4 big ways AI will disrupt software testing

AI in software testing is becoming more and more popular and with good reason. Although artificial intelligence today is mainly used for automation and convenience, the technology is growing smarter and more efficient every day. In software testing, AI can be used to enhance many repetitive tasks. This both speeds up the processes involved and improves accuracy.

Regression testing is an example. Any time the software code changes, regression tests are performed to ensure the applications are still working as intended. This is usually done manually. However, AI can automate this whole process by checking for problems that occur with the application after changing the code at different intervals.

AI will not take over completely, but it will certainly disrupt the traditional software testing process. This is the way.

1. Automatic regression testing

Usually, manual regression testing requires a lot of time and effort from testers and developers. Every time the software code changes, tests must be performed on the resulting application. It guarantees a back and forth between developers and testers – or, if there are no testers, it calls on developers to take on multiple tasks.

AI solutions can fully automate this process, perform tests almost instantly after any code is changed. As long as it is properly trained, the AI ​​will always be faster, more efficient, and more accurate than manual testers.

Furthermore, developers will receive test results and related data sooner, allowing them to start making the necessary fixes immediately. Or, if no problems are detected, they can move on to other areas of development more quickly.

Add one more layer here, AI can make regression testing better over time as it grows smarter. As a result, the regression test cycle becomes much more reliable.

2. Early detection of bugs and errors

A key element of software testing is making sure the code or the applications themselves are up to par and performing at expected levels. So a big part of the test is look for bugs and other software problems and fix them.

Even the most skilled developers can miss syntax or coding errors, especially in large-scale projects. But while modern programming tools can pinpoint fundamental errors, natural language problems are often ignored until they are detected at runtime. AI in software testing can be used to detect these errors by finding bugs and errors.

Machine learning (ML) relies on natural language processing (NLP), training data, and pre-programmed scanning engines to identify potential problems and flag them for review. Better yet, AI can be used to directly insert new code if and when the fixes are clearer. It saves time, money and a lot of headaches.

3. Productivity benefits

With early and buggy detection and more supportive regression testing, developers and testers can expect some serious productivity gains all around. With the help of AI and machine learning systems, many of the rote learning tasks involved in the development process can be accelerated, augmented, or implemented directly with automation solutions.

This is unlike what we see in other industries where AI is being used more and more Powering robotics automation (RPA), intelligent automation (IA), etc.

For example, communication between groups and departments. It is usually done after application tests. Now it can be handled by automated solutions. Instead of manually submitting information, which takes a long time for relevant contacts, information can be instantly shared with all parties. That same benefit extends to all the processes and solutions that AI touches.

4. Self-Recovery Password

AI and ML enable the so-called “self-healing technology” or self-healing code. Not only can AI identify and detect language problems faster than a human, but when aided by the right resources, it can fix that code faster than ever. Early failure and fault detection are just the beginning.

AI solution can actually inject code fixes for common problems or they can be guided to fix more complex problems when they know what to look for and how to react. Better yet, as we often see with ML, self-healing algorithms become more accurate and more representative of the code in action, over time, just through the input of more information.

This means that when developers change the code and it crashes the software, the AI ​​will immediately take over and fix the problem. It reduces the amount of time developers and testers have to spend looking for those problems, but it also cuts down on general troubleshooting significantly. It can also flag common issues for later review to ensure no repeat errors and uncomplicated issues throughout the development phase.

AI in Software Testing: A Good Breakthrough Is Coming

Thanks to productivity improvements from smarter and more efficient processes, AI in software testing has the potential to disrupt the field. Manual tasks that require a lot of time and effort can be automated almost entirely by AI. Additionally, early failure detection can be handled with NLP tools, while self-healing code ensures software applications stay up and running at runtime. All this reduces the time spent on manual troubleshooting and error detection. These benefits will positively disrupt the software testing process and we couldn’t be happier about that.

Emily Newton

Emily Newton is a technical and industry journalist. She regularly tells stories about how technology is changing the industry.


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