Automated Testing Frameworks

QA will not be same in 2025 (25 predictions based on 12 months' observations) In 2024, we've seen a fundamental shift in how software is developed, tested, and delivered. This isn’t incremental change; it’s a full-blown revolution, and we've seen it firsthand. Based on our data and the trends we've observed – including the rise of tools like Cursor.com for AI-assisted coding, the adoption of TestOps practices, and the evolution of frameworks like Selenium, Cypress, and Playwright – here are 25 predictions for QA in 2025: *𝐓𝐡𝐞 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐀𝐈 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧* > AI-Powered Test Design (Cursor.com Impact) > Self-Healing Tests (Functionize & Autify) > Intelligent Defect Prediction (AI-Powered Platforms) > Autonomous Testing (Emerging Solutions) > AI-Assisted Visual Testing (Applitools & Percy) > Real-Time Test Optimization (AI-Driven Orchestrators) > AI-Driven Performance Testing (Load Testing Tools) *𝐓𝐡𝐞 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐓𝐞𝐬𝐭𝐞𝐫* > Strategic Quality Engineer (Beyond Scripting) > AI Tooling Expert (Mastering the New Tech) > Customer Experience Advocate (UX First) > Data Analysis Powerhouse (Metrics Driven) > DevOps Collaborator (Integrated Teams) > Ethical AI Guardian (Ensuring Fairness) *𝐓𝐡𝐞 𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞* Shift-Left Testing Will Become the Norm (CI/CD Integration) Cloud-First Testing (Scalability & Flexibility) Microservices Testing (Complex Interactions) API-First Testing (Backend Dominance) Integration Testing Mastery (System-Level Testing) Accessibility Testing Mainstream (Inclusive Design) *𝐓𝐨𝐨𝐥𝐬 𝐚𝐧𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬* > No-Code/Low-Code Testing (Katalon & Tricentis's Tosca ) > The Rise of Open-Source AI Tools (Community Driven) > Specialized Tools for Specific Industries (Vertical Solutions) > Unified Test Platforms (Integrated Solutions) > Enhanced Mobile Testing (Cross-Platform Testing) *𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬* > Quality as a Revenue Driver (ROI Focus): Businesses will measure quality as a key contributor to customer retention, loyalty, and overall business performance. We project at least 20% increase in revenue for companies who strategically invest in QA practices. . . . . . . P.S. What are YOUR predictions for QA in 2025 based on your recent experiences? I'd love to hear your thoughts and insights. #softwaretesting #QA #QAautomation

 𝘞𝘩𝘺 𝘠𝘖𝘜𝘙 Automation 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘸𝘪𝘭𝘭 𝘍𝘈𝘐𝘓 𝘪𝘯 6 𝘮𝘰𝘯𝘵𝘩𝘴 (𝘢𝘯𝘥 𝘩𝘰𝘸 𝘵𝘰 𝘧𝘪𝘹 𝘪𝘵) I just audited a $50M company's automation framework. Result? 73% of their tests were failing randomly. Their CTO asked me one question: "How did we go from 10 passing tests to complete chaos?" The Brutal Truth: 85% of Selenium Projects Die the Same Death Month 1: "Our automation is amazing!" Month 6: "Why does everything break when we deploy?" Here's what kills every framework (and the fix): 1️⃣ The "Everything in One Folder" Disaster ❌ Death pattern: UI, API, utils all mixed together ✅ Fix: Dedicated packages → UI, API, POJO, services separated Reality check: Good teams onboard new devs in 2 hours, not 2 weeks. 2️⃣ The "Hardcoded Hell" Problem ❌ Death pattern: URLs, data, timeouts scattered everywhere ✅ Fix: Environment property files + externalized test data Game changer: Switch DEV→QA→PROD with one command. 3️⃣ The "No POJO = No Scale" Trap ❌ Death pattern: Raw JSON strings, manual API payloads ✅ Fix: Request/Response POJOs + schema validation Impact: API tests become 10x more maintainable. 4️⃣ The "Debug Nightmare" Issue ❌ Death pattern: "Test failed" with zero context ✅ Fix: Extent Reports + screenshots + API logs Truth: Debug time drops from 2 hours → 5 minutes The Framework That Actually Scales I've built a production-ready structure that includes: 🏗️ Proper separation of UI/API/POJO layers 🔧 External configurations for all environments 📊 Rich reporting with screenshots & metrics 🚀 CI/CD ready with Docker & Jenkins support 🎯 BDD structure that business teams understand The Bottom Line: Stop building "quick automation scripts." Start building software systems that scale. Your framework should work at test #10 AND test #1000. Want the complete folder structure? 👇 Comment "𝑭𝑹𝑨𝑴𝑬𝑾𝑶𝑹𝑲" and I'll send it to your inbox! Found this helpful? Share with someone struggling with flaky tests! 🚀 -x-x- Full Stack QA & Automation Framework Course with Clearing SDET Coding Rounds: https://lnkd.in/g7tn6Uif #japneetsachdeva

lets say, you are QA manager and i am a QA and Manager asked you to start implementing automation testing for regression testcases for a website first. how would you do it? this will be my approach: Since we already have a set of manual regression test cases, I’d begin by reviewing and prioritizing them. Not all test cases are worth automating immediately—some may be too unstable or rarely executed. So, I'd focus first on high-impact, frequently executed tests like login, signup, checkout, and other critical flows. I'd organize these into a clear, shared spreadsheet or test management tool and tag them as "Ready for Automation." a tag always helps. Next, I’d set up a basic Java + Selenium framework. If we don’t already have one, I’d recommend using Maven for dependency management, TestNG or JUnit for test orchestration, and Page Object Model (POM) as the design pattern to keep our tests modular and maintainable. I'd also propose integrating ExtentReports for test reporting and Log4j for logging. I can bootstrap this framework myself or pair with a dev/test automation resource if needed. Once the skeleton framework is ready, I’d start converting manual test cases into automated scripts one by one. I’d begin with the smoke tests and top-priority regressions. For each script, I’d ensure proper setup, execution, teardown, and validations using assertions. then, I’ll commit code to a shared Git repo with meaningful branches and naming conventions. For execution, I'd run the tests locally first, then configure them to run on different browsers. Later, we can integrate the suite with a CI/CD tool like Jenkins to schedule regular test runs (e.g., nightly builds or pre-release checks). This would give us feedback loops without manual intervention. I’d document everything—how to run the tests, add new ones, and generate reports—so the team can scale this effort. I’d also recommend setting aside a couple of hours weekly to maintain and update tests as the app evolves. Finally, I’d keep you in the loop with weekly updates on automation progress, blockers, and test coverage. Once the core regression suite is automated and stable, we can expand into edge cases, negative tests, and possibly integrate with tools like Selenium Grid or cloud providers (e.g., BrowserStack) for cross-browser coverage. what will you be your action plan? let's share. #testautomation #automationtesting #testautomationframework #sdets

Been experimenting with AI tools in testing for a while now. Here's what I'm seeing in the real world. Where AI is genuinely helping: -Locator generation - Tools analyzing your app and suggesting stable locators. Saves hours compared to manual inspection. Example: Instead of spending 20 mins finding the perfect CSS/XPath, AI suggests 5 options in seconds with stability scores. -Test code generation - Writing boilerplate test cases from user stories or requirements. Not perfect, but gets you 70% there. You still need to review and fix, but it's faster than starting from scratch. -Analyzing test failures - AI reading stack traces and logs to pinpoint why tests failed. Instead of digging through 500 lines of logs, it tells you "API timeout on line 47" in 10 seconds. -Visual testing at scale - Catching UI changes across browsers/devices that humans might miss. -Test data generation - Creating realistic test data for different scenarios. Need 100 test users with valid emails, phone numbers, addresses? Done in seconds. Where AI is overpromised and underdelivering: "AI will write all your tests" - Nope. It writes basic happy path tests. Edge cases? Complex business logic? Still needs human brains. "No-code test automation" - Sounds great until the AI-generated test breaks and you can't debug it because you don't understand the code it wrote. Self-healing tests - Yes, it can update some selectors automatically. But it also "fixes" tests that should actually fail, hiding real bugs. 100% accurate defect prediction - AI says "this area is risky" based on code changes. Sometimes right, often wrong. Don't skip testing based on AI predictions alone. Replacing manual exploratory testing - AI follows patterns. Humans find weird unexpected bugs. Real examples from my experience: Win: Used AI to convert 50 manual test cases into automation scripts. Took 3 hours instead of 3 days. Still spent 4 hours reviewing and fixing. Fail: Tried "AI-powered" test maintenance tool. It auto-updated 30 tests after a UI change. 22 were correct. 8 were broken and I didn't notice for 2 days. Lost time debugging those false positives. Win: AI analyzing our failed test suite every morning. Started getting Slack messages like "12 tests failed due to database connection timeout, not code issues." Fail: Spent $$/month on an AI tool that "predicts which tests to run." Ran the wrong tests, missed critical bugs. My honest take: AI is a tool, not magic. Use it for: -Repetitive boring tasks (updating selectors, generating data) -First draft of test scripts (but YOU review) -Analyzing large amounts of data (logs, failures, patterns) Don't use it for: -Final decision making on test coverage -Replacing your understanding of the application -Skipping code reviews of AI-generated tests -Blindly trusting "self-healing" without verification Bottom line: AI saves me about 20-30% time on specific tasks. You still need to know testing, understand your app, and think critically. #AIInTesting

If your automation needs constant babysitting, read this ⬇️⬇️⬇️ Automation is supposed to save time, but many QAs spend hours and hours every week fixing broken tests. Here’s why. Most traditional test automation works like an old GPS with hard-coded routes. You program it step by step: ↳  turn left at this exact sign ↳  stop at this exact light ↳  turn right at this exact building Now imagine the city changes slightly… just one sign gets renamed or a road shifts, or a building is redesigned… the GPS fails and your route is broken! That’s what happens when your UI changes. But what if your automation understood the destination instead? KaneAI by TestMu AI works exactly like a modern GPS. You don’t script every turn. Just a simple description of the goal is enough. KaneAI builds the test flow, runs it across web, mobile and APIs, adapts automatically when UI elements change and even generates tests directly from JIRA tickets. 👀 The focus is on the intent, not fragile instructions. For QA and engineering teams, it means: ☆ Faster releases ☆ Less test maintenance ☆ More confidence in deployments Automation FINALLY works the way it was always meant to. If you want testing that adapts with your product (not against it), KaneAI is definitely worth exploring: https://lnkd.in/ggeMdAf9 What’s your team’s “here we go again” moment in QA? I bet every team has (at least) one

Imagine writing Playwright tests in plain English. No locators. No selectors. Just tell the AI what to do — and it gets done. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝗻𝗴 𝗺𝘆 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗚𝗲𝗻𝗔𝗜 𝗶𝗻 𝗧𝗲𝘀𝘁 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, 𝗜 𝘁𝗿𝗶𝗲𝗱 𝗣𝗹𝗮𝘆𝘄𝗿𝗶𝗴𝗵𝘁 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘂𝘀𝗶𝗻𝗴 𝗭𝗲𝗿𝗼𝗦𝘁𝗲𝗽’𝘀 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁. With simple natural language instructions, I was able to: Retrieve product price & discount values from the demo table Find the difference between actual and discounted prices Navigate through pages (About Me → Contact) Fill out the Contact form with realistic values — without defining them manually! #ZeroStep Playwright handled it all. So far, it looks like a promising way to: - Reduce test automation effort -Speed up test execution -Minimize coding -Help engineers focus on what to test, not how to locate elements How to Get Started: 1️⃣ Install Packages npm install Playwright/test zerostep-playwright 2️⃣ Import & Use AI Steps import { ai } from '@zerostep/playwright'; 3️⃣ Write Intelligent Tests Combine Playwright commands with natural language AI steps. 4️⃣ Boost Productivity with: ✅ Dynamic element selection ✅ Smart validations ✅ Flexible workflows Integrating AI into test automation isn’t just an upgrade — it’s a game changer for reliability and speed. Setting up ZeroStep with Playwright is simple: Create a ZeroStep account, install the dependency, configure your API token, and start using ai calls right inside your test cases. (Free accounts allow up to 500 AI calls/month.) What I Loved: ✅ Writing tests like a human — no complex scripting, just plain English ✅ Faster automation — saves time by skipping manual script writing ✅ Flexibility — still allows coding for tricky scenarios What Could Be Better: Works only on Chromium for now (no cross-browser support yet) This approach can truly bridge the gap between manual and automation testing — making life easier for testers. I’ll be exploring more complex scenarios next, but so far, this looks like the start of something big. Have you tried AI-assisted test automation yet? Would you trust natural language for writing your test scripts? official link-https://lnkd.in/ghCQyaPv #TestAutomation #ZeroStepAI #Playwright #AITesting #Selenium #Automation #bharatpost

🚀 Roadmap to Becoming a QA Automation Tester in 2025 🚀 The demand for QA Automation Testers is growing rapidly, and many QA professionals are looking to transition from manual testing to automation. If you're on this journey, here’s a structured roadmap to help you get started and land your dream job! 📌 Step 1: Master the Basics of Testing ✅ Understand SDLC & STLC ✅ Learn Testing Methodologies (Agile, DevOps, etc.) ✅ Get comfortable with Test Case Design & Bug Reporting 📌 Step 2: Learn a Programming Language Automation testing requires coding! Start with: ✅ Java (Most widely used) or Python ✅ Learn OOPs concepts, Data Structures & Algorithms ✅ Hands-on practice with small coding exercises 📌 Step 3: Master Selenium for UI Automation ✅ Learn Selenium WebDriver & Locators ✅ Understand TestNG, JUnit for test execution ✅ Handle Web Elements, Alerts, Frames, Windows ✅ Implement Page Object Model (POM) 📌 Step 4: API & Database Testing ✅ Learn Postman & REST Assured for API Testing ✅ Understand JSON, XML, HTTP Methods, Status Codes ✅ Work with databases using SQL queries 📌 Step 5: Learn Automation Frameworks ✅ Build a framework using Selenium, TestNG/JUnit ✅ Understand CI/CD integration (Jenkins, GitHub Actions) ✅ Implement Maven/Gradle, Logging, Reporting 📌 Step 6: Learn Performance & Security Testing ✅ Explore JMeter for Load Testing ✅ Learn the basics of Security Testing 📌 Step 7: Version Control & CI/CD ✅ Learn Git/GitHub for version control ✅ Understand Jenkins, Docker, Kubernetes 📌 Step 8: Keep Practicing & Build a Portfolio ✅ Contribute to open-source projects ✅ Build and showcase automation projects ✅ Share your learning journey on LinkedIn, GitHub, or a Blog 📌 Step 9: Prepare for Interviews ✅ Work on real-world projects ✅ Solve coding & automation problems ✅ Practice answering interview questions 💡 Pro Tip: Stay curious, keep learning, and network with industry professionals! If you're on the journey to becoming a QA Automation Tester, drop a 💬 in the comments or connect with me! Let’s grow together! 🚀

🚨 Public Service Announcement: If you're building LLM-based applications for internal business use, especially for high-risk functions this is for you. Define Context Clearly ------------------------ 📋 Document the purpose, expected behavior, and users of the LLM system. 🚩 Note any undesirable or unacceptable behaviors upfront. Conduct a Risk Assessment ---------------------------- 🔍 Identify potential risks tied to the LLM (e.g., misinformation, bias, toxic outputs, etc), and be as specific as possible 📊 Categorize risks by impact on stakeholders or organizational goals. Implement a Test Suite ------------------------ 🧪 Ensure evaluations include relevant test cases for the expected use. ⚖️ Use benchmarks but complement them with tests tailored to your business needs. Monitor Risk Coverage ----------------------- 📈 Verify that test inputs reflect real-world usage and potential high-risk scenarios. 🚧 Address gaps in test coverage promptly. Test for Robustness --------------------- 🛡 Evaluate performance on varied inputs, ensuring consistent and accurate outputs. 🗣 Incorporate feedback from real users and subject matter experts. Document Everything ---------------------- 📑 Track risk assessments, test methods, thresholds, and results. ✅ Justify metrics and thresholds to enable accountability and traceability. #psa #llm #testingandevaluation #responsibleAI #AIGovernance Patrick Sullivan, Khoa Lam, Bryan Ilg, Jeffery Recker, Borhane Blili-Hamelin, PhD, Dr. Benjamin Lange, Dinah Rabe, Ali Hasan

𝐓𝐡𝐞 𝐰𝐢𝐧𝐧𝐢𝐧𝐠 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐞𝐚𝐫𝐥𝐲-𝐬𝐭𝐚𝐠𝐞 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬 — 𝐏𝐚𝐫𝐭 𝟓/𝟓 𝐄𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐄𝐯𝐚𝐥𝐬. This is the final part of the series — and the most important. In Parts 1–4, the discussion was on design partners, engineers watching users, AI-generated code, and daily shipping. All of that gives you speed. This part is about making sure that speed does not destroy your product quality. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐰𝐢𝐭𝐡 𝐬𝐩𝐞𝐞𝐝. This is where most early-stage AI teams fall short. They can build fast. They can ship fast. But they break things as fast as they fix them. A prompt change that improves one use case quietly degrades three others. A model upgrade slowly degrades quality in ways nobody catches until a customer complains. With AI product outputs being probabilistic, "correct" is often a judgement call. And the same input can produce different outputs on different days if you change your prompts, your model version, or your retrieval pipeline. 𝐍𝐨𝐧-𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦. The winning teams build measurement and observability into every layer — dev, CI/CD, staging, and production. The eval framework is the measurement system. Before you even generate your first-line of code, you have to get the test and eval framework setup. It could be deterministic checks, deterministic UI automations, LLMs as judge for generated AI quality checks, and a way to score the results across agents and systems. 𝐓𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐫𝐚𝐭𝐜𝐡𝐞𝐭. The hardest thing I have seen across portfolio companies, is building an end-to-end system for a way to move code in an autonomous way from dev to stage to production, without worrying that things may have broken. The most important thing that compounds is bringing test cases from production to stage to development in reverse. The more robust your eval infrastructure, the faster you can move. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 — 𝐚𝐥𝐥 𝐟𝐢𝐯𝐞 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. When all five are in place — real design partners, engineers next to users, AI-generated code with senior control, daily shipping, and eval-instrumented testing — the product quality compounds at a rate that is almost impossible for competitors to match. You are building a machine that converts user insight into shipped product in days, with quality that improves with every iteration. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: If you are an early-stage AI founder and you do not have all five in place, please fix it now. Start with design partners — everything else flows from there. The product quality that wins markets is not built in a lab. It is built in the field, with real users, at speed, with a safety net that gets stronger every day. Love to hear your experience in parts or full, if you are practicing any of the above.

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗼𝗯𝘀𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗟𝗟𝗠𝘀. But most teams aren’t even testing the default behavior properly. A team we spoke to spent 6 weeks fine-tuning a model to reduce hallucinations in a customer support workflow. What they didn’t realize? The base model was already mostly fine. The hallucinations were triggered by edge-case phrasing—stuff their devs never thought to test for. What actually solved it? Not fine-tuning. Rigorous scenario-based testing with Ragmetrics. They fed real prompts, real tasks, real failure cases through our eval framework—and uncovered inconsistencies that only showed up under pressure. No more guessing. No more hallucinations at the worst time. Here’s the thing: 💡 You don’t need to fine-tune if you haven’t test-tuned first. Start with evaluation. Then optimize. If you’re building with LLMs and want to make sure your model actually behaves when it counts, happy to share what we’ve seen work. Just drop a comment or DM—I’ll send over the playbook.