Automation Insights 101

Imagine spending weeks perfecting your app's UI.  The buttons are sleek, the layout's clean, and everything looks like it could win a design award. You go to bed feeling like a coding Picasso. Then… you wake up. Your buttons are misaligned. Your logo is somewhere in Ohio. And that "Sign Up" button? It's decided to explore a life of solitude. Welcome to the horror movie called Visual Regression,  where your UI goes rogue and doesn't text back. Enter AI: Your Pixel-Picking Sidekick Visual regression testing with AI compares snapshots of your app's UI over time, automatically detecting unintended visual changes like: A rogue font size tweak Padding that got a little too cozy Missing elements that got Thanos-snapped But instead of you manually comparing screenshots like a paranoid ex stalking your design system, AI handles it with laser focus and zero drama. How It Works (Without Making You Cry) Take a baseline scree...

Let's be real. Running all the tests, every time, sounds like a great idea… until you realize your test suite takes longer than the Lord of the Rings Extended Trilogy. Enter AI-based test case prioritization. It's like your test suite got a personal assistant who whispers, "Psst, you might wanna run these tests first. The rest? Meh, later." 🧠 What's the Deal? AI scans your codebase and thinks, "Okay, what just changed? What's risky? What part of the app do users abuse the most?" Then it ranks test cases like it's organizing a party guest list: VIPs (Run these first) : High-risk, recently impacted, or high-traffic areas. Maybe Later (Run if you have time) : Tests that haven't changed in years or cover rarely used features (looking at you, "Export to XML" button). Back of the Line (Run before retirement) : That one test no one knows what it does but no one dares delete. 🧰 Tools That Can Do This M...

Picture this: You're sipping coffee, dreading writing test cases. Suddenly, your QA buddy says, "You know you can just tell the AI what to do now, right?" You're like, "Wait… I can literally write: 👉 Click the login button 👉 Enter email and password 👉 Expect to see dashboard " And the AI's like, "Say less. I got you." 💥 BOOM. Test script = done. Welcome to the magical world of Natural Language Processing (NLP) Test Generation , where you talk like a human and your tests are coded like a pro. 🤖 What is NLP Test Generation? NLP Test Generation lets you describe tests in plain English (or whatever language you think in before caffeine), and the AI converts them into executable test scripts. So instead of writing: await page. click ( '#login-button' ); You write: Click the login button. And the AI translates it like your polyglot coworker who speaks JavaScript, Python, and sarcasm. 🛠️ Tools That ...

You know that one test in your suite? The one that passes on Mondays but fails every third Thursday if Mercury's in retrograde? Yeah, that's a flaky test. Flaky tests are the drama queens of QA. They show up, cause a scene, and leave you wondering if the bug was real or just performance art. Enter: AI-based QA with flaky test detection powered by machine learning. AKA: the cool, data-driven therapist who helps your tests get their act together. 🥐 What Are Flaky Tests? In technical terms: flaky tests are those that produce inconsistent results without any changes in the codebase. In human terms: they're the "it's not you, it's me" of your test suite. 🕵️‍♂️ How AI & ML Sniff Out the Flakes Machine Learning models can be trained to: Track patterns in test pass/fail history. Correlate failures with external signals (e.g., network delays, timing issues, thread contention). Cluster similar failures to spot root causes. La...

Let's face it: UI changes are like that one coworker who swears they'll stick to the plan… then shows up Monday morning with bangs, a new wardrobe, and a totally different personality. If you've ever maintained UI automation tests, you know the pain: One tiny change — a renamed id , a tweaked class name, or heaven forbid, a redesigned page — and BAM! Half your tests are failing, not because the feature is broken… but because your locators couldn't recognize it with its new haircut. Enter: Self-Healing Locators 🧠✨ 🧬 What Are Self-Healing Locators? Think of self-healing locators like the Sherlock Holmes of your test suite. When a locator goes missing in action, these clever AI-powered systems don't throw a tantrum — they investigate . Instead of giving up, they: Notice something's changed, Analyze the page, Find similar elements using AI and ML magic , And update the locator on the fly , so your test passes like nothing ever hap...

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...

The idea for this project started with a simple question: How can I create a personal workout AI that won't judge me for skipping leg day? I wanted something that could recommend workouts based on my mood, the time of day, the season, and even the weather in my region. This wasn't just about fitness—it was an opportunity to explore AI, practice web app engineering, and keep myself entertained while avoiding real exercise. Technologies and Tools Used To bring this chatbot to life, I used a combination of modern technologies and services (no, not magic, though it sometimes felt that way): Frontend: HTML, CSS, and JavaScript for the user interface and chatbot interaction (because making it look cool is half the battle). Backend: Python (Flask) to handle requests and AI-powered workout recommendations (it's like a fitness guru, minus the six-pack). Weather API: Integrated a real-world weather API to tailor recommendations based on live conditions (because nobody...

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...