Fuzzing generates surprising inputs at scale. For regex, this reveals both false positives/negatives and catastrophic performance paths your hand-written tests miss.

Define Invariants

Start by declaring what must always hold (for example round-trip parse/reformat validity). Fuzzing is most useful when invariants are machine-checkable.

Seed with Real-World Inputs

Combine synthetic generators with production-like examples. Real data shapes often expose bugs random strings will never trigger.

Store Failing Inputs as Regression Fixtures

Every fuzz-discovered failure should become a permanent test case. This prevents rediscovery and steadily hardens your regex library.

Fuzz Performance Too

Track execution time per generated input and fail when thresholds are exceeded. Correctness-only fuzzing misses ReDoS vulnerabilities.