Ethereum solidity unit testing

As such, minor vulnerabilities in smart contracts can and often lead to massive, irrecoverable losses for users. Comprehensive testing can, however, expose errors in smart contract code and reduce security risks before deployment. While traditional developers may be used to fixing software bugs after launching, Ethereum development leaves little room for patching security flaws once a smart contract is live on the blockchain. While upgradeability mechanisms for smart contracts, such as proxy patterns, these can be difficult to implement.

Besides reducing immutability and introducing complexity, upgrades often demand complex governance processes. For the most part, upgrades should be considered a last resort and avoided unless necessary. Detecting potential vulnerabilities and flaws in your smart contract during the pre-launch phase reduces the need for a logic upgrade.

Automated testing for smart contracts 1. Functional testing Functional testing verifies the functionality of a smart contract and provides assurance that each function in the code works as expected. Functional testing requires understanding how your smart contract should behave in certain conditions. Then you can test each function by running computations with selected values and comparing the returned output with the expected output. Functional testing covers three methods: unit testing, integration testing, and system testing.

Unit testing Unit testing involves testing individual components in a smart contract for correctness. A unit test is simple, quick to run, and provides a clear idea of what went wrong if the test fails. Unit tests are crucial for smart contract development, especially if you need to add new logic to the code. You can verify the behavior of each function and confirm that it executes as intended.

Running a unit test often requires creating assertions—simple, informal statements specifying requirements for a smart contract. Unit testing can then be used to test each assertion and see if it holds true under execution. Examples of contract-related assertions include: i. In integration testing, individual components of the smart contract are tested together.

This approach detects errors arising from interactions between different components of a contract or across multiple contracts. You should use this method if you have a complex contract with multiple functions or one that interfaces with other contracts. Integration testing can be useful for ensuring that things like inheritance and dependency injection work properly. System testing System testing is the final phase of functional testing for smart contracts.

A system evaluates the smart contract as one fully integrated product to see if it performs as specified in the technical requirements. A good way to perform system testing on a smart contract is to deploy it on a production-like environment, such as a testnet or development network.

System testing is important because you cannot change code once the contract is deployed in the main EVM environment. Both techniques, however, use different approaches for finding defects in contract code. Static analysis Static analysis examines the source code or bytecode of a smart contract before execution.

This means you can debug contract code without actually running the program. Static analyzers can detect common vulnerabilities in Ethereum smart contracts and aid compliance with best practices. Dynamic analysis Dynamic analysis techniques require executing the smart contract in a runtime environment to identify issues in your code. Dynamic code analyzers observe contract behaviors during execution and generate a detailed report of identified vulnerabilities and property violations.

Fuzzing is an example of a dynamic analysis technique for testing contracts. During fuzz testing, a fuzzer feeds your smart contract with malformed and invalid data and monitors how the contract responds to those inputs.

Like any program, smart contracts rely on inputs provided by users to execute functions. And, while we assume users will provide correct inputs, this may not always be the case. In some cases, sending incorrect input values to a smart contract can cause resource leaks, crashes, or worse, lead to unintended code execution.

Fuzzing campaigns identify such problems beforehand, allowing you to eliminate the vulnerability. It comes built-in with Hardhat, and it's used as the default network. You don't need to setup anything to use it. In our tests we're going to use ethers. Create a new directory called test inside our project root directory and create a new file in there called Token.

Let's start with the code below. We'll explain it next, but for now paste this into Token. It's used to send transactions to contracts and other accounts. Here we're getting a list of the accounts in the node we're connected to, which in this case is Hardhat Network, and we're only keeping the first one.

The ethers variable is available in the global scope. This is the object that has a method for each of your smart contract functions. Here we get the balance of the owner account by calling the contract's balanceOf method. Recall that the account that deploys the token gets its entire supply. By default, ContractFactory and Contract instances are connected to the first signer. This means that the account in the owner variable executed the deployment, and balanceOf should return the entire supply amount.

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So I wrote my own framework that runs in remix-tests and truffle-test. This is a lot of code that I'd be happy to delete again if there is a better way to do this. When requesting to switch to another actor, the test contract gets the balance of that actor and the bank contract data is modified so that the test contract gets the actors state.

This is not very beautiful but allows - IMHO - to write readable and maintainable tests. To "solve" the gas limit exceeded error, I changed the JavaScript source of remix-tests to have a 20 times higher gas limit ugly hack. I didn't find a way to achieve the same for truffle-test. Functional testing covers three methods: unit testing, integration testing, and system testing. Unit testing Unit testing involves testing individual components in a smart contract for correctness.

A unit test is simple, quick to run, and provides a clear idea of what went wrong if the test fails. Unit tests are crucial for smart contract development, especially if you need to add new logic to the code. You can verify the behavior of each function and confirm that it executes as intended. Running a unit test often requires creating assertions—simple, informal statements specifying requirements for a smart contract.

Unit testing can then be used to test each assertion and see if it holds true under execution. Examples of contract-related assertions include: i. In integration testing, individual components of the smart contract are tested together. This approach detects errors arising from interactions between different components of a contract or across multiple contracts. You should use this method if you have a complex contract with multiple functions or one that interfaces with other contracts.

Integration testing can be useful for ensuring that things like inheritance and dependency injection work properly. System testing System testing is the final phase of functional testing for smart contracts. A system evaluates the smart contract as one fully integrated product to see if it performs as specified in the technical requirements.

A good way to perform system testing on a smart contract is to deploy it on a production-like environment, such as a testnet or development network. System testing is important because you cannot change code once the contract is deployed in the main EVM environment.

Both techniques, however, use different approaches for finding defects in contract code. Static analysis Static analysis examines the source code or bytecode of a smart contract before execution. This means you can debug contract code without actually running the program. Static analyzers can detect common vulnerabilities in Ethereum smart contracts and aid compliance with best practices.

Dynamic analysis Dynamic analysis techniques require executing the smart contract in a runtime environment to identify issues in your code. Dynamic code analyzers observe contract behaviors during execution and generate a detailed report of identified vulnerabilities and property violations.

Fuzzing is an example of a dynamic analysis technique for testing contracts. During fuzz testing, a fuzzer feeds your smart contract with malformed and invalid data and monitors how the contract responds to those inputs. Like any program, smart contracts rely on inputs provided by users to execute functions. And, while we assume users will provide correct inputs, this may not always be the case.

In some cases, sending incorrect input values to a smart contract can cause resource leaks, crashes, or worse, lead to unintended code execution. Fuzzing campaigns identify such problems beforehand, allowing you to eliminate the vulnerability. Manual testing for smart contracts 1. Code audits A code audit is a detailed evaluation of a smart contract's source code to uncover possible failure-points, security flaws, and poor development practices. While code audits can be automated, we refer to human-aided code analysis here.

Code audits require an attacker mindset to map out possible attack vectors in smart contracts. Even if you run automated audits, analyzing every line of source code is a minimum requirement for writing secure smart contracts. You can also commission a security audit to give users higher assurances of smart contract safety.

Audits benefit from extensive analysis performed by cybersecurity professionals and detect potential vulnerabilities or bugs that could break the smart contract functionality. Bug bounties A bug bounty is a financial reward given to an individual who discovers a vulnerability or bug in a program's code and reports to developers. Bug bounties are similar to audits since it involves asking others to help find defects in smart contracts.

Bug bounty programs often attract a broad class of ethical hackers and independent security professionals with unique skills and experience. This may be an advantage over smart contract audits that mainly rely on teams who may possess limited or narrow expertise.

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