There is no question about it: the unprecedented rise of artificial intelligence in quality assurance has led to a huge shift in the industry, leading to an efficiency boost, acceleration of automation and a host of other benefits for many QA teams.
Many see AI simply as a force that does the work of people, only in a more efficient and cost-effective way. However, that is a somewhat limited viewpoint, according to Margarita Simonova, the founder and chief executive officer of the firm ILoveMyQA.
“Efficiency is not the only advantage of AI, it is also about creativity,” argues Vancouver, Canada-based Simonova. “Traditional automated testing has not necessarily been viewed as a creative endeavour,” she said.
“It is typically somewhat rigid, with predefined paths and predictable outcomes. But when AI is inserted into the process, a type of dynamic creativity is unlocked.”
Simonova stressed that AI in QA is not just about making things faster or easier, although it does both of those “brilliantly,” as she was quick to stress.
“It is about embracing a new way of creativity. By leveraging AI, testers can move beyond the mundane and routine to really innovate in the way testing is performed,” she pointed out.
The industry should embrace AI-led creativity in automated testing, Simonova noted, as it can create test scenarios, work as a creative assistant, can be perceived artistically and show what the future for collaboration holds.
Test scenarios
While it has been argued that AI is not about to replace QA professionals any time soon, there are many useful areas where AI is making strong inroads, Simonova continued.
“One of the main areas where AI is useful is in generating test scenarios. They require a lot of thought to outline the different ways in which an end user will use all the functionalities of a product, so it is an area in which AI has a lot of potential,” she explained.
AI can analyse vast amounts of user behaviour data to generate creative, real-world test scenarios that human testers might not have even considered,” Simonova added.
“So, how can this be accomplished? Picture a scenario where AI observes how users interact with a piece of software,” she continued. “After watching and learning, AI is able to add noise to that situation.”
For example, AI can click on areas of a software application that no one would have considered or send data that is formatted in unexpected ways.
“By adding these AI-generated test scenarios from user behaviour, more test scenarios than ever before can be created,” Simonova explained.
AI is also capable of continuous learning and adaptation. As it tests and retests, it learns from previous tests and user interactions.
“That lets it refine its approach and generate increasingly sophisticated scenarios. This dynamic process ensures that the testing is always aligned with actual user behaviour, making the software more user-friendly and resilient,” she noted. “This has positive effects on QA testers, who are free to work on higher-level tasks.”
Creative assistant
Another idea about AI is that incorporating it into the QA process takes us one step closer to the future of autonomous testing.
“From that viewpoint, we can see AI as a worker that performs its job non-stop, 24/7,” Simonova said. “But we can also see it as a copilot that works alongside a human QA engineer.”
As this type of creative assistant, AI helps QA engineers develop out-of-the-box solutions that come up with unusual behaviours and rare edge cases, she pointed out.
“AI tools can suggest new testing strategies and provide unique insights based on historical data.”
– Margarita Simonova
AI tools like ChatGPT and GitHub Copilot can help QA engineers brainstorm creative solutions to complex testing problems.
“Instead of simply following a script, these AI tools can suggest new testing strategies, provide unique insights based on historical data and even help design user-focused testing frameworks,” Simonova shared.
These types of ideas would ordinarily take a large cognitive effort from the engineer.
“But by handing this off to AI, the engineer can focus on other efforts. Engineers can spend more time analysing the reports that AI tools make,” she said. “From those reports, engineers can identify patterns and come up with solutions to address them.”
‘Artistic’ side of testing
Simonova argues that calling the output of AI “art” can be a controversial take. “But we don’t need to view AI in testing from a purely functional perspective,” she said.
“Instead, we can view it from an aesthetic angle. In this way, we can see that AI is like an artist that is continuously learning from its past to ‘craft’ better solutions.”
This artistry applies to how AI works with code, she explained. “AI can intelligently balance code quality with user experience, and since codeless automation tools are emerging as a game-changer, especially for teams that may lack extensive coding expertise, letting AI improve code can make a team much more effective.”
This can lead QA engineers to become curators who guide AI to refine a product’s performance, aesthetics and usability, Simonova pointed out.
“By viewing AI as an artist who is constantly improving their craft, we can see how its process of iteration is a noble pursuit of trying to reach perfection,” she stated.
“This blurring of the lines between artist and QA tester ultimately results in products that are more thoroughly tested and of higher quality.”
“AI creates test scenarios effortlessly while the QA team monitors reports and focuses on higher-level insights.”
– Margarita Simonova
In the foreseeable future, Simonova is convinced that AI and a QA team will be working alongside each other to ensure that a new CRM solution is bug-free and secure.
“AI creates test scenarios effortlessly while the QA team monitors reports and focuses on higher-level insights. All the while, AI is learning from each interaction and improving itself for the future,” she said.
“As we can see, AI and QA have a future that is intertwined.” In the future, Simonova sees that AI and QA teams are better able to collaborate in real time.
“That will let human intuition guide AI with its capacity for data-driven insights. With this synergy, QA will help ensure that products are innovative, user-friendly and reliable,” she stated.
“This is an exciting frontier where technology not only augments human capabilities but also redefines them, opening up avenues for creativity that were previously unimaginable. Welcome to the future of QA—where imagination meets automation,” Simonova concluded.
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