Key Ways Machine Learning is Being Used in Software Testing

14May - by aiuniverse - 0 - In Machine Learning


The impact that the software development industry has on the world is unparalleled to any other industry, because obviously software is one of the cores of modern-day society, no matter what industry or niche you’re looking at. This is only going to continue as time goes on.

However, with the rise of services like AI, Big Data, and machine learning, it’s going to be interesting to see how technologies like machine learning are going to be effect industries like the software development industry, in particular, the software testing part of the process.

Today, we’re going to explore exactly why and how machine learning is being used and is going to be used in software testing practices, and what benefits this is going to provide to the services and procedures. Let’s get into it.

What’s Wrong with Manual Testing?

When you manually test software, there are plenty of problems that currently affect the process. For one, software testing is time-consuming and expensive, and productivity is low. You also need a specialist software tester to make sure that everything is handled properly. This, of course, invites the risk of human error, which in some cases, could be incredibly costly.

Using Machine Learning Technologies

“On the other hand, when you train a machine to test software, once the machine has learned what it’s supposed to be doing, it’s incredibly fast at testing and will do what a human tester can do in a fraction of the time. This saves not only time, but also the money that would be spent on a software tester,” shares Nick Denning, a business writer.

Exceeding Manual Testing Limits

With a manual tester, you’ll have someone sitting in your office or remotely and testing your app or program. They’ll go through your software and sample different features and will test it. In bigger applications, you may have a group so you can test multiple users at once.

However, with machine learning testing, you can test up to 1,000+ of instances at the same time, meaning you can test network and user strain of multiple users of your software, or just try lots of different situations to see if bugs appear.

Recognizing Problems Manual Testing Doesn’t

When you’re manual testing your software, whether you’re doing it yourself, or you’re using a dedicated software tester, one key problem is that your tester might not be able to pick up glitches that they’re not used too. This can cause glitches to slip through the gaps and end up in your final product.

“When you’re using machine learning technologies, these are tools that are designed, by their very nature, to be as accurate as possible. Every single time to run them, they are going to go out of their way to deliver the results you’ve trained them to find. This is true whether you’re purchasing machine learning software or training your own,” explains Michael Taylor,  a tech writer.

Faster Testing Processes

As we mentioned above, software testing is a slow process when it’s carried out by a person or a small team. It can take weeks, or even months, in extreme cases, depending on the size of the project, and this can cost a huge amount of your budget. When machine learning is involved, you only need one technology to carry out all the tasks from start to finish.

This can save you so much time and will prevent you from having to carry out mundane tasks checking data logs and trying to find areas of code that are experiencing errors. This, of course, means you can automate a ton of steps in your testing procedures.

Since machine learning applications learn every single time they run, once they’ve found an error and addressed it, they can then learn this error has been dealt with, and can simultaneously run thousands of tests to make sure that nothing else was affected, all with this information still in mind.

This delivers more accurate results, more actionable data, and faster testing times that can help you get your software project to its final stages quicker than ever.

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