Previous work from some of the authors has shown Evolutionary Algorithms
(EAs) as an attractive alternative to synthesize FMs that are hard to analyze. In this paper, we explore the feasibility of using EAs to reverse engineer
FMs from the feature sets that describe the system variants and thus help coping
with the evolution scenario _ from system variants to SPLs - described above.
Our study analyzed 59 representative feature sets from publicly available case
studies of different sizes and complexity. For the implementation of our approach,
we used a specific instantiation of the evolutionary algorithm ETHOM,
integrated into the open source tool BeTTy