Greybox fuzzing is used extensively in research and practice. There are umpteen improvements proposed in the literature to 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 in terms of code coverage and bug finding capability. Such an evaluation is insufficient to understand the effect of improvements on the internals of fuzzer. Some of the literature developed tools to visualize the outcomes of the 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 towards the development of a fuzzing-specific visualization framework: a taxonomy of visualization analysis tasks that fuzzing experts desire to help them understand the internals of fuzzing. Our approach involves conducting semi-structured 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 visualization tools for fuzzing through the lens of our taxonomy. In our pilot study, we conducted interviews with six fuzzing experts and extracted a preliminary taxonomy. We aim to conduct another 20 interviews to gain more insights and make the taxonomy more robust at Phase 2.