Property-based testing (PBT) is a software testing technique that focuses on testing the behavior of a program or function by specifying mathematical properties that should hold for all inputs. This is in contrast to traditional testing techniques, which typically focus on testing specific examples.
PBT has a number of advantages over traditional testing techniques, including:
It can generate a much larger number of test cases, which can help to uncover bugs that would otherwise be missed.
It can be used to test more abstract properties of a program, such as its correctness, robustness, and performance.
It can be used to test programs that are difficult or impossible to test using traditional techniques, such as programs that generate random data.
Is PBT more powerful than black box testing?
PBT and black box testing are complementary techniques. Black box testing is good for testing the functionality of a program from the perspective of a user, while PBT is good for testing the abstract properties of a program.
In some cases, PBT can be more powerful than black box testing. For example, PBT can be used to test the correctness of a program, while black box testing can only be used to test the functionality of the program for a limited set of inputs.
However, PBT is not a replacement for black box testing. Black box testing is still important for testing the usability and functionality of a program from the perspective of a user.
Conclusion
PBT is a powerful software testing technique that can be used to uncover bugs that would otherwise be missed. It is complementary to black box testing, and both techniques are important for ensuring the quality of software.
To establish a test plan using property-based testing (PBT), you can follow these steps:
Identify the properties to test. This is the most important step, as it will determine the effectiveness of your test plan. Consider the following questions when identifying properties to test:
What are the invariants of the system under test?
What are the preconditions and postconditions of each function or method?
What are the expected performance characteristics of the system?
Choose a PBT framework. There are many different PBT frameworks available, such as QuickCheck, ScalaCheck, and Hypothesis. Choose a framework that is supported by your programming language and that provides the features you need.
Write property tests. Each property test should check a specific property of the system under test. Property tests are typically written using a declarative style, meaning that you specify the expected behavior of the system, but not how the system should achieve that behavior.
Generate test cases. The PBT framework will generate test cases from your property tests. The framework will typically use a variety of techniques to generate test cases, such as random generation and fuzzing.
Run the test cases. The PBT framework will run the test cases and report any failures.
Analyze the results. If the test cases fail, you should investigate the failures to determine the cause. You may need to fix the system under test, or you may need to rewrite the property test.
Here is an example of a property test for a function that adds two numbers:
Here is the equivalent of the property test in JavaScript for the given code:
JavaScript
import quickCheck from 'quickcheck-js';
// Property: The addition function is commutative.
quickCheck('addition is commutative', () => {
const x = quickCheck.integer();
const y = quickCheck.integer();
// Check that the addition function is commutative.
expect(x + y).toEqual(y + x);
});
To run the property test, we can use the following command:
npx quickcheck-js
If the property test fails, the framework will generate a counterexample, which is a set of input values that cause the property to fail. This can help debug the code under test.
Here is an example of a counterexample that the framework might generate:
JavaScript
// Counterexample: x = 1, y = 2
const x = 1;
const y = 2;
// Check that the addition function is commutative.
expect(x + y).toEqual(y + x); // This assertion fails!
The counterexample shows that the addition function is not commutative for all inputs. This is a bug in the code under test.
This property test (C#) checks that the addition function is commutative. The PBT framework will generate test cases for this property test, such as (1, 2), (-10, 100), and (0, 0). The framework will then run the test cases and report any failures.
Test Plan
PBT can be used to establish a test plan for any type of software system. It is particularly well-suited for testing complex systems with many different components.
Here are some tips for establishing a test plan using PBT:
Start by identifying the most important properties of the system under test. These are the properties that are most likely to be violated by bugs.
Use a variety of techniques to generate test cases. This will help to ensure that your test cases cover a wide range of possible inputs.
Analyze the results of the test cases carefully. If a test case fails, investigate the failure to determine the cause.
Use PBT as part of your overall testing strategy. PBT is a powerful tool, but it is not a replacement for other types of testing, such as unit testing and integration testing.
JavaScript testing
Here are some popular PBT frameworks for JavaScript:
QuickCheck.js: QuickCheck.js is a port of the popular QuickCheck PBT framework to JavaScript. It is a lightweight framework that is easy to use and provides a variety of features, such as test case generation, property shrinking, and counterexample generation.
ScalaCheck.js: ScalaCheck.js is a port of the ScalaCheck PBT framework to JavaScript. It is a more powerful framework than QuickCheck.js, but it is also more complex to use. ScalaCheck.js provides a variety of features, such as user-defined generators, property refactoring, and property composition.
Hypothesis: Hypothesis is a modern PBT framework that is designed to be easy to use and powerful. It provides a variety of features, such as test case generation, property shrinking, and counterexample generation. Hypothesis also provides support for asynchronous testing and property parameterization.
In addition to these frameworks, there are also a number of other PBT frameworks available for JavaScript, such as:
jsverify: jsverify is a lightweight PBT framework that is based on the Haskell library QuickCheck.
js-check: js-check is a PBT framework that is designed to be easy to use and integrate with existing testing frameworks.
proptest: proptest is a PBT framework that is designed to be used with the Jest testing framework.
Which PBT framework you choose will depend on your specific needs and preferences. If you are new to PBT, I recommend starting with QuickCheck.js or Hypothesis. These frameworks are easy to use and provide a good set of features. If you need more power or flexibility, you can try ScalaCheck.js or another PBT framework.
Here are some tips for choosing a PBT framework for JavaScript:
Consider the features that are important to you. Do you need support for asynchronous testing? Property parameterization? Test case shrinking?
Consider the complexity of the framework. Do you want a simple framework that is easy to use, or a more powerful framework that is more complex to use?
Consider the documentation and support available for the framework.
Versus Unit Testing
Here are some popular PBT frameworks for JavaScript:
QuickCheck.js: QuickCheck.js is a port of the popular QuickCheck PBT framework to JavaScript. It is a lightweight framework that is easy to use and provides a variety of features, such as test case generation, property shrinking, and counterexample generation.
ScalaCheck.js: ScalaCheck.js is a port of the ScalaCheck PBT framework to JavaScript. It is a more powerful framework than QuickCheck.js, but it is also more complex to use. ScalaCheck.js provides a variety of features, such as user-defined generators, property refactoring, and property composition.
Hypothesis: Hypothesis is a modern PBT framework that is designed to be easy to use and powerful. It provides a variety of features, such as test case generation, property shrinking, and counterexample generation. Hypothesis also provides support for asynchronous testing and property parameterization.
In addition to these frameworks, there are also a number of other PBT frameworks available for JavaScript, such as:
jsverify: jsverify is a lightweight PBT framework that is based on the Haskell library QuickCheck.
js-check: js-check is a PBT framework that is designed to be easy to use and integrate with existing testing frameworks.
proptest: proptest is a PBT framework that is designed to be used with the Jest testing framework.
Which PBT framework you choose will depend on your specific needs and preferences. If you are new to PBT, I recommend starting with QuickCheck.js or Hypothesis. These frameworks are easy to use and provide a good set of features. If you need more power or flexibility, you can try ScalaCheck.js or another PBT framework.
Here are some tips for choosing a PBT framework for JavaScript:
Consider the features that are important to you. Do you need support for asynchronous testing? Property parameterization? Test case shrinking?
Consider the complexity of the framework. Do you want a simple framework that is easy to use, or a more powerful framework that is more complex to use?
Consider the documentation and support available for the framework.
Example:
Here is an example of property testing in JavaScript using the QuickCheck.js framework:
JavaScript
import quickCheck from 'quickcheck-js';
// Property: The addition function is commutative.
quickCheck('addition is commutative', () => {
const x = quickCheck.integer();
const y = quickCheck.integer();
expect(x + y).toEqual(y + x);
});
This property test checks that the addition function is commutative, meaning that the order of the operands does not affect the result. The quickCheck.integer() function generates a random integer value. The expect() function checks that the actual value matches the expected value.
To run the property test, we can use the following command:
npx quickcheck-js
If the property test fails, the framework will generate a counterexample, which is a set of input values that cause the property to fail. This can help debug the code under test.
Here is another example of a property test in JavaScript:
JavaScript
import quickCheck from 'quickcheck-js';
// Property: The binary search algorithm always returns the index of the target element,
// or -1 if the target element is not found.
quickCheck('binary search always returns the correct index', () => {
const array = quickCheck.array(quickCheck.integer());
const target = quickCheck.integer();
const index = binarySearch(array, target);
if (array.includes(target)) {
expect(index).toBeGreaterThanOrEqual(0);
expect(index).toBeLessThan(array.length);
expect(array[index]).toEqual(target);
} else {
expect(index).toEqual(-1);
}
});
This property test checks that the binary search algorithm always returns the correct index of the target element, or -1 if the target element is not found. The quickCheck.array() function generates a random array of values. The binarySearch() function is a function that implements the binary search algorithm.
Property testing can be used to test a wide variety of properties, such as the correctness, performance, and robustness of software systems. It is a powerful tool for ensuring the quality of software.
Disclaim: I have never used PBT testing before and I just find out about it using AI (Bard). If you use it, share your story.