AI & Machine Learning in Software Testing

Most applications need to be verified on a variety of devices, platforms and browsers. To accomplish this, QAs perform cross-browser testing and cross-platform testing, making use of all kind of tools, like Applitools, LambdaTest, Browserstack and more. But did you know that some of these tools already have integrated AI & ML to help enhance your tests?

There are a lot of AI & ML tools that can help you in your work, resulting in more coverage, higher quality tests and for you to be more productive and efficient. Let’s see in the article below how AI can help us achieve this and how we can make the most out of it.

Test data on the fly

What if someone (or something) could have a quick look at your requirements or code and generate test cases and test data? We all know that creating test cases or generating test data is time-consuming, but AI tools can analyze the requirements or documentation, as well as pieces of code and generate all the data we need. Of course, we need to verify and validate it, but it saves us so much time and we can focus our efforts somewhere else.

What about automated test suites?

Good news here, too. AI can review the test results, recent code changes, code coverage and other metrics, and then decide which tests to run and run them for you. In addition, by using a few integrations, such as Qmetry or other test management tools, it can also create test suites automatically in Jira and report bugs if found. Let’s dive in, to find some interesting tools powered by AI & ML that will help you save time and effort in your testing process.

Examples of AI and ML in Software Testing

1. Applitools

Have a look at the image below. Can you tell if an airplane flight is missing by just looking at the image? Probably not, but the Applitools will find the differences in a matter of seconds. Applitools is an AI-powered visual testing platform that can analyze the application’s user interface and identify visual defects and bugs, such as layout issues, text overlaps, and color mismatches, that human testers might miss.


This tool has the ability to automate the process of creating and running tests. You just need to input your app’s requirements and specifications and it will generate test cases and test data, while also analyze the test results, finding flaws and bugs that you may have missed. Moreover, it also provides its AI tool for mobile too (android / iOS).

3. Mabl

Easily create, run, analyze or manage functional UI tests. It also provides support for API tests and it is mobile ready, just like More than that, it offers a bunch of cool integrations with well-known tools, such as Jira, Bitbucket, Gitlab, Github, Postman and more.

4. Functionize 

Functionize brings big data, machine learning, and computer vision testing to the table. What’s more, it can read test plans or test cases and generate fully-functional tests from it, covering functional tests, end-to-end testing, API testing, Localization testing and more. It also offers some out-of-the-box integrations with Jira, Slack, TestRail, etc

So, what are the benefits?

Improved accuracy: Let’s face it, you are prone to make mistakes when testing software. That’s why we make the most of automated tests, which are tireless. But with the help of the AI, we can develop more effective tests, to deliver even a higher quality to the end-users.

Faster Testing & Reduced costs: Besides creating test cases and test data easily and faster, AI can also adapt to frequent code changes made by the development team and run or re-run the automated tests when needed, at a much faster pace and with reduced costs.

Features will get faster to end-users: Improved accuracy + faster testing + reduced costs = faster time to market.

Adding all the pieces together

As a final thought, the trends goes towards a time when will be unable to keep pace with the tight delivery cycles, increasing technical complexity and frequent changes. By automating the test creation, execution, and maintenance processes, AI and ML are transforming the software testing industry nowadays. It can ensure that the application is thoroughly tested and save time and effort during the testing process. The tools above are just a few examples of how AI and ML are used in software testing.

As these tools continue to evolve, we can expect and hope to see more innovative and efficient testing solutions in the future. We need to learn how to work smarter, not harder and with the help of these tools, testers now have the ability & time to concentrate on more pressing responsibilities, such as expanding the scope of exploratory testing and raising the overall quality of software applications.

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