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Variability modeling, and in particular feature modeling, is a central element of model-driven software product line architectures. Such architectures often emerge from legacy code, but, unfortunately creating feature models from large, legacy systems is a long and arduous task. We address the problem of automatic synthesis of feature models from propositional constraints. We show that this problem is NP-hard. We design efficient techniques for synthesis of models from respectively CNF and DNFdoi:10.1145/2362536.2362553 dblp:conf/splc/AndersenCSW12 fatcat:etzk75i2gravjo3mhzqgirli2q