When I first started my journey as a QA engineer, one of the things I quickly noticed was that our test scripts were messy, brittle, and hard to maintain. Each test case was filled with dozens of assertions, hard-coded locators, and repetitive code that made debugging a nightmare. I remember sitting through test runs that …
Page Object Model Done Right: Removing Assertion Overload Read More »
When I first started working as a tester, one of the biggest mistakes I made (and saw others make) was relying on hard-coded test data in our automation scripts. At the time, it seemed like the quickest way to get tests running: plug in a username, password, or account number directly into the script …
Killing Hard-Coded Data: The Centralized Test Data Strategy That Worked Read More »
Artificial Intelligence (AI) is no longer a futuristic concept — it’s the present driving force behind innovation, automation, and decision-making across industries. By 2026, AI is projected to create millions of new jobs worldwide as organizations adopt smarter systems to boost productivity and accuracy. Whether you’re a student, IT professional, or someone planning a …
Career Options in AI: 10 High-Demand AI Jobs in 2026 Read More »
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 »
