Should You Use AI/ML in Software Testing?
Stephen Davis
Helping test professionals get best-of-breed software testing tools that increase productivity, consistency & coverage
The team at Calleo love to share our decades of testing and IT experience and have been doing this for many years via our blog Software Testing Insights .
Each month, we share the latest insights, testing news, tool updates and guides.
In this edition:
Should You Use AI/ML in Software Testing?
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.
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.
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.
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 Be Using AI/ML in Your Software Testing Already?
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:
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.
领英推荐
For example, UFT One AI can identify many types of search fields, user profile areas, input fields, buttons, shopping carts and more.
Don’t just take my word for it, why not see it in action?
Reduce SAP Defects & Cut Project Timelines, Without Any Extra Testers
When done inefficiently, SAP testing throttles progress and can take a project way over budget.
This is where automated testing, and increasingly AI-based automation, comes into play. A-based test automation allows you to test earlier and wider, and in less time with fewer resources.
Test Tool Checkpoint: Latest Software Versions
It can be hard to keep track of software updates and current versions.
Below we’ve listed the current releases of the industry leading Micro Focus test tools suite:
The perfect test management tool for traditional (e.g. waterfall) development methods.
Your ideal agile first test management.
Rapid and efficient cloud based performance testing.
Free IDE based performance testing.
On premises performance testing with huge support.
Global performance powerhouse for large companies.
Automation tool supporting the widest range of applications.
Your mobile testing toolkit including access to labs and virtual devices.
IDE based test automation.
In our next issue....
The True Cost of Test Tools
What are the long-term cost of open source and paid test tools?
In the next edition of Testing Times, we look at the long-term cost of open source and paid test tools. How do these options stack up over a 5-year period, and how can you make sure you get the most testing done with the least spend?
We’ll scratch beneath the surface to uncover if free really is free, and if paid tools are worth the license costs.