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Fuzzing has become one of the most popular techniques to identify bugs insoftware. To improve the fuzzing process, a plethora of techniques haverecently appeared in academic literature. However, evaluating and comparingthese techniques is challenging as fuzzers depend on randomness when generatingtest inputs. Commonly, existing evaluations only partially follow bestpractices for fuzzing evaluations. We argue that the reason for this aretwofold. First, it is unclear if the proposed guidelines are necessary due tothe lack of comprehensive empirical data in the case of fuzz testing. Second,there does not yet exist a framework that integrates statistical evaluationtechniques to enable fair comparison of fuzzers. To address these limitations,we introduce a novel fuzzing evaluation framework called SENF (StatisticalEvaluatioN of Fuzzers). We demonstrate the practical applicability of ourframework by utilizing the most wide-spread fuzzer AFL as our baseline fuzzerand exploring the impact of different evaluation parameters (e.g., the numberof repetitions or run-time), compilers, seeds, and fuzzing strategies. Usingour evaluation framework, we show that supposedly small changes of theparameters can have a major influence on the measured performance of a fuzzer.

My Fuzzer Beats Them All! Developing a Framework for Fair Evaluation and Comparison of Fuzzers