Over the last decade, fuzzing has been increasingly gaining
traction due to its effectiveness in finding
bugs. Nevertheless, fuzzer evaluations have been challenging
during this time, mainly due to lack of standardized
benchmarking. Aiming to alleviate this issue, in 2020, Google
released FuzzBench, an open-source benchmarking platform, that
is widely used for accurate fuzzer benchmarking.

However, a typical FuzzBench experiment takes CPU years to
run. If we additionally consider that fuzzers under active
development evaluate any changes empirically, benchmarking
becomes prohibitive both in terms of computational resources
and time. In this paper, we propose GreenBench, a greener
benchmarking platform that, compared to FuzzBench,
significantly speeds up fuzzer evaluations while maintaining
very high accuracy.

In contrast to FuzzBench, GreenBench drastically increases the
number of benchmarks while drastically decreasing the duration
of fuzzing campaigns. As a result, the fuzzer rankings
generated by GreenBench are almost as accurate as those by
FuzzBench (with very high correlation), but GreenBench is from
18 to 61 times faster. We discuss the implications of these
findings for the fuzzing community.