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.

Recent research has studied the importance and identified causes of nondeterminism in software. Static analysis tools exhibit many risk factors for nondeterministic behavior, but no work has analyzed the occurrence of such behavior in these tools. To bridge this gap, we perform an extensive empirical study aiming to understand past and ongoing nondeterminism in 12 popular, open-source static analysis tools that target 5 types of projects. We first conduct a qualitative study to understand the extent to which nondeterministic behavior has been found and addressed within the tools under study, and find results in 7 tool repositories. After classifying the issues and commits by root cause, we find that the majority of nondeterminisms are caused by concurrency issues, incorrect analysis logic, or assumed orderings of unordered data structures, which have shared patterns. We also perform a quantitative analysis, where we use two strategies and diverse input programs and configurations to detect yet-unknown nondeterministic behaviors. We discover such behavior in 8 out of the 12 tools, including 3 which had no results from the qualitative analysis. We find that nondeterminism often appears in multiple configurations on a variety of input programs. We communicated all identified nondeterminism to the developers, and received confirmation of five tools. Finally, we detail a case study of fixing FlowDroid's nondeterministic behavior.

Static analyses are powerful tools that can serve as a complement to dynamic approaches such as testing. In order to ensure generality, many static analysis tools are configurable. However, these configurations can make testing and debugging more difficult. To address this issue, we introduce a new tool, ECSTATIC, which leverages partial order relations between analysis configuration options to automatically test and debug static analyzers, even without ground truths. ECSTATIC’s results are reproducible by virtue of running within Docker containers, and ECSTATIC provides clear extension interfaces for users to add their own tools and input programs. We evaluated ECSTATIC on four popular dataflow analysis tools, and found 74 bugs in all four tools. We also found that ECSTATIC’s novel two-staged delta debugging was able to reduce real-world programs by 50%, compared to a baseline of 6%.