Automated Testing Frameworks

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

The AI Coding Revolution Is Here, But Are We Testing for It? As AI-assisted development reshapes how we build software, I've been thinking a lot about something that is talked about often but doesn't always get the focus it deserves: automated testing. At JPMorganChase, we're embracing AI coding tools to accelerate delivery, reduce toil, and empower our teams to focus on the work that matters, reducing cognitive load of repetitive tasks. But speed without safety is just risk in disguise. Here's what I believe every leader (and this is broader than technology) needs to consider right now: • AI writes code faster than humans can review it manually. If your testing strategy is still largely manual, you're already behind. AI-generated code can introduce subtle logic errors, security vulnerabilities, or edge-case failures that look perfectly reasonable on the surface. Automated testing is no longer a best practice, it's a non-negotiable safeguard. • Test coverage is your new quality contract. When AI is your co-developer, the test suite becomes the specification. If you can't describe expected behavior in a test, you can't trust what the AI builds. Investing in robust unit, integration, and regression testing frameworks is investing in the integrity of your entire delivery pipeline. • Shift-left testing amplifies AI's value. It doesn't slow it down. Some worry that rigorous testing will negate the speed gains from AI coding. The opposite is true. When automated tests are embedded early in the development lifecycle, AI tools can iterate faster, self-correct, and validate outputs in real time. Testing enables velocity; it doesn't constrain it. • Your teams need to evolve alongside the tools. The best teams of tomorrow won't just write code. They'll architect test strategies, evaluate AI outputs critically, and build systems that are observable and verifiable by design. We owe it to our teams to invest in this skill evolution now. At the scale we operate, serving millions of customers, the cost of a defect isn't just technical. It's trust. And trust, once broken, is hard to rebuild. AI is a force multiplier. But multiplying without a strong foundation multiplies risk just as fast as it multiplies output. Build fast. Test smarter. Ship with confidence. I'd love to hear how other leaders are thinking about quality engineering in the age of AI. What's working for your teams? #AIEngineering #SoftwareTesting

The future of test automation isn't about code—it's about strategy. After 9+ years in automation testing, here's what I've learned: 1️⃣ The Evolution of Testing Remember when we spent countless hours: ↳ Debugging XPath issues ↳ Building utility functions ↳ Managing test data ↳ Fighting framework issues ↳ Maintaining CI/CD pipelines ↳ Fixing flaky tests That's not where our value lies anymore. 2️⃣ The Paradigm Shift The real questions in 2024: ↳ Not "How to automate?" but "What to automate?" ↳ Not "Which framework?" but "Which business flows?" ↳ Not "How to code?" but "How to design tests?" Test architecture is the new programming. Strategy is the new syntax. 3️⃣ Enter Low-Code Revolution Just explored BrowserStack's Low Code Automation tool. Here's what impressed me: Record & Play Evolved: ↳ No more flaky recordings ↳ Built-in smart utilities ↳ Data variables that actually work ↳ Intuitive test flow creation Cross-Browser Excellence: ↳ Seamless cloud integration ↳ Parallel execution on real devices ↳ No infrastructure headaches ↳ Instant device access 4️⃣ Game-Changing Features Random Data Generation: A personal story: Just started automating a simple signup flow. My first thought: "How will I handle new email generation?" Within minutes of checking documentation, I discovered their built-in random data generator. No custom functions are needed. No external dependencies. More Powerful Features: ↳ Save and reuse variables across tests ↳ Built-in data sets management ↳ Zero setup time for cross-browser testing ↳ Comprehensive API testing support ↳ Visual validation capabilities ↳ Network logs and error tracking 5️⃣ Integration Excellence ↳ CI/CD ready (Jenkins, GitHub Actions) ↳ Email notifications ↳ Seamless team collaboration 6️⃣ Future Vision Imagine: "Test the checkout flow across all supported browsers," and AI will handle the rest. I wonder when tools like BrowserStack's Low Code Automation will evolve with more AI capabilities: ↳ Natural language test generation ↳ Skip recording through AI prompts ↳ Self-healing test maintenance ↳ Predictive test selection ↳ Autonomous test execution We're not there yet. But tools like BrowserStack are paving the way. 7️⃣ The New Tester's Toolkit Success in automation now requires: ↳ Strong test design skills ↳ Understanding of testing patterns ↳ Business domain expertise ↳ Risk analysis capabilities ↳ Strategic thinking "The future belongs to testers who master test design. Not just those who write the best code." What's your take on low-code automation tools? Have you tried BrowserStack's Low Code Automation? https://t.ly/Y5pIG Share your experiences below. #QualityAssurance #TestAutomation #TechTrends #Testing #SDET #BrowserStack #AutomationTesting #SoftwareTesting #TestStrategy #FutureOfTesting #LowCode #QA

Is the QA Team improving quality or babysitting code? A few years ago, we worked on a project with a custom automation framework that looked perfect on paper. It had deep coverage, a solid structure, and a team that was working around the clock to maintain it. But when we stepped back to look at the actual outcome, the reality was not great. We were spending more time keeping the framework alive than we were testing the product. Every release followed the same exhausting cycle: A minor UI change would go live. Half of the “solid” scripts would break. The team would spend the next two days fixing selectors and patching code. The team was working hard and putting more effort into maintaining the test scripts. But it is imperative that being with automation, but not adding value. We were mechanics for our own tools, always fixing the engine instead of driving the car towards the destination: Quality. Then we had an internal brainstorming session and we have changed the metrics from “How many tests can we automate”? To how much automation maintenance can we reduce”? Maintaining test scripts all day may feel productive, but it does not necessarily help you catch better issues or improve the user experience. That shifted our focus and test automation scripts/frameworks should be an asset and not a technical debt so focus on what is important in testing and reduce the repetitive work. Instead of continuing with the same pattern, we used AI to generate the initial test scripts, which helped reduce the effort required to build and update tests from scratch. This did not remove the need for validation or thinking, but it changed how we spent our time. Gradually, we saw a difference, because instead of constantly fixing the test scripts, the team had more time to look at workflows, understand edge cases, and focus on how the system behaved under different conditions. The effort moved from maintaining the tool to improving the product. This experience made one thing very clear. Being technical is not based on how much code we write or maintain, but it is how effectively we use our time to improve quality. As AI and low-code approaches are more common, this shift is becoming visible across teams, where the role of a tester is moving away from continuous execution and toward better decision-making, understanding risks, and focusing on outcomes that is important  to the business. It is important to do the right work and using the right tools to avoid getting stuck in the repetitive cycles that do not add the value in long term. Over time, the teams that recognize this and adapt their approach will help to reduce effort and also improve the way they deliver quality, because they are not spending most of their time fixing the same issues again and again. QA Touch #softwaretesting #QACommunity #AItesting #QA #testing #techleadership #testingtrends #qatouch #qatouchautomate

"Quality starts before code exists", This is how AI can be used to reimagine the Testing workflow Most teams start testing after the build. But using AI, we can start it in design phase Stage - 1: WHAT: Interactions, font-size, contrast, accessibility checks etc. can be validated using GPT-4o / Claude / Gemini (LLM design review prompts) - WAVE (accessibility validation) How we use them: Design files → exported automatically → checked by accessibility scanners → run through LLM agents to evaluate interaction states, spacing, labels, copy clarity, and UX risks. Stage - 2: Tools: • LLMs (GPT-4o / Claude 3.5 Sonnet) for requirement parsing • Figma API + OCR/vision models for flow extraction • GitHub Copilot for converting scenarios to code skeletons • TestRail / Zephyr for structured test storage How we use them: PRDs + user stories + Figma flows → AI generates: ✔ functional tests ✔ negative tests ✔ boundary cases ✔ data permutations SDETs then refine domain logic instead of writing from scratch. Stage - 3: Tools: • SonarQube + Semgrep (static checks) • LLM test reviewers (custom prompt agents) • GitHub PR integration How we use them: Every test case or automation file passes through: SonarQube: static rule checks LLM quality gate that flags: - missing assertions - incomplete edge coverage - ambiguous expected outcomes - inconsistent naming or structure We focus on strategy -> AI handles structural review. Stage - 4: Tools: • Playwright, WebDriver + REST Assured • GitHub Copilot for scaffold generation • OpenAPI/Swagger + AI for API test generation How we use them: Engineers describe intent → Copilot generates: ✔ Page objects / fixtures ✔ API client definitions ✔ Custom commands ✔ Assertion scaffolding SDETs optimise logic instead of writing boilerplate. THE RESULT - Test design time reduced 60% - Visual regressions detected with near-pixel accuracy - Review overhead for SDETs significantly reduced - AI hasn’t replaced SDETs. It removed mechanical work so humans can focus on: • investigation • creativity • user empathy • product risk understanding -x-x- Learn & Implement the fundamentals required to become a Full Stack SDET in 2026: https://lnkd.in/gcFkyxaK #japneetsachdeva

In modern software development, writing code is only half the job — testing it is just as critical. But as codebases grow, maintaining strong unit test coverage becomes increasingly challenging. A recent engineering blog from The New York Times explores an interesting approach: using generative AI tools to help scale unit test creation across a large frontend codebase. - The team built an AI-assisted workflow that systematically identifies gaps in test coverage and generates unit tests to fill them. Using a custom coverage analysis tool and carefully designed prompts, the AI proposes new test cases while following strict guardrails — such as never modifying the underlying source code. Engineers then review and refine the generated tests before merging them. - This human-in-the-loop approach proved surprisingly effective. In several projects, test coverage increased from the low double digits to around 80%, while the time engineers spent writing repetitive test scaffolding dropped significantly. The process also follows a simple iterative loop: measure coverage, generate tests, validate results, and repeat. The experiment also highlighted some limitations. AI can hallucinate tests, lose context in large codebases, or produce outputs that require careful review. The takeaway: AI works best as an accelerator — not a replacement — for engineering judgment. As these tools mature, this kind of collaborative workflow may become a practical way for teams to scale reliability without slowing down development. #DataScience #MachineLearning #SoftwareEngineering #AIinEngineering #GenerativeAI #DeveloperProductivity #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gj9fc322

With Playwright, 3 hours of test setup time removed every sprint. Not by switching tools Not by hiring more people Not by skipping tests Only 5 Playwright fixtures 🧩 Below is the exact playbook used daily 👇 🧰 The Fixture Playbook 🔐 Auth State Reuse • Login once → reused across 200+ tests • All login calls removed from beforeEach() • Test execution starts instantly ➡️ Result: authentication stops being a bottleneck 🌐 API Context Fixture • Test data seeded via API before UI launch • Every test begins with a clean, known state • Flaky tests disappear ➡️ Result: predictable runs, zero surprises 📱 Custom Browser Context • Viewport, locale, permissions preconfigured • One line switches mobile ↔ desktop ➡️ Result: environment consistency without duplication 🗄️ Database Seeding • Auto-populate before suite • Auto-cleanup after execution • No shared state contamination ➡️ Result: goodbye “works on my machine” 📸 Screenshot + Trace on Failure • Screenshot captured instantly on break • Trace attached automatically • Debugging time reduced ~60% ➡️ Result: faster root-cause discovery ⚠️ What Most Engineers Get Wrong "conftest.py" is treated as an afterthought as a dumping ground But in reality… 🏗️ It is the foundation of the test architecture That single file decides whether a framework scales… or collapses. 🧠 Takeaway Stop writing repetitive setup code Start building fixtures that do the heavy lifting Save this for your next test architecture refactor 🔖 🔖 Save this before your next test framework refactor 🔁 Repost to help another QA engineer fight flaky tests 📤 Share with your team before your next sprint planning 💬 Which fixture saves YOU the most time? Tell me below 👇 #Playwright #QuickTip #QA #TestAutomation #Coding

Test automation involves using specialized tools and scripts to automatically execute tests on software applications. The primary goal is to increase the efficiency and effectiveness of the testing process, reduce manual effort, and improve the accuracy of test results. ⭕ Benefits: ✅ Speed: Automated tests can run much faster than manual tests, especially when running large test suites or repeated tests across different environments. ✅Reusability: Once created, automated test scripts can be reused across multiple test cycles and projects, saving time in the long run. ✅Coverage: Automation can help achieve broader test coverage by executing more test cases in less time. It can also test various configurations and environments that might be impractical to test manually. ✅Consistency: Automated tests execute the same steps precisely each time, reducing the risk of human error and improving the reliability of the tests. ✅Regression Testing: Automated tests are particularly useful for regression testing, where previously tested functionality is checked to ensure it still works after changes are made. ⭕Challenges: ✅Initial Setup: Creating and maintaining automated tests requires a significant initial investment in terms of time and resources. ✅Maintenance: Automated tests need to be updated as the application changes. This can lead to additional maintenance overhead, especially if the application evolves frequently. ✅Complexity: Developing and managing automated tests can be complex, particularly for applications with dynamic or changing interfaces. ✅False Positives/Negatives: Automated tests might produce false positives or negatives if not carefully designed, leading to misleading results. ⭕Common Tools: ✅Selenium: A widely used tool for web application testing that supports various programming languages. ✅JUnit/TestNG: Frameworks for Java applications that provide annotations and assertions for unit testing. ✅Cypress: A modern testing framework for end-to-end testing of web applications. ✅Appium: An open-source tool for automating mobile applications on various platforms. ✅Jenkins: Often used in continuous integration/continuous deployment (CI/CD) pipelines to automate the execution of test suites. ⭕Best Practices: ✅Start Small: Begin with a few test cases to build your automation framework and gradually expand as you refine your approach. ✅Maintainability: Write clean, modular test scripts that are easy to maintain and update. ✅Data-Driven Testing: Use data-driven approaches to test various input scenarios and ensure comprehensive coverage. ✅Integrate with CI/CD: Incorporate test automation into your CI/CD pipeline to ensure automated tests run with each code change. Review and Refactor: Regularly review and refactor your test scripts to improve their efficiency and reliability. In summary, test automation can significantly enhance the testing process, but it requires thoughtful implementation and ongoing maintenance to be effective.