Bugged_But_Haapy

“Which testing framework should I use — Selenium, Cypress, or Playwright?” It’s still one of the most searched queries in 2025. Instead of a generic answer, here are my detailed notes, comparisons, and observations based on research and real-world use cases. Selenium (the enterprise veteran) Selenium is the old guard of automation — reliable, well-known, and still widely used in 2025. Why people still use it: Supports multiple languages (Java, Python, C#, Ruby, JS) and almost all browsers, even IE for legacy apps. Huge ecosystem with tutorials and integrations. Weaknesses: Setup can be complex, execution speed is slower, debugging can be painful. 2025 fit: Best for enterprises or teams that must support legacy systems and multiple languages. Cypress (the dev-friendly choice) Cypress made testing faster and more developer-friendly, especially...

Automation is one of the hottest topics in 2025 — but what exactly are people searching for? I explored Google Trends and SEMrush to find the most searched automation keywords this year, and here’s what stood out. Step 1: Checking Google Trends I started by plugging common automation terms into Google Trends: “automation trends 2025” → Huge global search volume, especially in January and after big tech events. “hyperautomation” → Flat for months, then spiked after Gartner’s latest report. “agentic AI” → Barely visible a year ago, but rapidly rising in the past six months. “automation testing tools” → Steady, but shifting toward tool comparisons like Selenium vs Playwright. Observation: Agentic AI is clearly an emerging trend. Old favorites like Selenium remain steady but are slowly being challenged by newer tools. Step 2: Using SEMrush fo...

Automation in 2025 is evolving faster than ever. From AI-driven workflows to collaborative robots in factories, people across the world are searching for ways to save time, cut costs, and improve accuracy. Here’s a breakdown of what’s trending globally—and why it matters to you. 1. AI-Driven Automation & Agentic AI Until a few years ago, AI in automation was mostly about predicting outcomes or flagging issues. Now, in 2025, the buzz is all about Agentic AI —systems that can plan, adapt, and act without needing step-by-step instructions. For example, in software testing, Agentic AI can automatically identify gaps in coverage, design test cases, and execute them without human prompts. In customer service, it can resolve support tickets by combining multiple backend processes on its own. Why people search it: Curiosity around whether AI can “replace” testers and operat...

Everyone’s racing to ship something “AI-powered.” That speed is exciting—until something breaks. That’s the AI Speed Trap : shipping faster than you can test, then paying for it in outages, bugs, and angry users. In this post Let’s Talk About the AI Hype What Exactly Is the AI Speed Trap? Why This Topic Went Viral Speed vs. Stability How Teams Can Avoid the Trap Case Studies The Hard Numbers A Quick Story Wrapping It Up FAQ Let’s Talk About the AI Hype Right now, every boardroom is echoing the same phrase: “We need AI in our product.” Startups and enterprises alike are racing to add “AI-powered” labels to features. From code assistants to AI-driven customer service, the hype is everywhere. But hype alone doesn’t equal value. Gartner research shows that 80% of executives believe AI is critical for succes...

Human + AI = The Next Generation of QA Engineers Quality Assurance has always evolved with the software we build. We moved from purely manual checklists to automation frameworks, from sporadic releases to CI/CD pipelines, and now we’re stepping into an era where human judgment teams up with artificial intelligence . The result is not about fewer testers—it’s about stronger testers : professionals who wield AI to design smarter tests, predict failure patterns, reduce flaky noise, and measure quality where users actually feel it. Table of Contents Why Now: The Forces Reshaping QA The Human + AI Collaboration Model Five Case Studies: AI in Action AI Testing Tool Comparison (2025) Practical Workflows: From Idea to Pipeline New Metrics for an AI-First QA Practice Skills & Learning Path for Next-Gen QA Risks, Ethics & Guardrails Quick FAQs Conclusion & Action Checklist ...

Software testing has never been a static field. Every five years, the way we test software feels completely different from the last. 2025 is no exception. Today, QA is no longer only about finding bugs at the end. It’s about ensuring quality at every step , making testing faster, smarter, and more integrated with development. In this article, I’ll take you through the most important testing trends in 2025 , not as a list of buzzwords but with real-world examples, case studies, and lessons I’ve learned working with multiple QA teams. Whether you are a manual tester, automation engineer, or aspiring QA lead, these insights will help you adapt and stay ahead. 1. AI-Powered Testing: Moving Beyond the Buzz AI is no longer just hype in QA. Tools like Testim, Mabl, Functionize , and Applitools Eyes are already being used in production teams worldwide. But what does this really mean for testers? Case Study: Retail Checkout Failures I once worked with a reta...

🚀 How AI is Changing QA: A Tester’s Story You’ll Relate To 🚀 How AI is Changing QA: A Tester’s Story You’ll Relate To I still remember my early days in QA. Running the same regression suite again and again. Fixing broken Selenium locators every time the dev team changed a button name. Spending hours staring at logs to figure out if the failure was in the code, the environment, or just a flaky test. Sound familiar? 😅 That’s the reality many testers live in today. But here’s the twist: AI is quietly becoming the teammate we didn’t know we needed. Let me show you how. 1. 📝 Smarter Test Case Design — Without Guesswork Back in 2018, I worked on a retail app where checkout was always buggy. But our test cases treated checkout the same as other flows. Result? Production bugs. Now imagine AI analyzing production logs and telling you: 👉 “Hey, 70% of user complaints come ...

No-Code & Low-Code AI Tools in Software Testing: The 2025 Deep-Dive (Practical & Human) If you’ve ever wished you could automate more tests without spending weeks building a framework, you’re exactly who this guide is for. In 2025, No-Code and Low-Code AI-assisted testing platforms have matured enough to help teams ship fast and keep quality high—provided you use them wisely. This article is a complete, plain-English playbook: what works, what doesn’t, and how to make these tools pay off in the real world. In this guide: What No-Code & Low-Code mean (in testing) How AI self-healing really works Tool landscape: strengths & trade-offs Hands-on examples (UI, API, data) 90-day adoption plan you can copy 12 best practices to reduce flakiness ROI math for leadership Security, compliance & accessibility CI/CD integration & metrics that matter Future tren...

Ever been awake at 3 AM debugging a flaky test that only fails on Fridays? You are not alone — and you won't be, for long. The nocturnal grind of software testing is part ritual, part ritual humiliation. Testers stare at logs, wrestle with flaky environments, and rewrite brittle scripts while the product they support evolves in ways that make last week's work obsolete. For years automation promised escape: write once, run forever. In practice, automation often felt like a treadmill — fast but exhausting. Enter ChatGPT-5. This isn't just a speed bump in tooling; it's a reconceptualization of testing as a dialogue between human intuition and machine-scaled pattern recognition. It means tests that adapt, triage that narrows to the truth, and exploratory work that focuses on human-centric risk. It means fewer late-night firefights — and m...