value-category-cheatsheet VS American Fuzzy Lop

Here is a great summary: https://github.com/jeaye/value-category-cheatsheet/blob/master/value-category-cheatsheet.pdf

There's some efforts to guide test generation for property based testing to make the instruction pointer explore as large a space as possible.

This effort is more mature in the fuzzing community. See eg American Fuzzy Lop https://github.com/google/AFL

What you're describing, just generating random input to test a program, is sometimes called "blind fuzzing" but the state-of-the-art is far beyond that. Maybe try reading through the documentation of e.g. https://github.com/google/AFL to see what a fuzzer does and why just producing random input isn't even scratching the surface.

for general riscv I used to use this https://github.com/google/AFL I dont know if it supports x64 tho.

Ex ( AFL, WinAFL, HonggFuzz, LibFuzzer, Jazzer )

I wrote this little fuzz test target in order to fuzz it with afl (under ASan and UBSan):

afl, which is trivial to apply to this program:

I made my own version of a TCL interpreter (well, a very TCL like langauge) derived from "picol" available at https://github.com/howerj/pickle. There are many different re-implementations and derivatives of this interpreter but they all seem very "crashy", this one has been significantly hardened by using a fuzzer on it which ran for months called American Fuzzy Lop https://lcamtuf.coredump.cx/afl/ . It is also more suitable for embedded use whilst still not having arbitrary restrictions like many other implementations.

On Linux afl is a very powerful bug-finding tool, and it's a great companion when doing code review. Composes well with ASan and UBSan.

b-, https://lcamtuf.coredump.cx/afl/