How AI and ML Can Improve and Revolutionize Software Testing?

Artificial Intelligence works on the concept of training a machine to get desired outputs. When a particular intelligence of making decisions or performing certain tasks is imparted to a machine, it is said to become artificially intelligent. Companies around the world are leveraging the technology in multitudes of verticals and business operations, as the technology knows no bounds. With AI, there is an immense potential to transform and revolutionize almost every facet of a business.

In the area of software testing, artificial intelligence is imparting automation to testing processes, thus improving the efficiency of software testing methods and reducing the time to test an application, while also ensuring better tests and top-notch quality. With Machine Learning, a subset of Artificial Intelligence, companies are training machines to learn and adapt to the training environment, and then work to understand the patterns and respond effectively to further queries of the same kind.

A Machine Learning algorithm develops through the training cycle and then yields in its operational phase. When it comes to software testing services, companies have realized that a quick software turnaround time can be made possible with not only improving the development efficiency but cutting down on the immense time required to test an application.


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Bugs and Beyond – Software Testing with AI and ML

With software testing using ML and AI, companies are not just focusing on finding bugs, but wish to move a step ahead with fixing them too. Since long, companies perceive that the goal of software testing is to see to it that a software product works just as it was supposed to.

Some researchers disagreed, and are now trying to use a combination of Natural Language Processing and Statistical Analysis of open-source code that is available online to not just find a bug in a software application but automating the software testing tool to fix the bug too. Microsoft Research, with Peking University and University of Electronic Science and Technology of China The focus here, is to make software testing not a generalized piece of work that a person who has no knowledge in software development can also perform.

With this aim in mind, AI and ML and their subset technologies such as NLP are being leveraged to create software testing processes that will lead to better outcomes and faster results. It is important to note that companies, both start-ups and large corporations are moving towards better ways to test their products in a bid to save significantly on cost and time.

Predictive QA with AI and ML

Companies who are serious about transforming their software development processes are using advanced testing methods to assess their products. Defect management and test automation are leveraged to create better testing models that predict fails and loopholes within a system proactively, while the development is underway.

With this predictive testing methodology, a lot of time and efforts are saved as the developer comes to know about a defect before he has moved on to the testing phase. Companies are testing not just their end products but their complete product development lifecycles to look for white spaces in the process and to replace any lagging tasks with utmost ease and efficiency. Machines have started learning a lot. As we feed data to the machines, they are able to map patterns and gain insights on the purpose of the software program, thereby getting empowered to do the same when a new set of values are fed as input.

You must have to watch this video on “Automated Software Defect Prediction Using Machine Learning


Companies such as Facebook, Amazon, and Netflix employ ML algorithms to display personalized content and recommendations to each buyer or viewer, resulting in higher use of their platforms and exceptional customer services. You may like this Tweet:

Software Testing Services

According to a study, about 60 percent of software testing is repetitive in nature, and AI-enabled tools are in a better position to analyze these tests faster and with improved efficiency.