Most greybox fuzzing tools are coverage-guided as code coverage is strongly
correlated with bug coverage. However, since most covered codes may not contain
bugs, blindly extending code coverage is less efficient, especially for corner
cases. Unlike coverage-guided greybox fuzzers who extend code coverage in an
undirected manner, a directed greybox fuzzer spends most of its time allocation
on reaching specific targets (e.g., the bug-prone zone) without wasting
resources stressing unrelated parts. Thus, directed greybox fuzzing (DGF) is
particularly suitable for scenarios such as patch testing, bug reproduction,
and specialist bug hunting. This paper studies DGF from a broader view, which
takes into account not only the location-directed type that targets specific
code parts, but also the behaviour-directed type that aims to expose abnormal
program behaviours. Herein, the first in-depth study of DGF is made based on
the investigation of 32 state-of-the-art fuzzers (78% were published after
2019) that are closely related to DGF. A thorough assessment of the collected
tools is conducted so as to systemise recent progress in this field. Finally,
it summarises the challenges and provides perspectives for future research.

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