How AI Is Changing Automation Testing In Quality Assurance

AI has transformed automation testing in quality assurance, expediting the process and enhancing analytical capabilities. However, it faces challenges like potential bias and complex training requirements. It offers significant benefits but warrants caution in high-stakes testing situations.

Margarita Simonova
Margarita Simonova
Founder and CEO of ILoveMyQA

September 14, 2023

4 min read

How AI Is Changing Automation Testing In Quality Assurance

AI has been changing the world. In many cases, it has been making a variety of tasks easier and more efficient. From ChatGPT to the AI used in automation, the work of AI can be seen everywhere.

One new way of automating the testing process to assure that software is meeting standards is by using AI. With AI, the process of automatic testing becomes even faster.

Before AI

Before AI was introduced to automation testing, quality assurance was slower with a mix of manual and automatic processes. In the beginning, software was tested using a collection of manual methodologies and with a team testing the software over and over again to achieve consistency. This was time-consuming and therefore expensive.

Automation machines changed the quality assurance world by combining manual methods with automation tools and open-source frameworks. This still wasn’t the perfect process as it took time and still required some manual work.

AI came around and completely changed how automatic testing worked. Now, instead of testing being partially automatic and partially manual, software and technology are testing completely automatic.

The Benefits Of AI Being Used In Automation Testing

As you can imagine, the use of AI in automation testing comes with plenty of amazing benefits. Let’s take a look at some of the most prominent ones below:

AI Is Faster

One major way AI automatic testing of software has changed the process of quality assurance is by speeding up the entire process. With AI, there is no need to make manual calculations or go through the process of manually testing the software over and over again.

AI Can Learn

As AI completes tasks that humans give it, one of the main components of this technology is its ability to learn after completing tasks. If an AI does the same thing over and over again, it learns how to better the process over time.

The more AI is used, the smarter it becomes and, therefore, the better at automatic testing it is.

Enhanced Analytics

By using AI, you may receive aspects of analytics that you may not have considered otherwise. AI can find the information that is harder to pick out and turn it into comprehensive analytics. These analytics can help your team locate weak or troubled areas of your software that may have been missed otherwise.

AI Optimization

A feature in automatic testing of quality assurance that manual and many automatic machines fail to have is optimization after completing tests. This enhances the quality of tests completed by the AI and therefore gives you better data.

Potential Issues With AI Being Used In Automation Testing

Unfortunately, using AI in automation testing doesn’t come without its drawbacks. Let’s take a look at some of the main issues that tend to come up when AI is used in this case below:

AI Can Be Difficult To Train

AI systems need to be trained on a whole lot of different scenarios and sets of data if they’re going to be able to effectively respond to specific situations.

To make matters more complicated, when new data gets introduced, the AI will need to be trained all over again—otherwise, it might not produce the most accurate results. AI also tends to struggle with ideas and scenarios that are more complex or nuanced.

AI Can Be Prone To Bias

This one might surprise some people, but unfortunately, it’s true. If the data that was used to train the AI initially is biased, or certain factors don’t mesh well with the algorithms that were used to analyze the data, this can definitely cause issues. For example, let’s say an AI system is biased toward a specific demographic. If this is the case, it’s probably not going to produce very accurate information.

Bugs Or Issues With The Software

As with most software, the AI that’s used to test quality assurance doesn’t come without the occasional bug. It may, for example, flag something that’s not actually an issue or fail to identify very real issues in the software or technology products being tested. This is a big concern, and it can end up leading to a lot of wasted time and resources if human testers need to be called in to verify the results that the AI came up with.

When Companies Should Consider Forgoing AI

While AI has made great strides recently in terms of automation testing, it’s certainly not a perfect system. If your company happens to have limited access to data to train the AI on, you could end up running into issues, in which case, a human touch may be needed instead. Similarly, your company may be better off relying on manual testing methods if you don’t happen to have good financial resources, as AI for automation testing tends to be rather expensive.

Using AI to test something that has high stakes attached to it probably isn’t the best idea either. AI for automation testing is still in its infancy, and it’s important to keep that in mind when deciding whether or not you’re going to use it to test out your newest software or tech product. You don’t want to deal with the potential consequences of the AI software bugging out, so conducting manual tests will probably be your safest bet when it comes to high-stakes situations.

AI Is Making Automatic Testing For Quality Assurance Easier And Of Higher Quality

With the development of AI, the world of quality assurance has evolved once again. Testing software has become faster, better at looking for bugs, and requires less work from the human developer team.

With AI, you can test your software in record time, which is perfect if you are ever on a time crunch. With businesses always looking for ways to speed along different processes, AI is the perfect tool to enhance your software.

Margarita Simonova
Margarita Simonova
Founder and CEO of ILoveMyQA

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