Web3 & DApp Development Fundamentals · Pelajaran

Fuzzing dan Invarian

Pengujian properti

Fuzzing dan Invarian adalah pelajaran Web3 & DApp Development Fundamentals gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Web3 & DApp Development Fundamentals, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Web3 & DApp Development Fundamentals mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

Beyond Hardcoded Inputs

Unit tests check specific inputs you thought of. But bugs often hide in inputs you did not think of. Property-based testing flips this: you state a property that should always hold, and the tool generates many random inputs trying to break it.

Foundry supports two flavors: fuzz tests and invariant tests.

Writing a Fuzz Test

A fuzz test is simply a test function with parameters. Foundry automatically calls it many times with randomized argument values, hunting for a counterexample.

function testFuzzDeposit(uint256 amount) public {
    vm.assume(amount > 0 && amount < 1e30);
    vault.deposit(amount);
    assertEq(vault.balanceOf(address(this)), amount);
}

Bounding Inputs

Random inputs may be unrealistic. Constrain them with:

vm.assume(cond) — discard runs that fail the condition
bound(x, min, max) — map any value into a range

Prefer bound for ranges since assume can waste runs by rejecting too many inputs.

function testFuzz(uint256 x) public {
    x = bound(x, 1, 1000); // always in [1, 1000]
    // ...
}

What Makes a Good Property

A good fuzz property is a statement that must hold for all valid inputs. Examples:

Depositing then withdrawing returns the same amount
Total supply never changes on a transfer
A user can never withdraw more than they deposited

Think in terms of universal truths, not specific values.

function testFuzzTransferConservesSupply(uint256 amt) public {
    uint256 supplyBefore = token.totalSupply();
    token.transfer(bob, bound(amt, 0, token.balanceOf(address(this))));
    assertEq(token.totalSupply(), supplyBefore);
}

Reading Fuzz Output

When a fuzz test fails, Foundry prints the exact counterexample that broke the property and the number of runs. It also stores a corpus so the failing input is replayed on future runs until you fix it.

$ forge test
[FAIL. Reason: assertion failed]
  Counterexample: calldata=0x..., args=[115792089237316195...]

Configuring the Fuzzer

Control fuzzing in foundry.toml. The runs setting is how many random inputs each fuzz test gets. More runs increase confidence but take longer.

# foundry.toml
[fuzz]
runs = 1000
max_test_rejects = 65536

Invariant Testing

Invariants go a step further. Instead of one function call, Foundry executes long random sequences of calls to your contract, then checks that a property still holds after every sequence.

Invariant functions are named with the invariant_ prefix.

function invariant_totalSupplyConstant() public {
    assertEq(token.totalSupply(), INITIAL_SUPPLY);
}

Handlers

For meaningful invariant tests you usually write a handler contract that exposes a curated set of actions. The fuzzer calls the handler's functions in random order, keeping the call sequences valid and focused.

function setUp() public {
    handler = new Handler(token);
    targetContract(address(handler));
}

Ghost Variables

Handlers often track ghost variables: bookkeeping totals updated as actions run. Invariants compare the contract's real state against these ghost values.

For example, summing every deposit in the handler and asserting it equals the vault's total assets catches accounting drift.

// inside handler
uint256 public ghostTotalDeposited;
function deposit(uint256 a) external {
    vault.deposit(a);
    ghostTotalDeposited += a;
}

Fuzz vs Invariant

Knowing which to reach for:

Fuzz tests a single function against random arguments — good for input validation and pure logic
Invariant tests random sequences of actions — good for stateful systems like vaults, AMMs, and token accounting

Use both: fuzz for unit-level properties, invariants for system-level guarantees.

Best Practices

Effective property testing:

State properties as universal truths, not examples
Use bound over heavy assume
Increase runs for critical contracts
Write focused handlers for invariants
Track ghost variables for accounting checks

Property tests catch the edge cases humans miss.

Quick Check

What is the key difference between a fuzz test and an invariant test in Foundry?

Recap

You learned property-based testing:

Fuzz tests take parameters; Foundry generates random inputs
Use bound and vm.assume to constrain inputs
Invariant tests run random call sequences via handlers
Ghost variables track expected state for accounting invariants
Counterexamples are saved and replayed

Next: the cast and anvil CLI tools.

Pertanyaan yang Sering Diajukan

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