If you’ve worked in QA long enough, you know that test data can make or break your automation efforts. It sounds simple: just feed your tests some input values and check the output. But in reality, creating and maintaining reliable test data is one of the hardest parts of building stable automation. When I started my journey as a tester, we relied …
How Faker + APIs Made Our Test Data Creation 10x Faster Read More »
As a QA professional, one of the most important aspects of my job is to provide clear, concise, and actionable evidence of what went wrong during test execution. In the early days of test automation, this was limited to screenshots. Screenshots captured what was on the screen when a failure occurred—useful, but often lacking critical context. Over time, …
Screenshots to Videos: The Evolution of QA Evidence Read More »
In 2026, Artificial Intelligence (AI) is no longer a buzzword—it’s everywhere. From your Netflix recommendations to AI chatbots, almost everything you interact with is powered by Machine Learning (ML) or Deep Learning (DL). They sound similar, but they’re not the same. Think of them as two steps of evolution in how machines learn—Machine Learning …
Machine Learning vs Deep Learning (2026): Key Differences Explained Simply Read More »
Artificial Intelligence (AI) and Machine Learning (ML) are not just tech buzzwords anymore — they’re career-defining paths in 2026. India’s AI job market has grown by over 40% year-on-year, with demand soaring across every industry — from finance and healthcare to testing and automation. Whether you’re a fresher exploring AI or a senior engineer …
AI & ML Engineer Salary in India 2026: Freshers to Senior Level Breakdown Read More »
In the world of QA, debugging can often feel like trying to find a needle in a haystack. Test failures happen for a myriad of reasons, and when those failures happen in complex environments, finding the root cause can feel like an endless chase. As a tester, I’ve dealt with my fair share of frustrating, seemingly random test failures. Sometimes it’s clear—an element isn’t found, …
HAR Files: The Underrated Debugging Tool Every QA Should Use Read More »
Introduction In the world of Quality Assurance (QA), decision-making often relies on evidence rather than assumptions. Whether it’s validating a new feature, analyzing defect trends, or confirming a performance improvement, QA engineers must rely on statistical thinking. That’s where hypothesis testing comes in — a structured approach to validate assumptions with data. This method, widely …
Hypothesis Testing in QA: Step-By-Step Guide With Real Examples Read More »
As a QA professional, one of the most frustrating and overwhelming experiences I’ve had is dealing with clueless deployments. I’m not talking about simple deployment errors or occasional misconfigurations; I’m talking about full-scale chaos when a deployment is pushed to production without proper communication, visibility, or coordination with the QA team. In those early …
The QA Nightmare of Clueless Deployments (And How We Fixed It) Read More »
In the ever-changing world of software development, automation testing continues to be a cornerstone for delivering high-quality applications faster. While new testing tools and frameworks emerge each year, Selenium still stands tall as the go-to choice for QA professionals and organizations worldwide. Even in 2026, with advancements in AI-driven tools and codeless testing platforms, …
Why Automation Testing with Selenium Is Still the #1 Choice in 2026 Read More »
Choosing between Data Science and Cybersecurity in 2026 can feel tough. Both careers are hot. Both pay well. Both matter to every industry. The real question is not “Which is better?” It is “Which is better for you?” Here’s a clear, no-jargon guide to help you decide. What each field actually does Data Science …
Data Science vs Cybersecurity 2026: Which Is Best for You? Read More »
Cyberattacks are faster, smarter, and harder to spot than ever. In 2026, the real shift is not more tools—it’s more intelligence. AI and Machine Learning (ML) are now the frontline of defense. They learn patterns, catch tiny anomalies, and adapt in real time. This is not a trend. It’s a new operating model for …
AI & Machine Learning in Cybersecurity: 2026’s Game-Changing Shift Read More »
