Smart Contract Testing Tools & Libraries
Smart contract testing involves confirming that a smart contract's code behaves as predicted. It helps to ensure the smart contract is reliable, user-friendly, and secure.
This is lesson 6 of the Smart Contracts 101 free course by Rohas Nagpal.
1. Introduction to Smart Contract Testing
Smart contract testing involves confirming that a smart contract's code behaves as predicted. It helps to ensure the smart contract is reliable, user-friendly, and secure.
Typically, testing involves running a smart contract using a smaller data subset than it would ordinarily handle. If the outcomes align with expectations, it is believed to be working well. Various tools can help write and run test scenarios to check if a contract behaves as expected.
Testing smart contracts is crucial as they often handle valuable financial transactions. Even small coding mistakes can lead to significant user losses. Testing helps find and fix these issues before launching the smart contract on Mainnet.
Upgrading a contract to fix a bug is a complex process and can result in further errors. Instead, thorough testing reduces security risks and the need for complex upgrades after deploying.
Smart contract testing methods for Ethereum fall under two categories:
automated and
manual testing.
2. Automated Smart Contract Testing
Automated testing uses tools to automatically find errors in a contract.
The advantage here is that tests can be set to run frequently with minimal human input, making it more efficient than manual testing. Automated testing is great for repetitive, time-consuming tests, or when testing crucial contract functions.
However, these tools can sometimes miss bugs and give false positives, so combining them with manual testing is advisable.
2.1 Unit Testing of Smart Contracts
Unit testing checks each part of a smart contract individually to ensure they work correctly.
These tests are straightforward, quick, and help determine any errors. They verify function outputs and whether contract storage updates properly.
Running these tests after modifying contract code helps maintain accuracy.
Popular tools
solidity-coverage - This tool provides code coverage for smart contracts written in Solidity.
Waffle - This is a framework for advanced development and testing of smart contracts, based on ethers.js.
Remix Test- This tool tests Solidity smart contracts. It works with the "Solidity Unit Testing" plugin in Remix IDE, which is used to write and run contract test cases.
OpenZeppelin Test Helpers- This is an assertion library for Ethereum smart contract testing, helping ensure contracts behave as planned.
Truffle Tests - This is an automated testing framework designed to simplify contract testing.
Brownie unit testing framework - Brownie uses Pytest, a robust test framework that allows small tests with minimal code and is suitable for large projects due to its scalability.
Foundry Tests- Foundry features Forge, a speedy and adaptable Ethereum testing framework that can conduct simple unit tests, gas optimization checks, and contract fuzzing.
Hardhat Tests - This is a testing framework for smart contracts, based on ethers.js, Mocha, and Chai.
ApeWorx- This is a Python-based development and testing framework for smart contracts, targeting the Ethereum Virtual Machine.
2.2 Integration Testing of Smart Contracts
Integration testing, on the other hand, checks the entire smart contract, including interactions between different functions or contracts.
This testing type is handy if your contract works with other on-chain contracts.
A common method involves creating a copy of the blockchain and simulating interactions between your contract and others in a safe, local environment.
2.3 Property-based Testing of Smart Contracts
Property-based testing ensures a smart contract upholds certain properties in various scenarios, like "Arithmetic operations in the contract never overflow or underflow."
There are two main techniques for this:
static analysis and
dynamic analysis.
Static analysis
Static analysis uses the smart contract's source code to determine if it meets the property. This technique doesn't involve contract execution but inspects possible execution paths based on the code structure.
It's often used to spot safety issues, syntax errors, or code standard violations. However, it might miss deeper vulnerabilities and may flag false positives.
Popular tools:
Slither - This is a Python-based framework for static analysis of Solidity contracts. It helps find vulnerabilities, improves code understanding, and supports writing custom analyses for smart contracts.
Ethlint- This is a linter used for applying style and security best practices in Solidity, the smart contract programming language.
Dynamic analysis
Dynamic analysis tests smart contract functions by generating symbolic or concrete inputs to see if any execution violates the properties.
Unlike unit tests that cover a single scenario, dynamic analysis covers multiple scenarios, with test case generation handled by a program.
Popular tools:
Echidna - This is a quick contract fuzzer for spotting vulnerabilities in smart contracts using property-based testing.
Diligence Fuzzing - This is an automated fuzzing tool helpful for identifying property violations in smart contract code.
Manticore - This is a dynamic symbolic execution framework for analyzing EVM bytecode.
Mythril - This tool assesses EVM bytecode for contract vulnerabilities using taint analysis, concolic analysis, and control flow checking.
Diligence Scribble - Scribble is a specification language and runtime verification tool that allows you to annotate smart contracts with properties for automatic contract testing using tools like Diligence Fuzzing or MythX.
3. Manual Smart Contract Testing
Manual testing involves humans running each test one after the other and comparing the contract's actual behavior with the expected behavior.
This approach requires significant resources and there's a chance of human error leading to missed issues. However, human testers can sometimes catch edge cases that automated tools might overlook.
3.1 Testing Smart Contracts on a local Blockchain
Testing smart contracts on a local blockchain is a useful alternative to testing on Mainnet - the main Ethereum network, which costs gas fees and poses the risk of losing actual money due to potential bugs.
A local blockchain, run on your computer, mimics the behavior of Ethereum's execution environment.
This allows you to interact with a contract without significant costs. It's helpful for manual integration testing, ensuring complex on-chain interactions work correctly.
3.2 Testing Smart Contracts on Testnets
Testing contracts on a testnet is another approach. A testnet operates just like Ethereum Mainnet but uses Ether (ETH) which holds no real-world value. This enables anyone to interact with your contract risk-free.
This testing type helps evaluate the overall user experience of your application. Beta testers can do trial runs and report issues with the contract's functionality and logic.
Ideally, after testing on a local blockchain, contracts should be deployed on a testnet as it mirrors the Ethereum Virtual Machine's behavior more closely.
Many Ethereum projects deploy dapps on testnets to test smart contracts under realistic conditions.