Testing Unsafe Invariants

In this lesson, you will learn how to use property-based testing to verify the complex safety invariants of your unsafe code blocks.

When you write unsafe Rust, you are making a contract with the compiler. You might guarantee that a pointer is always non-null, that a specific memory region is aligned, or that a slice never overlaps with another. Because these invariants often depend on state that the borrow checker cannot see, standard unit tests often fall short—they only test "happy paths."

Property-based testing (using crates like proptest) allows us to throw thousands of randomly generated inputs at our unsafe code to hunt for edge cases that violate our assumptions.

The Strategy

To test an invariant, you don't just test values; you test the properties that must hold true regardless of the input. If you have implemented a custom Vec-like structure that performs manual memory allocation, you shouldn't just test that push works. You should write a test that:

1. Generates a random sequence of push, pop, and insert operations.
2. Interleaves them with random capacity changes.
3. Checks after every operation that your unsafe pointer logic hasn't corrupted the header or gone out of bounds.

Example: Verifying Alignment

Imagine you have a custom memory arena that returns pointers aligned to 16 bytes. Instead of testing one hardcoded allocation, you use a property-based approach:

proptest! {
    #[test]
    fn test_arena_alignment(size in 1..1024usize) {
        let arena = MemoryArena::new();
        let ptr = arena.alloc(size);
        
        // The Invariant: Pointer must be 16-byte aligned
        assert_eq!(ptr as usize % 16, 0, "Alignment violation at size {}", size);
        
        // Additional Invariant: Pointer must be non-null
        assert!(!ptr.is_null());
    }
}

By fuzzing the input size, the test runner will often discover the specific values that cause your arithmetic to overflow or miss the alignment requirement. If a failure occurs, proptest automatically "shrinks" the input to the smallest possible case that triggers the bug, making it significantly easier to debug your unsafe logic than a standard random-number generator would.

Key Takeaway: Use property-based testing to treat your unsafe invariants as mathematical constraints, allowing the test suite to discover the "impossible" edge cases that your manual reasoning might have missed.