If you’re a beginner QA automation engineer or SDET (or want to become one), understanding how code goes from a developer’s laptop to production is a key skill to grow beyond just “writing tests”. In this guide, I’ll walk step by step through the software release process: • What a CI/CD pipeline is • What happens during the build–deploy–test cycle • Where QA fits into each stage By the end, you’ll have a clear picture of how releases work and how this knowledge helps you design better, more reliable automated tests. ──────────────────────────────────────── 🟢 There are many ways teams release software, but most of them are just variations of two main approaches ➤ 𝐌𝐚𝐧𝐮𝐚𝐥 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: Developer writes code → someone manually builds the app → someone manually deploys it → someone manually runs tests. Slow, error-prone, and hard to repeat the same way every time. ➤ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: Developer pushes code → Jenkins/GitHub Actions automatically builds → Automatically deploys to QA → Automatically triggers your test suite. Fast. Consistent. Reliable. Companies automate these repetitive tasks so they can release software faster with fewer bugs. Understanding this pipeline is essential if you want to move beyond writing basic test scripts and be treated as a real QA Automation Engineer / SDET. ──────────────────────────────────────── 🟢 𝐓𝐇𝐄 𝐓𝐇𝐑𝐄𝐄 𝐒𝐓𝐀𝐆𝐄𝐒 𝐎𝐅 𝐒𝐎𝐅𝐓𝐖𝐀𝐑𝐄 𝐑𝐄𝐋𝐄𝐀𝐒𝐄 Every software release follows the same pattern. Tools like Jenkins, GitHub Actions, and GitLab CI/CD automate these stages so releases happen in minutes, not hours: ┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈ 𝟏. 𝐁𝐔𝐈𝐋𝐃 𝐒𝐓𝐀𝐆𝐄 📦 (𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐝𝐞 𝐈𝐧𝐭𝐨 𝐒𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐃𝐞𝐩𝐥𝐨𝐲𝐚𝐛𝐥𝐞) Raw code can't just run on a server. It needs to be built into an artifact - a packaged, executable version of your application. 𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐭𝐚𝐠𝐞: ⟩ Dependencies get installed - External libraries and frameworks your code needs (npm install, pip install, Maven dependencies) ⟩ Code gets compiled and packaged - Source code becomes executable format:

📌 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗷𝗼𝗯? 𝗥𝗼𝗹𝗲: QA Automation Engineer - Playwright/Python (Remote) 𝗖𝗼𝗺𝗽𝗮𝗻𝘆: Via CyberCoders (recruiting firm) 𝗣𝗮𝘆 𝗥𝗮𝗻𝗴𝗲: $100,000 – $150,000 + Bonus 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Remote (New York, NY based) 𝗦𝘁𝗮𝗰𝗸: Playwright, Python, API testing, E2E automation 𝗔𝗽𝗽𝗹𝘆 𝗵𝗲𝗿𝗲: [https://www.linkedin.com/jobs/view/4334981914] This is actually one of the more realistic QA Automation job postings I've seen lately. 🔗 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗼𝗻𝗲 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁: They're not playing title inflation games. They call it what it is: 𝗤𝗔 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. No "Senior Software Engineer in Test" or "Principal Quality Architect" nonsense. Just straight to the point. 📌 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘀𝘁𝗼𝗿𝘆: This company built a B2B platform for high-stakes operations. Not consumer-facing, but mission-critical stuff. The kind of software that can't afford to break. They need someone who can build solid test automation infrastructure and actually code, not just record-and-playback scripts. ⚠️ 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆'𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿: ✅ 2+ years of E2E & API test automation using 𝗣𝗹𝗮𝘆𝘄𝗿𝗶𝗴𝗵𝘁 ✅ Hands-on coding in 𝗣𝘆𝘁𝗵𝗼𝗻 (they specifically say "not just scripting") ✅ Building QA infrastructure - test environments, performance testing, security tooling ✅ Experience testing complex web applications ✅ Manual testing when automation gaps exist 𝗕𝗼𝗻𝘂𝘀 𝘀𝗸𝗶𝗹𝗹𝘀: JavaScript, C#, WebGL, performance/security/accessibility testing, telemetry tools 🟢 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗶𝗸𝗲 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁𝗶𝗻𝗴: They're honest about needing someone who can code, not just "write test scripts." They acknowledge manual testing is still part of the job, no pretending it's 100% automation. The salary range is realistic for mid-to-senior level QA automation ($100k-$150k). They list actual tools (Playwright, Python) instead of vague "automation experience." 🚩 𝗧𝗵𝗲 𝗰𝗮𝘁𝗰𝗵: It's posted by CyberCoders, a recruiting firm. That means you're not applying directly to the company. Over 100 applicants already, competition is real. "2+ years" sounds entry-level, but they want someone building QA infrastructure. That's more like 3-5 years of actual experience.

𝐓𝐡𝐞 𝐐𝐀 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐆𝐚𝐩 𝐍𝐨𝐛𝐨𝐝𝐲 𝐓𝐚𝐥𝐤𝐬 𝐀𝐛𝐨𝐮𝐭 ⤷ You've finished another online course ⤷ You understand a Testing Framework ⤷ You've built a few test scripts, maybe even completed a portfolio project But when you look at job postings asking for "2+ years of QA Automation experience," you freeze. 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐢𝐬𝐧’𝐭 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐚𝐬 𝐣𝐨𝐛-𝐫𝐞𝐚𝐝𝐲 𝐬𝐤𝐢𝐥𝐥𝐬. 𝐀𝐧𝐝 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐫𝐬 𝐤𝐧𝐨𝐰 𝐢𝐭 𝐭𝐨𝐨. ──────────────────────────────────────── 🚩 𝐖𝐡𝐚𝐭’𝐬 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐌𝐢𝐬𝐬𝐢𝐧𝐠: The gap between “𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧” and “𝐈 𝐜𝐚𝐧 𝐝𝐨 𝐭𝐡𝐢𝐬 𝐣𝐨𝐛” comes down to 3 things: ❶ Structured, real-world training that goes beyond basics. ≫ Most courses teach tools. They don't teach you how to think like an SDET and QA Automation Engineer, build frameworks from scratch, or integrate CI/CD pipelines the way companies actually use them. ❷ Proof of competency that employers trust. ≫ Saying "I know Playwright" on your resume means nothing without verified credentials. Employers need tangible evidence you can do the work. ❸ Deliberate practice on the exact skills interviews test. ≫ You can't Google your way through a technical interview. You need to have answered these questions dozens of times before you walk into that room. 📌 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐲 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭 𝐐𝐀 𝐀𝐮𝐭𝐨𝐓𝐞𝐬𝐭 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: 𝐀 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐡𝐚𝐭 𝐜𝐨𝐦𝐛𝐢𝐧𝐞𝐬 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠, 𝐯𝐞𝐫𝐢𝐟𝐢𝐞𝐝 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐭𝐨 𝐭𝐚𝐤𝐞 𝐲𝐨𝐮 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 𝐭𝐨 𝐣𝐨𝐛-𝐫𝐞𝐚𝐝𝐲 𝐦𝐢𝐝-𝐥𝐞𝐯𝐞𝐥 𝐒𝐃𝐄𝐓 𝐨𝐫 𝐐𝐀 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐧 𝟑-𝟒 𝐦𝐨𝐧𝐭𝐡𝐬 𝐇𝐞𝐫𝐞’𝐬 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 ──────────────────────────────────────── 🎓 𝐓𝐡𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: 𝐐𝐀 𝐀𝐮𝐭𝐨𝐓𝐞𝐬𝐭 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 Three volumes that take you from fundamentals to job-ready: - Volume 1: QA Automation & DevOps Fundamentals + Git + GitHub CI/CD - Volume 2: Python + Playwright + Framework Building + CI/CD Integration - Volume 3: Job Search, Resume & LinkedIn Strategies, Interview Prep (Behavioral + Technical + Coding) This isn't a crash course. It's a career transformation program designed around what companies actually hire for. ✅ 𝐓𝐡𝐞 𝐏𝐫𝐨𝐨𝐟: 𝐕𝐞𝐫𝐢𝐟𝐢𝐞𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞 𝐨𝐟 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐢𝐨𝐧

Most manual testers don’t realize this 👇 You don’t need an “Automation Engineer” title to gain automation experience. Recruiters care about proof of skill, not the title on your badge. Here’s how to build that proof while you’re still in your manual testing role. ⚡️ The Quick Strategy 💡 Learn automation tools — like Playwright Even 30 minutes a day compounds fast. 🧩 Build a testing framework and upload it to GitHub. That’s your public portfolio, your real proof of work. 📝 Add README files. Explain how to run your tests, what tech you used, and why you built it that way. Good docs = you think like an engineer. 📅 Commit consistently. Your commit history shows growth. Months of small progress beat one big upload. 📣 Make it visible. Link your GitHub on your resume and LinkedIn. If recruiters have to search for it, they won’t. 🎤 Own your story. Talk about your automation work confidently, even if your title still says “Manual Tester.” 🧠 The Mindset That Works Stop waiting for your company to “let” you do automation. Start acting like an automation engineer now. Your GitHub is your living resume it shows what you can do today, not what’s written in your title. Continuous learning + visible progress any job title. 🚀 Your Next Step 1️⃣ Pick one tool 2️⃣ Build one small framework 3️⃣ Make one commit to GitHub Then do it again tomorrow. That’s how manual testers become automation engineers not by waiting… but by building. 💭 What’s stopping you from making your first commit today?

Most QA Testers and QA Automation Engineer are still either ignoring AI testing tools or getting stuck with generic, low-value answers from ChatGPT and other AI tools. But today, you can change that. Get FREE access to our AI for QA Engineers course: a practical, hands-on program designed specifically for software testers. ➠ This isn't another generic "intro to AI" course. This is real-world AI automation techniques that actually improve your testing workflow. It’s a systematic AI for QA curriculum that shows you how to use AI strategically in your day-to-day testing work, the same frameworks AI-literate QA engineers and SDETs use to increase productivity, stand out in interviews, and command higher salaries. 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐠𝐞𝐭 𝐢𝐧𝐬𝐢𝐝𝐞 “𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐀𝐈 𝐟𝐨𝐫 𝐐𝐀 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬” ✅ 𝟓 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐜𝐡𝐚𝐩𝐭𝐞𝐫𝐬 – from AI fundamentals for QA engineers to advanced prompt engineering for testing ✅ 𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 𝐰𝐢𝐭𝐡 𝟓 𝐦𝐚𝐣𝐨𝐫 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 – ChatGPT, Claude, Gemini, Perplexity, and Grok for QA & test automation work ✅ 𝐃𝐚𝐢𝐥𝐲 𝐀𝐈 𝐭𝐨𝐨𝐥𝐤𝐢𝐭 𝐟𝐨𝐫 𝐭𝐞𝐬𝐭𝐞𝐫𝐬 – generate test cases, analyze bugs, design test data, and create professional QA reports faster ✅ 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠 – how to use the STAR method + an AI value framework to talk confidently about AI in QA interviews ✅ 𝐂𝐚𝐫𝐞𝐞𝐫 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐟𝐨𝐫 𝐐𝐀 & 𝐒𝐃𝐄𝐓 𝐫𝐨𝐥𝐞𝐬– position yourself as a QA who actually uses AI in real projects, without empty buzzwords Right now, companies are actively looking for QA engineers and SDETs who can combine solid testing skills with AI tools. These profiles are still rare, which is why they often command premium compensation and better opportunities. 📌 Most QAs fall into one of two traps: - They oversell their AI skills with buzzwords and get exposed in interviews - Or they avoid AI completely and look outdated compared to AI-literate candidates 👀 This training puts you in the realistic, high-value middle: You become a QA who can actually use AI to design tests, speed up debugging, improve coverage, and talk about it credibly in interviews.