How AI is transforming software testing?

AI (Artificial Intelligence) and ML (Machine Learning) are the cool new kids on the block in tech. They are being integrated into many verticals and are impacting our daily quality of life. You can find many practical examples in existence such as Netflix’s movie recommendation, Amazon’s product recommendations, home automation, and even self-driving cars. In regards to software companies, more specifically, QA testers are wondering how AI is transforming software testing.

“In brief, AI is transforming software testing by not only automating manual tasks but learning of changes and automatically adapting. This helps save time which reduces costs. It can also point out signals for managers to make better data-driven decisions.”

According to Raj Subramanian, an expert speaker in the field, Artificial Intelligence is an area of computer science for building machines that can ‘think.’ Machine Learning is a subset of AI, giving computers the ability to learn without being explicitly told too. And finally, Deep Learning is one area of ML, which is a technology based on neurons like in the human body. Each neuron learns from another, reacting together in a neural network.

For example, think of these ‘neurons’ working together and learning like a sense of smell. Ever realize how you can smell something and it brings back a set of connected memories?

UI testing can be cumbersome because the user interface constantly changes. When you combine that with building test scripts, an automated solution makes for the more logical decision. And with many DevOps teams developing in fast agile life cycles, it is important not to have a bottleneck at the regression testing stage. This can delay the incremental releases of the product.

Regression test maintenance can become an issue in an ever-changing environment- especially at scale.

So, how can we alleviate pain points by making tests easy to maintain? We do it with AI-powered automated testing software such as Autify. With Autify, a QA tester can record a test case scenario and the software automatically transforms it into a script. If there is a change in the UI, the automation engine will automatically detect it for the tester and adapt test scenarios accordingly.

Regression testing ensures that older code and features still work while retesting the newly added code and functionality making sure it works as well with the existing code. Regression tests are necessary to ensure changes have not caused unintended adverse side-effects. In the illustration above, Regression testing can be split into these three principles:

1. Retest All – this technique requires the most time and resources which could consume more man-hours. It requires retesting all tests in the testing queue.
2. Regression Test Selection – instead of retesting the entire test stack. They can be split into categories: “Reusable Test Cases” or “Obsolete Test Cases.” The former can be reused in future test scripts, whereas, the latter cannot.
3. Prioritization Of Test Cases – literally means just that. This principle allows for selecting tests based on the highest priorities to the business use case(s).

One of the greatest benefits of regression testing, when executed properly, is ensuring a stable product and new feature releases are brought to market faster.

The other is cost savings which can benefit the organization. Recall the three testing principles above. Instead of retesting the entire application, a QA manager can select portions of the test bucket or suite to test faster and cheaper.

Maintaining Regression tests can become challenging, especially with frequent UI and functionality changes or at scale. It also can grow to be one of the most time consuming and resource-intensive portions of software development.

In current web UI technology, identifiers like ‘id’ and ‘class’ attributes are often easily changed by design and function. Changing these typically break test scripts. We have written a guide detailing how this can be problematic. If the DevOps team is dependent on manual human intervention, this can become costly. Hence, why AI and automation are necessary and changing the landscape for software testing.

AI testing can do many tasks that a human can do, repeatedly, and without tiring. However, the magic comes in machine learning algorithms that can detect UI changes. Instead of failing, they can recognize and recover to complete testing.

It can signal discovered changes for QA managers. Which can help make more informed data-driven decisions going forward.

There are some AI regression test SaaS in the marketplace today. When evaluating, it is important to choose one that is easy to maintain and leverages AI for automated testing. The ultimate goal is to release stable quality features to the product faster, saving time, and reducing costs associated with testing. Autify is the simplest and easiest AI-powered automated solution you can try. It also has multi-browser support. Request a demo for your test suite today!