Artificial Intelligence (AI) and Machine Learning (ML) are the talk of the town. The rise of ChatGPT has catapulted these technologies firmly into the mainstream, and you’re increasingly likely to hear them mentioned in everyday conversations at the pub or the gym.
But while these latest developments have taken the world by storm, more nuanced applications have been bubbling under the surface for years.
Far away from the cultural limelight, test automation AI has been steadily making gains and is now a well-established testing approach that can reduce testing time and effort, scripts that heal themselves, while increasing test coverage. It’s fair to say that if you aren’t feeling the impact yet, you soon will be.
In this article, we will explore the benefits and challenges of using AI/ML in software testing, and help you decide if your organisation should be using them already.
The Current State of AI and ML in Testing
Artificial intelligence (AI) and machine learning (ML) are rapidly changing the face of software testing. In recent years, these cutting-edge technologies have made significant strides in automating testing processes, increasing efficiency, and improving software quality. As a result, many companies are already using AI/ML in their software testing processes.
By far the most mature and real-world uses of AI and ML are assisted scripting, self-healing test automation scripts and cross-browser testing.
- Assisted Scripting with AI makes it much easier to create and maintain automated scripts. It also makes scripts more readable and consistent.
- Self-healing test automation is the process of automatically identifying and fixing any issues that arise during the test execution process. This can be done using AI algorithms that can analyse the test results and identify the root cause of any failures. Once the root cause is identified, the AI algorithm can then generate a fix and apply it automatically, without the need for human intervention. This can greatly improve the speed and efficiency of the test automation process.
- Cross-Browser testing means that your scripts can operate seamlessly across different mobile devices, browser types, and even different operating systems.
As mentioned above, these aspects of AI/ML are mature and already deployed across thousands of businesses worldwide. They’re no longer theoretical or edge cases. There is no doubt that, where implemented correctly, these technologies make automation easier, faster and more robust.
As well as the technologies mentioned above, there are additional, less mature, and more theoretical AI/ML use cases that we won’t cover in this article. Today we’re purely focused on the here and now, and whether you should adopt proven AI/ML-based software testing technologies.
The Benefits of AI/ML in Software Testing
Hopefully, we’ve adequately set the scene and explained what is currently available. So the next logical step is to ask, why should you be interested? What can these technologies do for you?
Here are some of the key benefits you can expect to see from AI/ML-based software testing:
- Simple Script Development and Maintenance: One of the most significant benefits of using AI/ML in software testing is increased simplicity. AI/ML makes scripting easier and faster than ever before.
- Improved Productivity: By quickly automating your testing processes, you allow your testers to focus on more complex tasks
- Increased Speed to Market: This results in faster testing cycles, reduced time to market, and more efficient use of resources.
- Improved Accuracy: AI/ML-powered automation scripts can execute tests as often as needed, without the possibility of human error. They can detect even the most subtle defects in software, making them highly accurate. This means that software testing is more thorough and reliable, which helps you stop defects from slipping through the cracks and causing issues down the line.
- Cost Savings: By automating testing processes and improving accuracy, AI/ML can help organisations save money in the long run. As I always say, when it comes to testing, people are the real cost. Manual testing is time-consuming and expensive, whereas AI/ML-based software testing can be performed quickly, out-of-hours, behind the scenes, and with fewer resources.
The Challenges of AI/ML in Software Testing
Interestingly, AI/ML-based software testing presents fewer challenges than traditional software automation.
As with test automation, you will need to make sure you have the right testing processes in place before you start automating with AI/ML. You’ll need to know your processes and have some sort of requirements coverage matrix in place, also understand defect and QA workflows, all the standard stuff that goes into making a successful testing project.
Apart from that, the biggest challenge with AI/ML in software testing is making sure your resources have the correct knowledge and skills. However, these days there is an abundance of education and information resources available online. Plus, because AI/ML makes things easier, there’s less to learn than with traditional automation.
If we take UFT One for example, there is a dedicated video section of the Micro Focus website full of useful tutorials and information.
Should You Use AI/ML in Your Software Testing?
As discussed, AI/ML is transforming software testing, offering significant benefits in terms of efficiency, accuracy, and cost savings.
If implemented correctly, AI/ML can revolutionise software testing, leading to higher-quality software and better user experiences. But is it right for you? And how can you decide?
Is AI/ML right for you?
Well, before deciding whether to use AI/ML in testing, you should consider your needs, capabilities, and resources.
Questions to ask yourself when considering AI/ML:
- Are you struggling to quickly release software?
- Would you like to reduce the time spend creating automation scripts and maintaining them?
- Do you spend too much time testing?
- Would you like to “do more with less” – to either save money or achieve more with the same budget?
- Are you testing complex systems?
If the answer to these questions is yes, then it is time to explore using AI/ML in software testing.
See A Demo of AI and ML-based Test Automation
As a next step, why not arrange a demo of AI/ML in action?
OpenText (formerly Micro Focus) UFT One uses AI to identify objects visually, based on a wide variety of images, context, and sometimes text. This leading test automation tool has incredible AI and ML features that enable your tests to interact with the application you are testing in the same way a person would.
UFT One AI can identify many types of search fields, user profile areas, input fields, buttons, shopping carts and more.
- Easier to edit: Test scripts are more intuitive.
- Test multiple environments: Tests are technology agnostic so you can use the same script to test different browsers or mobile devices, identifying objects visually, regardless of the technology details used behind the scenes.
- Test resilience: Tests are easier to maintain, as an object changing location, framework, or even shape, won’t break the test script as long as the object remains visually similar, or its purpose remains clear.
Don’t just take my word for it, why not see it in action?
