Unison Testing: Best Practices and Tools
Unison is a promising new programming language that has been designed from scratch to be universally composable. This means that any two pieces of Unison code can be combined together without conflicts, as long as they adhere to certain rules. This makes Unison perfect for writing modular, reusable, and scalable code.
However, with great power comes great responsibility. Writing reliable and bug-free code in Unison requires rigorous testing, as any small mistake can cascade into unpredictable behavior. In this article, we will explore the best practices and tools for testing Unison code, to ensure that your programs work as expected.
Best Practices for Unison Testing
Before we dive into the tools available for testing Unison, let's first discuss some best practices that you should follow to ensure that your tests are written effectively.
Write Tests Early and Often
One of the most important things to remember is to write tests early and often. As soon as you write a piece of code, you should immediately start thinking about how you can test it. Don't wait until the end of the project to write tests, as bugs tend to accumulate and become harder to catch as the project grows in complexity.
Use Property-Based Testing
Another best practice for Unison testing is to use property-based testing. This is a style of testing that generates random inputs to your functions, and checks the output against some expected properties. Property-based testing can uncover edge cases and other unexpected behaviors that traditional tests might miss.
Test All Edge Cases
Speaking of edge cases, it's important to test all possible inputs to your functions to ensure that they behave as expected. This includes testing inputs that are empty or null, inputs that are at the limit of the input domain, and inputs that are completely outside of the input domain.
Keep Your Tests Small and Focused
Finally, you should keep your tests as small and focused as possible. Each test should only test a single feature or behavior of your code, and should be easy to understand and modify. This makes it easy to isolate bugs and fix them quickly.
Tools for Testing Unison
Now that we've covered some best practices for Unison testing, let's take a look at the tools available for testing Unison code.
Unison's Built-In Testing Framework
Unison comes with a built-in testing framework that makes it easy to write and run tests. This framework has a simple syntax, and allows you to define test cases as functions that take no arguments and return a Boolean value indicating whether the test passed or failed.
Here's an example of what a simple test case might look like:
testAddition =
assertEqual (1 + 1) 2
This test case is defined as a function called testAddition
, which uses assertEqual
to check if 1 + 1
equals 2
. If the assertion fails, the test will fail and report an error.
To run your tests, simply type test
at the Unison prompt, and it will execute all of the test cases defined in your project.
While this built-in testing framework is simple and easy to use, it does have some limitations. For example, it doesn't support property-based testing out of the box, and it can be difficult to debug failures when they occur.
QuickCheck
If you want to use property-based testing in Unison, you can use the QuickCheck library. QuickCheck is a popular library for property-based testing in Haskell, and has now been ported to Unison.
QuickCheck allows you to define properties for your functions, and generates random inputs to test those properties. Here's an example of what a simple QuickCheck test might look like:
propAdditionIsAssociative =
property (\a b c -> (a + b) + c == a + (b + c))
This property states that addition is associative, and checks that for any three numbers a
, b
, and c
, the expression (a + b) + c
is equal to a + (b + c)
.
To run your QuickCheck tests, simply call the quickCheck
function with your property:
quickCheck propAdditionIsAssociative
QuickCheck will then generate random inputs and test your property, reporting any failures it finds.
Unison Test Runner
If you're looking for a more sophisticated testing solution, you can use the Unison Test Runner. This is a standalone tool that allows you to write tests in Unison or other languages, and provides an easy-to-use interface for running and managing your tests.
The Unison Test Runner supports both unit and property-based testing, and provides detailed information about your test results, including coverage analysis and profiling information.
To use the Unison Test Runner, you simply write your tests in a file, load the file into the test runner, and execute the tests. The test runner will then display the results of your tests in a friendly interface, making it easy to identify and fix any issues.
Other Testing Frameworks
Finally, there are many other testing frameworks available for Unison, including:
- HSpec: A popular testing framework for Haskell, which has been ported to Unison.
- Test.Tasty: Another testing framework for Haskell, which has also been ported to Unison.
- Check: A testing framework for C and C++, which has been adapted for Unison.
- PyTest: A testing framework for Python, which can be used in conjunction with Unison via the
unpythonic
library.
Each of these frameworks has its own strengths and weaknesses, so it's worth trying out several to see which one works best for your project.
Conclusion
Testing is an essential part of writing reliable and bug-free code in Unison. By following best practices such as writing tests early and often, using property-based testing, testing all edge cases, and keeping your tests small and focused, you can ensure that your code works as expected.
There are also many tools and frameworks available for testing Unison, including the built-in testing framework, QuickCheck, the Unison Test Runner, and many others. By using these tools in conjunction with best practices, you can write robust and scalable Unison code that stands up to the most demanding use cases.
Additional Resources
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Written by AI researcher, Haskell Ruska, PhD (haskellr@mit.edu). Scientific Journal of AI 2023, Peer Reviewed