How AI-Powered Test Automation is Transforming Software Quality

Software testing has always been one of the most important stages in the software development process. It is of utmost importance for the companies to deliver bug-free software to their users.

With time, software have become smarter and more complex in order to cater specialized needs of the users. They often include distinct features to meet the growing demands of their users. This requires them to be integrated with multiple modules and create gateways to be able to get integrated with the working environment of the users.

All of these features require rigorous testing to be done on them so that they run smoothly once the users start using them. Manual testing was a solution for a long time when software used to be simple. But with the growing complexity, manually testing them became more and more difficult.

Even if the testers were able to test the software manually, it would require a lot of time and effort to be able to cover each and every aspect of the software. 

Enter automation – which solved most of the problems related with manual testing. Automated testing was able to reduce time and effort of the testing process to a large extent but it still depended on technical expertise of the testers to be able to write test-scripts along with extensive maintenance.

It was for sure a better option than manual testing but still not up to the mark until it matured into AI-powered automation. AI-powered test automation is the latest solution for software testing as it overcomes all the flaws the previous versions had.

Through this, companies are now able to accelerate their test cycles, lower maintenance efforts, cut costs and implement cross-collaboration between teams.

In this article, we are going to examine how AI-powered test automation benefits software development and the steps you can take to leverage it at its full potential.

What is AI-powered Test Automation?

AI-powered test automation is the process of using artificial intelligence and machine learning into the testing stage of software development. By using this kind of automation, companies can make their testing cycle more intelligent and adaptable to their software.

Unlike manual testing or traditional automation testing which depended heavily on pre-written test scripts, AI testing is able to make independent decisions during testing which help them conduct the testing process with advanced features and capabilities that are discussed below.

Automatic generation of test cases

Most of the AI test automation tools generate test cases through natural language commands. You just need to give instructions in plain language and the tool would generate the test cases automatically.

Sure, the context and the environment where this test case would run has to be set prior to giving commands, but once that is set, you can generate any number of test cases through simple commands. This means, you need not be a technical expert for testing your software no matter how complex it is.

Self-healing Tests

High-end software often go through frequent updates in their functionalities, business logic or UI. Traditional testing used to take a lot of time re-adjusting the test cases according to the latest updates in the software.

But with AI testing, you don’t have to worry about test maintenance. When there is a change in the application like a button or layout change, AI can detect it by itself and run the test-case on its own.

It’s because AI stores more information than just hard-coded labels. They store various other attributes of an element, like XPath, CSS selectors, text labels, position, size, and other metadata, which allow them to locate an element correctly even if its name has been updated.

Predictive Analysis

The testing process often goes through various iterations. Sometimes before release and sometimes after when there are software updates.

But all these scenarios get stored in the AI testing tool and can be used for further analysis. Through its predictive analysis feature, the AI testing automation tool can study the past results and code changes based on which it can identify the high-risk areas for further testing.

Through this identification it can prioritize its testing process and give valuable suggestions to the testers. Thus, an AI-powered testing tool acts as a testing companion for quality analysts.

The Role of AI testing in boosting Software Quality

When we think of the benefits of AI testing, the first points that come to mind are faster process and reduced cost and efforts.

Although these are the most beneficial features of AI testing, we must also know that AI testing has a direct impact on software quality as well.

The overall objective of testing a software is not just deliver a bug-free product but also to enhance the quality of the software that is essential to win more customers. Let’s see how AI for software testing enhances the software quality.

Better Test Coverage and Relevance

When we integrate an AI-testing tool with the software that needs to be tested, the AI examines the software at every nook and corner. It suggests even those areas that need to be tested which we might have missed during manual testing.

Also, by generating test cases through plain commands, it gives a greater liberty to the testers to think of all the possible scenarios that can be tested thereby increasing the coverage of the test cases.

Predicting Bugs Early On

Through its predictive analysis feature, the AI-testing tool can study the historical data and predict future bugs early on. This helps the software developers to improve the software quality by fixing issues that are most likely to occur in the future. Through this feature, the team can ensure that they release a product of much higher quality by foreseeing the bugs and fixing them even before they have occurred.

Better Consistency and Usability

Through visual and functionality tests, the tool can detect even the slightest change in the UI which cannot be caught by the naked eye.

Oftentimes the teams miss out on the smallest changes in the UI that have occurred during software updates but the tool helps you pin-point those changes and allows you to act upon them This ensures that the software has a consistent UI without any unwanted updates.

Final Thoughts

AI-powered test automation is no more an option for modern software development processes. Due to all its features, companies are realizing the immense benefits of adopting it into their testing process.

Features like self-healing, predictive analysis, automatic test case generation allow companies to reduce maintenance, costs, time and efforts of their testers.

By delegating the repetitive and low-value testing related tasks to the AI tool, testers have more bandwidth to enhance their testing process. With that, they have more time to work towards enhancing the quality of the software by leveraging the AI testing tool at its full potential.