There is no question about it: generative artificial intelligence technology is rapidly changing the tech ecosystem as we know it, and the QA space is no exception.
Developers and software firms are rolling out new AI-powered solutions at unprecedented speed, ranging from app testing to mobile solutions and tools aimed at improving efficiency, speed and quality of tests.
Most of their clients, with many in the crucial financial services space, are more than keen to embrace this trend, racing to implement and integrate GenAI technology into their digital infrastructure.
“In the ever-evolving landscape of software engineering, tools like GitHub Copilot are fundamentally changing how developers use Generative AI in coding,” states Madhup Mishra, a senior vice president at SmartBear.
“This shift is especially evident in the software testing realm, where AI-generated code is starting to impact customer environments significantly,” Mishra wrote in a recent blog SDT post.
He called it “fascinating to observe” how the software engineering world is transforming with the advent of GenAI.
“It feels akin to watching a science fiction movie become reality. GitHub Copilot and similar AI tools are revolutionizing the way developers approach coding, injecting an element of excitement into their routines.”
“It feels akin to watching a science fiction movie become reality.”
– Madhup Mishra
Mishra’s own company is no exception.
SmartBear, a major software testing solution provider, recently developed a new AI-driven technology that will be implemented throughout its entire testing solution portfolio.
The launch means the company is further pushing into AI, following a range of new products and features in recent months.
The firm’s newest artificial intelligence powered-capability, called HaloAI, comprises of generative AI solutions that are designed to tackle complex, real-world problems.
Analysts across the board told QA Financial, following the announcement, they endorse SmartBear’s latest moves as the company’s artificial intelligence strategy is rapidly maturing.
“Interestingly, [the rapid rise of GenAI] may lead developers to engage even less in basic testing, a task many already find mundane,” Mishra argued.
“The prevailing sentiment is that developers prefer crafting and refining code over testing it.”
He pointed out that, in order to encourage the “shift-left” movement—which advocates for testing early and often—many test tool vendors are exploring Copilot-like methods for script-based test automation.
“They anticipate that developers will use these tools to generate early test scripts with GenAI assistance. This trend highlights how AI-assisted technologies can optimize workflows by automating routine tasks and suggesting improvements, perfectly aligning with the proactive shift-left approach,” Mishra wrote.
Real strength of AI
However, should we narrowly define GenAI-driven test automation as merely an extension of tools like Copilot for creating Selenium-like scripts? Mishra wondered.
“Such a view greatly underestimates the transformative impact of AI in quality assurance (QA) testing,” he stated.
“To truly leverage GenAI’s capabilities, we must expand our perspective beyond developer-centric models.”
Mishra argued that “while integrating testing earlier in the development process is beneficial, GenAI’s real strength lies in democratizing testing, fulfilling its core promise by enabling a broader range of participants, including manual testers, to effectively use no-code test automation tools.”
To Mishra, no-code test automation platforms are particularly promising. They empower individuals without programming skills, such as manual testers and business analysts, to actively participate in the testing process, he said.
“By incorporating GenAI, these platforms can now interpret plain language instructions to automatically create and manage test,” Mishra said.
He added that “this shift not only makes testing more inclusive but also enhances the quality and scope of software testing by integrating diverse perspectives. Analysts, understanding the intended use scenarios of an application, can now articulate these tests in plain language.”
This made Mishra wonder where this may signal a significant trend reversal from shift-left to shift-right?
“It’s too early to say definitively. Developers will likely continue to use tools like Copilot to improve unit testing and automate some aspects of it with AI-assisted scripts.”
Yet, given the projection shared by various analysts in recent months, including the recent IDC Directions 2024 conference—that customers will create one billion applications by 2028, many utilizing GenAI— “the need for quality assurance will intensify to maintain some level of quality in production environments,” Mishra said.
“It’s unrealistic to expect developer-led testing to keep pace with this shift.”
– Madhup Mishra
A shift-right approach may be necessary to Mishra, so augmenting developers with non-technical personnel to enhance QA with no-code test automation tools.
“This hybrid strategy can leverage both developers and QA team members using GenAI-driven copilots and no-code test automation platforms to create tests, bringing together the broadest range of technical and business insights.”
Mishra pointed out that GenAI-driven low-code test automation doesn’t just enhance the testing framework—it also fosters a collaborative environment where quality is everyone’s responsibility, breaking down traditional barriers between technical and non-technical roles.
“This culture of quality ensures that software not only meets technical standards but also closely aligns with user needs and business objectives,” he explained.
“Ultimately, while AI tools like Copilot are invaluable for boosting developer productivity in test automation, the future of test automation must prioritize inclusivity,” Mishra concluded.
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