Automation Insights 101

AI is a fantastic tool for coding—until it isn't. It promises to save time, automate tasks, and help developers move faster. But if you trust it  too much , you might just end up doing extra work instead of less. How do I know? Because the other day, I did exactly that. The Day AI Made Me File My Own Bug I was working on a personal project, feeling pretty good about my progress, when I asked AI to generate some code. It looked solid—clean, well-structured, and exactly what I needed. So, in a moment of blind optimism, I deployed it  without testing locally first. You can probably guess what happened next. Five minutes later, I was filing my own bug report, debugging like a madman, and fixing issues on a separate branch. After some trial and error (and a few choice words), I finally did what I should have done in the first place:  tested the code locally first.  Only after confirming it actually worked did I roll out the fix. Sound familiar? If you've ever used AI-gene...

They say automation saves time, but have you ever spent three days fixing a broken test that was supposed to save you five minutes? That's like buying a self-cleaning litter box and still having to scoop because the cat refuses to use it. Automation in software testing is like ordering takeout instead of cooking—you do it to save time, but if you overdo it, you'll end up with a fridge full of soggy leftovers. Many teams think the goal is to automate everything, but that's like trying to train a Roomba to babysit your kids—ambitious, but doomed to fail. Instead, let's talk about smart automation, where we focus on high-value tests that provide fast, reliable feedback, like a well-trained barista who gets your coffee order right every single time. Why Automating Everything Will Drive You (and Your Team) Insane The dream of automating everything is great until reality slaps you in the face. Here's why it's a terrible idea: Maintenance Overhead: The more ...

Introduction Once upon a time in the wild, wild world of software development, programmers wrote code, deployed it, and prayed it worked. Spoiler alert: it often didn't. From debugging literal moths in the 1940s to AI-driven quality assurance in the 2020s, the evolution of Software Quality Assurance (QA) has been one rollercoaster ride of broken code, existential crises, and heroic testers saving the day. But here's a fun fact many QA engineers learn way too late in their careers : There are dozens of different job titles for people who do testing! Many QA engineers spend a decade in their company's test silo, breaking things, filing bug reports, and perfecting their "This is fine" face—only to find out later that their role could've been called Software Development Engineer in Test (SDET), Automation Architect, Quality Evangelist, or even AI Test Engineer somewhere else. So, grab some popcorn (or a stress ball if you're a QA engineer), a...

Once upon a time, in a land filled with legacy test frameworks and stale documentation, a brave automation tester (me) decided to embark on an epic quest: Setting up Playwright. Did I have experience with Playwright? Nope. Did I care? Also nope. Did I have AI by my side? Absolutely. Why Even Try? Look, as an automation tester, I tend to stick with what works. I mean, if a tool runs my tests, why mess with it? But every now and then, an opportunity arises to experiment with something new—whether out of necessity, curiosity, or sheer boredom. This time, Playwright caught my attention, and with AI as my trusty sidekick, I was off to the races. Step 1: Let AI Do the Heavy Lifting Back in the olden days (aka pre-AI times), setting up a test automation framework meant: ☠️ Digging through outdated documentation 💀 Copy-pasting error messages into Google ⚰️ Watching my soul leave my body as I debugged for hours But this time? I outsourced my brainpower to AI. Here’s what I asked it to d...