How machine learning is transforming software testing

Machine learning (ML) has completely transformed our daily lives and the way we approach software development. Artificial intelligence (AI) wouldn’t exist without machine learning.

ML powers the smart assistants we use in our homes, helps create self-driving cars, and gives us new tools to run our businesses and businesses more efficiently.

All of these advances would not have been possible without software testing. And, fortunately, machine learning also offers a more efficient way to perform software testing.

Software testing history

Software testing is essentially a method of verifying whether a software product meets expected requirements and that a software product is actually doing what it is supposed to do.

Software development has always relied on software testing as an essential aspect of its lifecycle. The benefits of software testing include preventing bugs, improving performance, and reducing development costs.

As you can imagine, software testing has evolved considerably:

Manual test

Historically, software testing was a manual process that took a lot of time and energy. It required an army of people rigorously testing every part of the app and recording the results.

Test automation

Then came test automation, which made software testing more efficient and faster than the manual process of the past.

Test automation uses a computer to mimic the actions of a manual tester, following a script that has been created for it.

This allowed testing to become much faster and more accurate, but with it came the problem of test maintenance.

AI-powered tests

Today, artificial intelligence (AI) is transforming software testing in ways unimaginable ten years ago.

AI simplifies test creation, allowing anyone to create automated tests. This reduces the need for test maintenance, saving your team valuable time.

AI tests even offer new ways to assess results.

Critical Ways ML helps software testing

AI-powered tests uses ML to transform software testing in three critical areas: creation, analysis and maintenance.

I. Creation of tests

Automated testing requires you to create and run a test script, which means you are relying on a framework like Selenium.

The actions of a real user interacting with the user interface (UI) are replicated by Selenium using a combination of item selectors and actions. Selectors allow Selenium to identify the correct item on the screen. Then it can perform actions like clicking, hovering, entering text, or just checking that a component exists.

This process is fully controlled by the creation of a detailed test script. Unfortunately, these test scripts are notoriously difficult and time consuming to perform. Even the simplest scripts can take several hours to create, slowing down the process.

With AI, you can now create tests with just a few clicks on the screen. Rather than taking hours, these tests only take a few minutes to create. You simply interact with the tested site and the system converts it into a test.

The system learns:

• Which buttons are pressed and why
• How your site works
• What your test is trying to achieve

At the same time, it saves large amounts of data which can help it later.

II. Analysis of tests

When it comes to test analysis, test scripts tend to rely on pass-fail metrics. While this concept can simplify the analysis, it complicates the process much more.

The two main problems with this traditional style of test automation are only testing the things you explicitly say so and too many false positives. These pitfalls make it difficult to trust the validity of your pass / fail test. But AI can change that:

• Take advantage of computer vision
• Have more reliable tests
• Understand how your test should work

These features help AI-based testing solutions provide more accurate test analysis.

III. Test the maintenance

Test maintenance is known to be tedious and complicated for test engineers. Unfortunately, whenever the UI changes or is modified in any way, all test scripts will fail. Countless hours must then be spent debugging these tests.

Fortunately, AI fixes this problem as well. Since AI creates a model of your site, it can eliminate the need for test maintenance. It simply determines what has changed and corrects the tests. Essentially, machine learning allows it to self-repair rather than requiring manual maintenance.

Ultimately, ML has not only helped overcome the shortcomings of conventional software testing, but also ensures that you deliver what matters most to users: a great user experience. By leveraging AI and ML, you can streamline test creation, improve analysis, and reduce maintenance.