I’m thrilled to sit down with Anand Naidu, our resident development expert, whose proficiency in both frontend and backend development offers a unique perspective on the evolving landscape of software testing and quality assurance. With a deep understanding of various coding languages, Anand brings invaluable insights into how testing roles, continuous quality, and emerging technologies like AI are shaping the industry. In our conversation, we explore the changing responsibilities of testers, the challenges of measuring quality effectively, the integration of AI in testing workflows, and the barriers to achieving seamless quality in today’s fast-paced development environments.
How do you see the role of software testers evolving in today’s rapid software development cycles?
The role of software testers is definitely shifting. With the pace of development accelerating, testers are no longer just the gatekeepers at the end of the process. They’re becoming more integrated into the entire development lifecycle, often collaborating early on to define requirements and prevent issues before they arise. I’ve seen testers gain more influence in decisions like release readiness, which is a big step forward. However, in some organizations, they’re still stuck in purely execution-focused roles, which can limit their impact on overall quality. When testers are sidelined from strategic input, it often leads to a reactive approach—fixing problems after they’re found rather than preventing them.
Who do you think should ultimately be responsible for continuous quality in a software project?
I believe continuous quality has to be a shared responsibility. Testers bring specialized expertise, but developers, product owners, and even designers need to own quality at their respective stages. If it’s just on testers, you’re bottlenecked at the end of the cycle, and that’s not sustainable in today’s agile environments. Organizations can balance this by fostering a culture where quality is everyone’s job—think developers writing unit tests, peer reviews, and testers focusing on exploratory testing. Dedicated testing roles are still crucial for deep analysis, but distributing ownership ensures quality is baked into every step.
What are your thoughts on how testing teams are evaluated for success in most organizations?
Honestly, the way testing teams are measured often misses the mark. Most metrics focus on things like test coverage or defect counts, which are important but don’t tell the whole story. These numbers can make a team look productive without showing if the product actually meets user needs or business goals. I think it’s a shame so few teams are tied to outcomes like customer satisfaction scores. Bridging this gap means aligning testing goals with business impact—maybe tracking how quality improvements correlate with user retention or revenue. It’s a mindset shift, but it’s necessary to prove testing’s strategic value.
How much trust do you see among developers and testers when it comes to using AI tools for quality assurance?
There’s a mixed bag of trust right now. Many developers and testers appreciate AI for automating repetitive tasks, but there’s definite skepticism when it comes to critical areas like deployment or monitoring. I’ve noticed hesitation, especially when AI outputs are close but not quite right—debugging those can be a headache. Building confidence requires starting small, maybe using AI in low-risk areas first, and ensuring there’s transparency or at least human oversight. Over time, as teams see consistent results, trust will grow, but it’s not there yet for high-stakes tasks.
What do you consider the biggest obstacles to achieving continuous quality in software development today?
One major hurdle is that many existing tools and frameworks aren’t built for the speed of modern CI/CD pipelines. Integration is often clunky, leaving QA teams spending too much time maintaining scripts instead of validating new features. I’d say some teams spend upwards of 50% of their effort just keeping tests up to date, which is a huge drain. On top of that, there’s a cultural challenge—getting everyone to prioritize quality over speed. Until tools evolve and mindsets shift, continuous quality will remain more of an ideal than a reality for many organizations.
What challenges have you encountered or observed when adopting AI in software testing?
Adopting AI in testing comes with a steep learning curve and some resistance. Teams are often used to having full control over test suites, so trusting AI-generated tests feels like a leap. Then there’s the opacity—AI can be a black box, and not understanding why a test was created erodes confidence. I’ve also seen infrastructure issues; AI tools often need robust data and logs to work well, which isn’t always available, especially in legacy systems. Starting with pilot projects in non-critical areas and maintaining a hybrid approach with human review can help ease these pain points.
What is your forecast for the future of quality responsibility in software development?
I see quality responsibility moving toward a hybrid model where it’s truly distributed across teams. Automation, especially with AI, will reduce the manual burden on testers, letting them focus on strategy and exploratory work. At the same time, developers will take on more unit and integration testing as part of their workflow. The future will be about collaboration—dedicated quality experts guiding the process while embedding quality thinking into every role. Success will hinge on aligning metrics with business outcomes and investing in tools and skills that support this shared ownership.
