Using AI To Generate Unit Tests for Your Apps — Is It Worth It?
Tags: webdev, testing, ai, machinelearning
Published: 2023-04-22 14:46:20

There is a lot of value in having automated tests for your software. They not only help you find and fix problems quickly but also give you a better understanding of what your software is actually doing. In this post, we’ll explain how Artificial Intelligence can be used to help you create unit tests for your apps (no code examples included).

## Using AI to Create Unit Tests for Your Apps
AI can help with a lot of tasks that humans are bad at — like language translation, problem-solving, natural language understanding, and machine learning. AI has many different applications in the testing field, but it is perhaps most relevant for automating the creation of unit tests for software. A unit test is the smallest possible test that can prove the functionality of a software component or functionality within a larger system. It’s the kind of test that catches mistakes early on so they don’t get incorporated into the main code. A good unit test should cover all functionality that makes up the component or functionality and should be as specific as possible. It should explain what the component does and what data is expected to be passed through the API and should use stubbing or mock objects to help test API clients.

Let’s say you are developing an app that lets users create and order gifts online. You want to make sure that the user interface and the order process work as intended. AI can be used to assist with creating unit tests for your app based on actual user behavior or demographic data so that even if a user doesn’t explicitly state their intent, they will always realize they made the wrong selection when they place an order.

Here are 2 examples that can help you understand how you can use AI for unit tests:

Customer AI — Using AI to generate random numbers or letters that are close to a required value. This can be used to generate mock data for unit tests to ensure the algorithms work as intended and that any results are realistic. For example, suppose you have a grocery store app where customers can place orders for food. You want to make sure that the ordering process works as intended and does not result in any unwanted orders being placed. You could use AI to generate random numbers or letters that are close to the desired value to make sure the algorithm works as intended.

Data-driven AI — Using data-driven AI, you can create models that learn from real-world data and make predictions based on statistical data. This is useful for generating mock data that can be used to test the model and ensure it is working as intended. For example, suppose you have an app where users can create a to-do list. You want to make sure that the user interface and the engine work as intended and that no data is missing or incorrect. One way to do this is to use data-driven AI to learn from the to-do list entries and create a model that outputs the list items that are close to the required value. Then, for each entry, you can be sure that the model is working as intended and that no data is being discarded or overlooked.

## Benefits of Using AI to Create Unit Tests
Creating effective unit tests is crucial to the health of any software project. They give you an opportunity to test out new code and make sure it actually works before you ship it. There are many benefits to using AI to create unit tests for your apps, including:

Smarter and faster developers — By automating the creation of unit tests, you can speed up the workflow and reduce the overall time for development. You won’t have to spend as much time writing the test and reviewing the results, which will help speed up the release process.

Faster developers — By automating the creation of unit tests, you can speed up the workflow and reduce the overall time for development. You won’t have to spend as much time writing the test and reviewing the results, which will help speed up the release process.

More accurate estimates — AI-generated unit tests are more accurate than traditional manual testing, as they only run the tests that need to be run. As a result, you get more accurate estimates for the time it will take to complete the test since the AI is creating the test from scratch.

Effective communication — AI-generated unit tests enable effective communication between teams — since everyone working on the test runs it, they can see what the other teams are working on and resolve issues quickly. This helps everyone stay on the same page, which in turn leads to faster and more accurate outcomes.

Improved understanding of code — By using AI to generate random numbers or letters that are close to a required value, you can ensure the software actually works when run by users. If users report bugs or problems with the software, you can be sure that the generated mock data is accurate and representative of what the application does.

## Be Creative
There are many ways you can use AI to create unit tests for your apps. You can use it to create mock objects for mock data to help test functionality, create visual exercises that help you understand your functionality, or use machine learning to create randomness in your unit tests. You can also use AI to create a business case for your feature and present it to leadership and stakeholders to generate approval or push forward with implementation.

### Conclusion
Artificial intelligence can be a powerful tool for creating more effective unit tests for your apps. With AI, you can create random numbers or letters that are close to a required value, generate mock data to test the algorithm, or learn from real-world data and create more realistic testing data.