Covering Disruptive Technology Powering Business in The Digital Age

Authored by: Anna McCowan, Software Engineer – Keysight Technologies

The truth about traditional software test automation is that it requires considerable manual effort. Now, that seems counterintuitive, but it is actually true. There is a significant amount of human intervention involved in what are generally considered as “automated” software test processes.

In “AI-Driven Testing: Bridging the Software Automation Gap,” author and chief scientist Tariq King, described the current “gap” between manual and automated testing. King claims that the best way to narrow that gap is to use Artificial Intelligence (AI) in the approach to test generation and execution. To access full report, you can download it here.

With that in mind, here are the top five lessons I have learned in as far as closing the gap between manual and automated testing is concerned.

1. Testing means more than checking.

When we think of test automation, what we often think of is test execution. But there is more to it than that. Testing, when done right, involves learning, experimenting, troubleshooting, trial-and-error, observation and extrapolation. All these are complex, highly cognitive tasks that would typically require a human tester to manually perform.

This is where AI comes in. Advances in AI and Machine Learning (ML) technologies have made it possible to optimise repetitive tasks, perform bug hunting and monitor change.

2. AI can test for things previously thought of as subjective.

A common roadblock on the path to automation is the inability for machines to handle some of the nuances of human perception. For user interface (UI) design, in particular, most of the developer requirements are qualitative in nature. Attributes like usability, accessibility and trustworthiness can all fall under this category.

However, AI has proven that machines are able to model patterns, workflows and tasks, including UI design testing. AI can test user interfaces, services and lower-level components and can evaluate the functionality, performance, design, accessibility and trustworthiness of applications.

“The truth about traditional software test automation is that it requires considerable manual effort.”

3.  AI-driven test automation requires very little maintenance for visual updates and redesigns.

The purpose of functional UI testing is just how it sounds: to confirm the functionality of an application’s UI. For most web and mobile applications, functional UI testing can be challenging when using testing frameworks that rely on the document object model (DOM). DOM-based element selectors make tests susceptible to breaking because the structure and behavior of the UI changes with each update.

AI—and, more specifically, a branch of AI known as “intelligent computer vision”—gives us the newfound capability to perceive and test anything with a screen. Using image-based analysis, AI bots recognise what appears on an application screen independently of how it is implemented. Without the need for DOM-based analysis, UI design changes do not result in excessive test script maintenance.

4. AI-driven test automation will increase both the level of test coverage and test speed.

Traditional approaches to test automation include manually creating test cases for each new feature or application. This can take weeks or even months to complete, and there is a high level of risk with manual test case creation. As King stated in his report, “Over time the test coverage required to validate the quality of your software product diverges from the engineering team’s ability to design and write test scripts for it.” Essentially, the software’s complexity is increasing faster than test automation can keep up.

AI-driven testing narrows that gap between software complexity and test automation. This is especially beneficial in the enterprise space, where modern business applications need end-to-end testing and where time-to-market cycles are continuously shrinking.

5. AI-driven test automation is already here.

We once thought tasks such as voice and image recognition, driving, and even musical composition were impossible for a machine to accomplish. But we are already seeing this type of automation today. Hence, it should not surprise you to know we are currently training AI bots to perceive, explore, model and test software functionality.

With all of the activity and buzz around AI for software testing, we are at the beginning of a new era of test automation. AI testing is enabling testers, developers and all software professionals to tackle challenges that were once thought to be insurmountable.

For a complete look at the expanding role that AI plays in software test automation, I highly recommend that you download King’s report, “AI-Driven Testing: Bridging the Software Automation Gap.”

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