I’m thrilled to sit down with Anand Naidu, our resident development expert at Brightgrove, who brings a wealth of knowledge in both frontend and backend development, as well as a deep understanding of various coding languages. Today, we’re diving into the evolving world of software testing, exploring how artificial intelligence is transforming the field, the unique value of human insight, and the innovative strategies that keep quality assurance ahead of the curve. Anand offers a firsthand perspective on managing global testing teams, integrating AI tools into daily workflows, and balancing technological advancements with the irreplaceable human touch. Let’s get started.
How did you get into software development and testing, and what does a typical day look like for you in your current role?
I’ve always been fascinated by how software can solve real-world problems, which led me to dive into both frontend and backend development early in my career. Over time, I found myself drawn to the testing side of things because ensuring quality is where the rubber meets the road. At Brightgrove, my days are a mix of coordinating with teams across different time zones, reviewing test strategies, and diving into code when needed. I’m often in meetings to align on project goals, troubleshooting issues with developers, or exploring new tools that can streamline our processes. It’s a dynamic role that keeps me on my toes.
Can you explain what the follow-the-sun model means for software testing and how it impacts the speed of development cycles?
Absolutely. The follow-the-sun model is all about leveraging global teams to keep work moving 24/7. When one region finishes their day, another picks up right where they left off. For software testing, this means we’re not waiting for a single team to come back online to address issues or run tests. It dramatically shortens feedback loops, so instead of waiting days for results, we can often turn things around in hours. This approach cuts down development cycles significantly, getting products to market faster without sacrificing quality.
In what ways has AI started to reshape the daily tasks of software testers in your experience?
AI has been a game-changer for us. It’s taken over a lot of the repetitive grunt work, like generating test data or updating automation scripts when requirements shift. Tools powered by AI can analyze massive amounts of data to spot patterns or potential issues before we even run a test. This frees up testers to focus on the bigger picture—designing complex test scenarios or diving into edge cases that require creative thinking. It’s less about manual labor now and more about strategic problem-solving.
There’s a lot of talk about AI potentially replacing jobs. How do you see it affecting the long-term role of testers?
I understand the concern, but I truly believe AI is making testers more valuable, not less. It’s not about replacement; it’s about augmentation. Testers who embrace AI can handle larger, more intricate projects because they’re not bogged down by routine tasks. Their role evolves into something more analytical and strategic. The key is adaptability—learning to work alongside AI tools and focusing on skills like critical thinking and user experience insight, which machines can’t replicate. Testers who do that will stay ahead of the curve.
You’ve mentioned human intuition as something AI can’t match. Can you share a specific moment where human insight made a critical difference in a testing scenario?
Definitely. I recall a project where the automated tests all passed, but something just didn’t feel right about the user interface during a manual review. A tester on my team noticed that while the functionality was fine, the flow felt clunky and unintuitive—like a user would get frustrated trying to navigate it. We dug deeper, ran some user simulations, and found a design flaw that AI hadn’t flagged because it wasn’t a ‘bug’ in the traditional sense. That human ability to empathize with the end user and think beyond the code saved us from a potential flop.
What are some practical AI tools you’ve integrated into your testing processes, and how have they improved efficiency?
We’ve been using tools like GitHub Copilot for writing and updating test code, which has been a huge time-saver. You can describe what you need in plain English, and it generates a solid starting point for scripts, cutting down hours of manual coding to minutes. Another tool in our arsenal helps with documentation—think structured test plans and bug reports that are clear and consistent. This reduces miscommunication across teams, especially when working globally. These tools don’t just save time; they let us focus on higher-value tasks.
With data privacy being a major concern for many companies, how do you address those challenges when adopting AI in testing?
Privacy is absolutely critical, especially for clients in sensitive industries. One approach we take is using open-source AI models that can run on our own servers rather than relying on cloud-based solutions. This keeps data in-house and under strict control. We also ensure that any AI tool we adopt aligns with our security protocols, anonymizing data where possible before it’s processed. It’s about finding a balance—leveraging AI’s power while maintaining trust through rigorous privacy measures.
Looking ahead, what is your forecast for the future of AI in software testing over the next few years?
I think we’re just scratching the surface of what AI can do in testing. In the next few years, I expect AI to become even more predictive—anticipating issues before they happen by analyzing trends across massive datasets. We’ll likely see tighter integration with development pipelines, where AI not only tests but also suggests code fixes in real-time. That said, the human element will remain vital for defining quality and ensuring user satisfaction. The future is a partnership—AI will handle scale and speed, but human judgment will steer the ship. I’m excited to see how this collaboration unfolds.
