The approach toward software testing has drastically changed over the years. It has changed from manual testing to automation frameworks and now to AI-based testing.
It is not just about increasing productivity but rather how the software quality is maintained in a time of continuous deployment and unparalleled innovation, argues Srinivasa Rao Bittla, a senior development engineer at Adobe.
“The question is no longer ‘should AI be integrated into testing?’ but rather ‘how can AI be leveraged effectively to enhance quality, accelerate release cycles and empower engineering teams?’,” said Rao Bittla.
He added that, for tech leaders, the challenge is clear: How do you transition from traditional quality engineering (QE) to an AI-driven testing strategy while ensuring long-term sustainability?
Not merely automation
Rao Bittla stressed that many assume AI-driven testing is just a smarter version of traditional automation.
“That’s a misconception. This shift isn’t about replacing automation—it’s about evolving how we think about software quality.”
He added: “AI doesn’t just automate; it learns.”
Unlike traditional scripted automation, which follows predefined steps, AI can analyse patterns, self-heal broken tests and predict failures before they occur, Rao Bittla explained.
“This means testing is no longer just reactive—it becomes proactive and predictive,” he wrote in a recent analysis. “AI can test beyond human capabilities.”
He continued by saying that traditional testing teams work within predefined test cases, often constrained by resources and timelines.
“AI can generate thousands of test scenarios dynamically, covering edge cases that humans might miss. This results in higher accuracy, broader test coverage and more resilient software,” Rao Bittla observed.
“AI doesn’t just automate; it learns. It can enhance but not replace human testers.”
– Srinivasa Rao Bittla
A big fear around AI-driven testing within the industry is that it will replace QE professionals.
“The reality? AI removes the repetitive, mundane tasks so that human testers can focus on strategic thinking, exploratory testing and risk assessment,” Rao Bittla stated.
“To ensure long-term success, businesses must go beyond short-term fixes and focus on sustainable AI-driven quality engineering,” he stressed.
According to Rao Bittla, you do that by building a culture that supports AI adoption.
“The primary obstacle to AI in testing is not only technological in nature; the attitude is much more critical.”
First, teams have to understand what AI can do and how it integrates within existing QE practices and methodologies, he noted.
“By undertaking a proactive approach to train, mentor, and upskill, investment in AI facilitates the evolution of technology and the testers who utilize it,” Rao Bittla explained.
Secondly, it is vital to transform the way systems operate.
“This can seem daunting, but with that said, there are specific areas where AI can be integrated without losing ground in other processes.”
As examples, he singled out maintenance-friendly test automation, automated generation of test data, as well as predictive Defect Analysis.
“Once these AI implementations prove successful, adapt it across teams and then roll it out to the entire organization,” Rao Bittla said.
Moreover, balancing AI with human oversight is also vital, he stressed.
‘AI is powerful but not infallible. False positives, misinterpretations and contextual blind spots will arise,” Rao Bittla argued.
“Maintain a hybrid model where AI handles execution, but human testers validate findings and interpret results.”
AI explainability testing
“When it comes to, for instance, how AI is engaged in the testing phase, explainability is key,” Rao Bittla continued.
“It is important for the team to know how AI reaches different conclusions, detects the issues and handles any bias that might be present,” he said.
Transparency has AI testing bias; therefore, trust and accountability have become more reliable, he noted.
The true impact of AI in software testing is not about speed alone, Rao Bittla argues. ” It’s about building resilient, intelligent testing systems that evolve with changing software landscapes.”
For QE Professionals: AI should not be seen as a threat but an opportunity to expand the role into AI-assisted testing, exploratory analysis and strategic quality engineering, he continued.
Moreover, for tech leaders, AI adoption should prioritise long-term benefits, phased implementation, and ethical use to create a sustainable and future-ready testing strategy, Rao Bittla said.
Finally, for financial services firms, “the competitive advantage lies in leveraging AI for predictive testing, minimizing defects before release and accelerating innovation without compromising quality,” he said.
Rao Bittla calls the shift from traditional QE to AI-powered quality engineering “inevitable.”
“The question is, will you drive the change or struggle to keep up? The future of software testing isn’t coming—it’s already here. Lead it with AI.”
Finally, he argued that, to keep up with rapid growth, IT leaders should embrace a scalable, automation-first approach.
“They should also invest in cloud, AI-driven monitoring and DevOps to streamline workflows,” Rao Bittla said.
“And finally, they should foster continuous learning with training on emerging technologies, eliminate silos by promoting cross-functional collaboration and prioritize agile approaches and proactive problem-solving to stay nimble and keep on innovating in the long term,” he concluded.
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