A Practical, Principled Measure of Fuzzer Appeal: A Preliminary Study
Miroslav Gavrilov, Kyle T. Dewey, Alex Groce, Davina J. Zamanzadeh, Ben Hardekopf · 2020
Fuzzers are important bug-finding tools in both academia and industry. To ensure scientific progress, we need a metric for fuzzer comparison. Bug-based metrics are impractical because (1) the definition of "bug" is vague, and (2) mapping bug-revealing inputs to bugs requires extensive domain knowledge.In this paper, we propose an automated method for comparing fuzzers that alleviates these problems. We replace the question "What bugs can this fuzzer find?" with "What changes in program behavior over time can this fuzzer detect?". Intuitively, fuzzers which find more behavioral changes are likely to find more bugs. However, unlike bugs, behavioral changes are well-defined and readily detectable. Our evaluation, executed on three targets with several fuzzers, shows that our method is consistent with bug-based metrics, but without associated difficulties. While further evaluation is needed to establish superiority, our results show that our method warrants further investigation.