Fuzz-Testing or Fuzzing is a testing approach based on the semi-random provision of inputs for software systems under test. With specific adaptations, the election of inputs can be manipulated in order to optimize the testing performance. The field of fuzzing features many publications which introduce distinctive fuzzing algorithms optimized for specific products or with general advancements. A Software Product Line (SPL) describes a family of software products which share core properties but consist of diverse configurations and combinations of features. In this thesis, we introduce a process for the analysis and augmentation of variability models of SPLs which allows the product-specific optimization of fuzzing programs for these particular products. We apply the proposal to an examplary SPL and test a product instantiation to evaluate the effectiveness and efficiency of the adapted fuzzer compared to domain-independent fuzzers. Due to the fluctuating results of fuzzing, caused by the semi-randomness of input election, we perform numerous tests examining the minimum-, average- and median amounts of testing rounds, required to find different bugs and the relative rate of success of finding the bugs. The evalution shows that the test results confirm the increased performance of our novel approach in testing instantiations of the particular SPL, when compared to domain-independent approaches.