一句话：提出可视化手段来揭示灰盒模糊测试内部组件的行为，以弥补仅靠覆盖率或漏洞发现能力评估改进效果的不足。
灰盒模糊测试已被广泛研究并实用化，大量论文针对模糊测试器的内部组件提出改进。然而，现有评估大多只报告运行时代码覆盖率或漏洞发现能力，而改进实际发生在模糊测试器内部组件上，因此这类实验结果不足以判断内部改动对模糊测试器性能的真实影响。本文通过初步文献调研指出这一评估缺口，并提出对模糊测试器内部进行可视化与理解的方法，旨在让研究者与开发者能够观察和分析内部组件的行为，从而更准确地评估改进是否真正有效、是否具有泛化性。
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.