Files in the corpora will be used as default inputs in subsequent runs in the fuzzer. The fuzzer will then "mutate" or modify these inputs using various algorithms to create new yet similar inputs. This is a way to get more value from fuzzing as we'll be able to test using inputs similar to ones that have been problematic in the past and therefore more likely to find bugs.

Another benefit is that we will be able to detect regressions in the codebase by simply running our known corpora against the fuzzer and making sure the code doesn't crash.

Finally, the corpora make for good building blocks for unit tests as they represent known error cases that the code has had at some point.

Example

e.g. scenario: while testing ZkBinary's decode() function, you find that an empty input causes a panic.

Identify your fuzz target (cargo fuzz list or whatever you used for cargo fuzz run TARGET
Examine the fuzzing artifacts: ls artifacts/TARGET/
cat the file and check that it matches the error message from the fuzzer. The filename's prefix will match the kind of error encountered: oom (out-of-memory), crash, etc.
Choose a NAME for the crash file, e.g. corpus-crash-emptyfile
cp artifacts/TARGET/CRASH-FILE corpus/TARGET/NAME

Then add the new corpus/TARGET/NAME file to git.