Compositional Feature-Model Analyses

Feature modeling is widely used to systematically model features of variant-rich software systems and their dependencies. By translating feature models into propositional formulas and analyzing them with solvers, a wide range of automated analyses across all phases of the software development process become possible. Most solvers only accept formulas in conjunctive normal form (CNF), so an additional transformation of feature models is often necessary.
In this project, we investigate whether this transformation has a noticeable impact on analyses and how to influence this impact positively. We raise awareness about CNF transformations for feature-model analysis and mitigate it as a threat to validity for research evaluations to ensure reproducibility and fair comparisons. Furthermore, we investigate other steps in the feature-model analysis process, their alternatives, and their interactions; for instance, we study the potential and impact of knowledge compilation, interfaces, slicing, and evolution on feature-model analyses.
Our vision for this project is to lay a foundation for a compositional feature-model analysis algebra; that is, to understand how complex analyses are made of simple parts, how they can be re-assembled, and how those parts interact with each other.