How Machine Learning Is Transforming Software Testing

Machine learning (ML) has completely transformed our day-to-day lives and how we tackle software development. Artificial intelligence (AI) wouldn’t exist without machine learning.  

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

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

 

History of Software Testing

 

Software testing is essentially a method of checking whether a software product matches expected requirements and that a software product actually does what it is supposed to do.

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

As you would expect, software testing has evolved significantly:

 

Manual testing

 

Historically, software testing was a manual process that was painstakingly time and energy-consuming. It required an army of people rigorously testing every part of the application and recording the results.

 

Test automation

 

Then came test automation, which allowed software testing to become more efficient and fast than the manual process of the past.

Test automation uses a computer to replicate 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 it came with the problem of test maintenance.

 

AI-powered testing

 

Today, artificial intelligence (AI) is transforming software testing in ways that could not have been dreamt of a decade ago.

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

AI testing even offers new ways to assess the results.

 

Critical Ways ML Helps Software Testing

 

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

 

I. Test Creation

 

Automated testing requires you to create and run tests script, which means relying on a framework such as Selenium.

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

This process is controlled entirely through the creation of a detailed test script. Unfortunately, these test scripts are notoriously hard and time consuming to get right. Even the most straightforward scripts can take several hours to create, bogging down the process.

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

The system is learning:

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

At the same time, it records vast amounts of data that can help it later.

 

II. Test analysis

 

When it comes to test analysis, test scripts tend to fall back on pass-fail measures. While this concept can make the analysis simple, it complicates much more of the process.

The two main problems with this traditional style of test automation are testing only things you explicitly tell it to and too many false positives. Because of these pitfalls, it’s hard to rely on the validity of your pass/fail test. But AI can change that:

• Leverage computer vision
• Have more reliable tests
• Understand how your test should be performing

 These features help AI-powered test solutions to provide more accurate test analysis.
 

III. Test maintenance

 

Test maintenance is renowned for being painful and complicated for test engineers. Unfortunately, any time the UI changes or is altered in any way, all of the test scripts will fail. Countless hours must then be spent debugging these tests.

Thankfully, AI solves this problem as well. Since AI creates a model of your site, it can eliminate the need for test maintenance. It just works out what changed and fixes the tests. Essentially, machine learning allows it to be self-healing rather than requiring manual maintenance.

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