerator and reproduce prior results. We provide all software and evaluation results in an artifact.