property-based-testing

Use the Hypothesis library with the @given decorator and strategies from hypothesis.strategies to generate inputs. Define properties like roundtrip (decode(encode(x)) == x) or idempotence (f(f(x)) == f(x)) rather than asserting specific example outputs.

Example-based tests check specific hand-picked inputs and outputs, while property-based tests verify general properties across hundreds of generated inputs. Property-based testing excels at finding edge cases in serialization, parsing, and normalization code that examples typically miss.

The choice depends on your language: Hypothesis for Python, fast-check for JavaScript/TypeScript, proptest for Rust, rapid for Go, and jqwik for Java. All provide input generation and shrinking to minimal counterexamples.

Yes, Echidna and Medusa are property-based fuzzers for EVM contracts that test state invariants. You define functions like echidna_balance_invariant that must always return true, and the fuzzer attempts to violate them.

Avoid PBT for simple CRUD operations without transformation logic, UI code, integration tests with heavy external setup, and code with unavoidable side effects like network calls. It works best for pure functions and unit-level testing.

Vacuous tests usually come from contradictory or overly narrow assume() calls that filter out nearly all inputs. Tautological tests happen when the assertion reimplements the function logic, such as assert add(a, b) == a + b; use algebraic properties like commutativity instead.