Before making important decisions about an ongoing fuzzing campaign, we believe an engineer may want to know: (i) the achieved level of confidence about the program's correctness (residual risk), (ii) the expected increase in confidence about the program’s correctness if we invest more time for the current campaign (cost-benefit trade-off), and (iii) the total number of bugs that the fuzzer can find in the limit (effectiveness). The ability to accurately estimate the above quantities through observed data of the fuzzing campaign allows engineers to make required decisions with quantifiable accuracy. Currently, there are popular data-driven approaches to provide such quantitative guidance on decision making for white- and blackbox fuzzing campaigns. However, none of these prevailing techniques can guarantee unbiased estimation of residual risk, cost-benefit trade-off, or effectiveness for greybox fuzzing – the most popular automated software vulnerability discovery technique to date. Greybox fuzzers introduce an adaptive bias to existing estimators that needs to be corrected during the quantitative analysis to make accurate decisions about the campaign.

In this thesis, our primary objective is to develop a rich statistical framework that supports quantitative decision-making for greybox fuzzing campaigns. We leverage this framework to introduce appropriate bias correction strategies to existing estimators and propose novel estimators that account for adaptive bias in greybox fuzzing.