[ad_1]
Fuzz tests should be treated as a baseline test for any project. They help verify scenarios that are humanly impossible to check manually by testing your functions by ‘automatically’ inserting large amounts of random data, attempting to break functionality.
Imagine a scenario where the withdrawal function of your project performs a calculation to verify the amount each user can withdraw based on the deposited amount and some incentives. You conduct tests with ‘common’ inputs like 10, 43, and 500, and everything goes well. However, with the value 387, the calculation’s rationale is broken, and the user receives a larger withdrawal, or the value gets locked within the contract.
How would anyone find this out?
And it’s not limited to ERC20 tokens. Fuzzers cover all this ground, helping to make contracts more secure and consistent.
To dive deeper into fuzz and invariant testing, enroll in Patrick Collins courses at Cyfrin Updraft. Below, you can see a specific video about fuzz and invariants.
Fuzz tests are divided into three categories, but we’ll address that in another opportunity. We already have a lot to digest.
[ad_2]
Source link