Will Machine Learning Take Over Testing Roles?

It has been heavily speculated that machine learning (ML) is going to take over manual software testing. 

However, many people still believe that ML won’t eliminate manual testing roles, but will certainly redefine the way it works. 

Both schools of thoughts are justified and backed up by a solid argument, which makes it more difficult to reliably predict the future role of AI in testing roles. Let’s look into it in detail.

A little background

Nearly six decades have been passed since Machine Learning came into existence. Since then, it has been applied in numerous fields.

First, ML was successfully used to diagnose cancerous tumors in kidneys in 1991.

Moreover, it’s playing an instrumental role in the financial sector—mainly to trade securities since 1992.

Also, since the last decade, it’s been utilized in the development of driverless car technology.

These are only a few examples; ML is applied in several sectors to automate and improve the operations and workflow.

Appdiff—A Recent Example

Appdiff—a leading AI-assisted mobile testing company—has built a mobile testing platform that’s heavily relied on machine learning. This platform facilitates mobile app testing without requiring any sort of interference of humans.  

The company has navigated through several challenges such as teaching a testing program to perform gestures, navigation, defining types of input, among several others.

They also created ML algorithms onhow to predict if the outcome of a given action was likely to expose a defect.

That being said, Appdiff tests about 90% of the surface area of a typical mobile app, which is almost the same percentage as human testers.

Thus, the case is more inclined toward efficiency and volume than the extent of app testing. There are only a handful of testers who are able to interact with an application as quickly as ML. 

Other Side of The Coin

There are several challenges that ML is still unable to resolve, which strengthens the argument that ML will not “completely” take over the testing process.

For instance, each application is different and it’s difficult for a bot to determine what the application is supposed to do. Machine learning primarily works on likelihood-based strategy rather than dealing with uncertainties. 

Though the ML technology has made great leaps, it’s still missing the creativity, exploration, knowledge, and analytical capabilities that humans possess.

Furthermore, testing roles are more difficult than writing software. Testers need to be smarter and more detail-oriented than programmers to identify problems in the code.

Final Words

It appears unclear whether testing roles will be replaced by machine learning in the future, but it’s definitely going to make a big impact on testing jobs.

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