How AI Transforms Agile Software Testing

Agile methodologies have changed how we get work done. In the software development space, this change-driven approach transformed how we developed and tested software and mobile applications.

Unlike frameworks like Scrum or feature-driven development, Agile is more of a mindset. It incorporates the best principles of different development philosophies and focuses on the people doing the work.

This approach makes collaboration within small groups and across different teams more productive and highly efficient. When the focus is on working together and following best practices, individual contributions also become more effective.

However, with the rising demand for rapid delivery and more accuracy, Agile can create an unrelenting high-pressure environment that can quickly become overwhelming.

To overcome this hurdle, the industry started leveraging big data and analytics and smart algorithms to extend the scope of automated testing on software development projects.

With artificial intelligence and machine learning in the equation, even non-technical staff can support Quality Assurance (QA) teams with a high level of sophistication and capability. According to the practicing QA providers a1qa, this approach helps accelerate Agile development and deployment during each production stage.

AI in Agile Software Testing: A Few Use Cases

AI algorithms are programmed to learn from data. This enables them to make better decisions over time. So what kind of tests can AI engage in? Let’s take a look.

Unit Tests

Unit testing starts with setting up a robust and stable testing environment. With robotic process automation, your development team can minimize the number of peculiar test cases and better maintain unit test protocols.

User Interface (UI) Tests

Automation of UI tests can be achieved with AI-powered tools that can run test cases on multiple browsers and platforms and learn from functional flows.
For example, AI is now used widely in object application categorizations for all types of UIs. In this scenario, controls are recognized and categorized when new tools are developed, so testers can pre-train controls that are often seen in out-of-the-box setups.

Once the hierarchy of the controls is established, QA teams can develop a technical map to help smart algorithms look at the graphical user interface and label different controls.

As you can see from the above, this approach can make tests more reliable, significantly reduce time to market, and minimize maintenance efforts. But you’ll need a team to oversee automated testing protocols to negate any potential errors that might come up during each iteration.


AI-Powered Test Automation Tools

Some of the popular AI-based testing tools used by Agile-driven software development teams are as follows:


Applitools is designed to mimic the human eye and mind. These smart algorithms can instantly analyze and visualize application pages and report the differences. It can save development teams a significant amount of time and resources by automatically identifying the similarities and differences across product pages, break down the images and layouts, and run your application across devices, browsers, and operating systems.


Appvance leverages smart algorithms to develop test cases. These test cases are based on user behavior and mimic what the actual end-users of the product will do on the system. This tool is equipped with an extensive portfolio of tests that are highly user-centric and comprehensive. So leveraging Appvance can help negate the need for patches and potential brand damage after the product is released.


Functionize automates functional testing. This ML-powered tool helps rapidly develop tests without scripts. You can engage in continuous tests concurrently and perform in-depth analysis to ensure consistency and reliability.


mabl is a popular testing automation tool that helps you test the quality, UI, user experience and performance of your application at scale. This enables product owners to start creating tests and fix problems within minutes.

For mobile test automation, there is This tool is designed to perform regression testing, which can help enhance the performance metrics of your application. Although it’s similar to functional testing tools, is more of a monitoring solution.

Summing Up

The key benefits of incorporating AI in your Agile testing approach are the following:

  • Accelerating time to market
  • Boosting productivity
  • Expanding the test coverage
  • Improving testing accuracy
  • Overcoming the limitations of manual testing
  • Saving time and resources

The only disadvantage of automated testing is the fact that the ML algorithms will only be as good as the data they work on. When developing AI testing models, a conscious effort must also be made to eliminate human bias. It’s also important to note that minor changes can have a significant impact on the overall execution and results.

AI-driven testing is now essential to meet the rising demands of the marketplace. It’s critical to achieving greater operational efficiency and accuracy as well as to creating truly robust software products.

About the Author


Maxim Chernyak is Head of Test Automation and Performance Testing Lab at a1qa, an expert in test automation methodologies and tools for functional and nonfunctional testing. Accountable for the education and adoption of state-of-the-art quality engineering practices by QA teams
Find out more about @maximchernyak

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