Greybox fuzzing is used extensively in research and practice. There are umpteen publications that improve greybox fuzzing. However, to what extent do these improvements affect the internal components or internals of a given fuzzer is not yet understood as the improvements are mostly evaluated using code coverage and bug finding capability. Such an evaluation is insufficient to understand the effect of improvements on the fuzzer internals. Some of the literature visualizes the outcomes of fuzzing to enhance the understanding. However, they only focus on high-level information, and no previous research on visualization has been dedicated to understanding fuzzing internals. To close this gap, we propose the first step toward development of a fuzzing-specific visualization framework: a taxonomy of visualization analysis tasks that fuzzing experts desire to help them understand the fuzzing internals. Our approach involves conducting interviews with fuzzing experts and using qualitative data analysis to systematically extract the task taxonomy from the interview data. We also evaluate the support of existing fuzzing visualization tools through the lens of our taxonomy. In our study, we have conducted 33 interviews with fuzzing practitioners and extracted a taxonomy of 120 visualization analysis tasks. Our evaluation shows that the existing fuzzing visualization tools only provide aids to support 10 of them.

Greybox fuzzing is one of the fuzzing techniques that has been extensively researched and used in practice. Plenty of publications propose improvements to greybox fuzzing. However, the extent to which these improvements really work and generalize is not yet understood: our preliminary study of the recent literature in grey-box fuzzing shows that most papers evaluate their fuzzers in terms of runtime code coverage or bug-finding capability, although the improvements made are to the internal components (or internals) of the fuzzer. Results drawn from such experiments are insufficient to judge the impact the changes in the fuzzer's internals have on its performance.