Feature Model Differences
Mathieu Acher, Patrick Heymans, Mathieu Acher, Patrick Heymans, Et Al, Mathieu Acher, Patrick Heymans, Clément Quinton · 2012
Abstract. Feature models are a widespread means to represent com-monality and variability in software product lines. As is the case for other kinds of models, computing and managing feature model differences is useful in various real-world situations. In this paper, we propose a set of novel differencing techniques that combine syntactic and semantic mech-anisms, and automatically produce meaningful differences. Practitioners can exploit our results in various ways: to understand, manipulate, vi-sualize and reason about differences. They can also combine them with existing feature model composition and decomposition operators. The proposed automations rely on satisfiability algorithms. They come with a dedicated language and a comprehensive environment. We illustrate and evaluate the practical usage of our techniques through a case study dealing with a configurable component framework.