Solving the main challenges of traditional software testing

Traditional software testing used to be a simple manually performed task where the tester would use a system as a potential end-user would, go through a list of actions and steps, then compare results with expectations to check if the system is working as intended or has bugs. Nowadays, this has become almost impossible to do this manually because systems are becoming more complex and testers are facing huge amounts of data they need to check and handle in a very short and limited period.

When faced with increased amounts of data and shortened times, testers are more likely to make assumptions, overlook bugs and green-light a faulty software. The result is shipping a buggy system, which usually turns happy users into frustrated ones, and even worse hurts the brand’s reputation.

Luckily, testers have technology on their side as well and can apply a new, automated and more efficient approach – one that combines machine learning, artificial intelligence, and predictive analysis.

The machine learning and AI approach, which teaches systems to learn while they do, and apply the acquired knowledge in the future, makes software testing not only faster, but also more precise. This is because automated AI-powered testing systems have the ability to learn without human intervention or complex programming.

The testing systems should improve from experience, automatically access data, run tests with it, learn from the results, and as a result improve the quality of the tests by learning patterns and predicting problems. What is even better, regardless of how much the amount of data increases, machine learning and AI-powered testing can handle it, with the margin of error decreasing constantly.

Moreover, as market demand grows, it is important for enterprises to be able to predict customers’ needs more accurately. And machine learning and predictive analysis offer businesses the opportunity to analyze and understand customers better so they can predict what new products or features to develop. This is also necessary for having an uninterrupted and shorter testing process that leads to creating solutions that offer improved user experience.

Is AI the future of software testing?

AI and machine learning have become an increasingly important component of software testing and QA. It gives testers the opportunity to better understand the end-users’ needs, compare them to the software’s features and options, and react faster to changing market expectations.

In order to improve software quality, release software faster and scale-up activities, enterprises need to seriously consider automating their software testing. This new approach is set to fill in the gaps of traditional software testing methods, make the whole process more efficient, and most importantly, deliver results more relevant to end-users’ needs.