  Fuzzing is one of the fastest growing fields in software testing. The idea
behind fuzzing is to check the behavior of software against a large number of
randomly generated inputs, trying to cover all interesting parts of the input
space, while observing the tested software for anomalous behaviour. One of the
biggest challenges facing fuzzer users is how to validate software behavior,
and how to improve the quality of oracles used. While mutation analysis is the
premier technique for evaluating the quality of software test oracles, mutation
score is rarely used as a metric for evaluating fuzzer quality. Unless mutation
analysis researchers can solve multiple problems that make applying mutation
analysis to fuzzing challenging, mutation analysis may be permanently sidelined
in one of the most important areas of testing and security research. This paper
attempts to understand the main challenges in applying mutation analysis for
evaluating fuzzers, so that researchers can focus on solving these challenges.
