Computer/Embedded Technology

Five truths about AI-driven software testing

23 November 2022 Computer/Embedded Technology AI & ML

Here’s the hard truth about traditional software test automation: it requires significant manual effort. It seems counterintuitive, but it’s true. There is a considerable amount of human intervention involved in what are considered to be ‘automated’ software test processes.

In a report titled ‘AI-driven testing: bridging the software automation gap’, author Tariq King describes the current ‘gap’ between manual and automated testing. King argues the best way to narrow that gap is to make use of artificial intelligence (AI) in the approach to test generation and execution.

Below is a summary of the top four lessons learned to close the gap between manual and automated testing.

Testing is more than checking.

A simple statement, but one that is a really important point to make. When test automation is considered, what is often thought of is test execution. However, there’s more to it than that. Testing, when done right, consists of learning, experimenting, troubleshooting, trial-and-error, observation, extrapolation, etc. These are complex, highly cognitive tasks that would typically require a human tester to manually perform them.

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 – which brings me to the next takeaway…

AI can test for things we previously thought 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, most of the developer requirements are qualitative in nature. Attributes like usability, accessibility, and trustworthiness can all fall under this category.

However, AI has shown us 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.

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 behaviour 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.

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

Traditional approaches to test automation consist of 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 states in the 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 essentially 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.

AI-driven test automation is not a thing of the future; it’s already here.

It was once thought tasks such as voice and image recognition, driving, and even musical composition were impossible for a machine to accomplish. But this type of automation is being seen today. Therefore, it shouldn’t be a surprise to know that AI bots are currently in training to perceive, explore, model, and test software functionality.

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

For the full report, visit


Share this article:
Share via emailShare via LinkedInPrint this page

Further reading:

E-Mobility: navigate safety, interoperability and conformance
Concilium Technologies Power Electronics / Power Management
Although the concept of electric vehicles is not a new one, the market remains in its infancy, and is not well-regulated or fully operational. This presents a number of challenges for manufacturers throughout the EV and EVSE ecosystem.

Embedded software development
Computer/Embedded Technology
The reliance on C is being reduced, with Python the language of choice for embedded applications in the fields of IoT and AI.

Bridging the gap between MCUs and MPUs
Future Electronics Editor's Choice AI & ML
The Renesas RA8 series microcontrollers feature Arm Helium technology, which boosts the performance of DSP functions and of AI and machine learning algorithms.

Microsoft Windows IoT on ARM
Altron Arrow Computer/Embedded Technology
This expansion means that the Windows IoT ecosystem can now harness the power of ARM processors, known for their energy efficiency and versatility.

Hardware architectural options for artificial intelligence systems
NuVision Electronics Editor's Choice AI & ML
With smart sensors creating data at an ever-increasing rate, it is becoming exponentially more difficult to consume and make sense of the data to extract relevant insight. This is providing the impetus behind the rapidly developing field of artificial intelligence.

Hardened-grade network switches
CST Electronics Computer/Embedded Technology
Lantronix’s hardened switches provide Layer 2 or Layer 3 networking, and are available as Power-over-Ethernet (PoE) or Power-over-Ethernet Plus (PoE+).

Switched mezzanine card for enhanced Ethernet connectivity
Rugged Interconnect Technologies Computer/Embedded Technology
The TXMC897 sets a new standard in high-speed Ethernet communication, with advanced features and flexibility.

Ryzen V3000 computer on module
Altron Arrow Computer/Embedded Technology
SolidRun has recently announced the launch of its new Ryzen V3000 CX7 Com module, configurable with the eight-core/16-thread Ryzen Embedded V3C48 processor.

1.6T Ethernet IP solution to drive AI and hyperscale data centre chips
Computer/Embedded Technology
As artificial intelligence (AI) workloads continue to grow exponentially, and hyperscale data centres become the backbone of our digital infrastructure, the need for faster and more efficient communication technologies becomes imperative. 1.6T Ethernet will rapidly be replacing 400G and 800G Ethernet as the backbone of hyperscale data centres.

Maximising edge computing
Computer/Embedded Technology
Senao Networks has announced its launch of its SX904 SmartNIC based on the Intel NetSec Accelerator Reference Design.