How artificial intelligence and machine learning can revolutionise software testing

Over the years, Artificial intelligence (AI) and machine learning have come a long way. Currently, most of us are using AI and machine learning based applications in our daily lives.

AI and machine learning have already started revolutionizing software testing. As per the  World Quality Report, for QA and testing, user satisfaction is paramount and to achieve the same, it is essential to accelerate quality assurance processes. Here comes the role of AI testing framework and machine learning in software testing.

Let’s first understand the challenges of traditional testing

Earlier, software testing used to be a simple task. In a traditional approach, a tester used to go through the checklist to ensure the proper execution of potential users’ steps and actions. However, it was daunting for traditional testing methods to keep in sync with the changing requirements of customers.

The main issue was the sheer amount of data that software testers used to handle in a stipulated time frame. It alone emphasised on the need for a more relevant approach. It means the one that is powered by machine learning and artificial intelligence.

The inclusion of artificial intelligence and machine learning within the software development space will help QA testers to focus on pivotal tasks and leave the mundane tasks to smart algorithms.

Now, let’s ponder over the benefits of machine learning and AI based testing in software testing:

When you deal with thousands of lines of code, it is tough to test each line. Not only it will confuse testers, but will also take days or months to get through.

In short, manual testing is both time and resource intensive. No matter how efficient the tester is, it is difficult for the testing team to go through it swiftly.

By using artificial intelligence in testing, it becomes easy to write script, analyse large data and accelerate the entire testing cycle.

As smart algorithms can quickly sort through log files to ensure the code is correct, QA engineers have a comprehensive view of the changes which are required to be made from the inception.  

The more you test the code, the costlier it becomes. However, artificial intelligence and machine learning software testing curtail both time and money involved.

Whenever there is a change in code, AI-bots also evolve accordingly, which can help in identifying bugs more efficiently.  

The motive behind software testing is to detect and eliminate bugs in the system. However, even the most competent QA teams can miss detecting bugs due to paucity of time and the quantity of data involved. Testers can leverage AI to detect and eliminate bugs.

Another serious problem which is rampant in software testing is that understaffed companies focus more on product launches than bugs. However, this practice can hamper the company’s brand value in the long run. Now with machine learning software testing, it is feasible to get more reliable outcomes as against traditional testing. Moreover, its speed is unmatchable.

Conclusion

AI testing framework and machine learning software testing give testers the opportunity to comprehend their customers’ requirements better and work expeditiously to the changing expectations. In amidst of stiff competition, testers today have bulk data which they need to analyze in relatively less time. Tools like AI and machine learning help in addressing these challenges in a better way. A company can do it either through its in-house team or by outsourcing it to competent team. Either way, it helps in plugging the gap of traditional testing methods and expedite the entire process.

Image Credit : Malware Bytes

SHARE THIS

Get A Free Quote

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.