Automation goes beyond script building: A shift in QA mindset

The New Era of Automation Testing: Survive or Evolve: Let’s be honest - automation testing today isn’t what it used to be. It’s no longer about writing scripts and running them overnight. The world around us has changed - CI/CD pipelines move in minutes, releases happen multiple times a day, and quality is no longer “owned” by QA alone. So, what does it take to exist - or better, thrive - in this new era? Adapt fast: Tools and frameworks will keep changing - Playwright, Cypress, AI-based testing, low-code platforms - but our mindset must evolve faster. Think beyond scripts: Understand business flows, data, performance, and integrations. The real value of automation lies in the insights it provides, not just the tests it executes. Be the bridge: The best automation engineers today connect development, operations, and performance teams - ensuring continuous feedback and faster confidence in every release. Leverage AI, but don’t rely on it blindly. The future isn’t humans vs. machines - it’s humans using machines wisely. The new generation of testers isn’t defined by the tools they know - it’s defined by how quickly they learn, adapt, and influence change. So the question is: Are you just automating… or are you evolving with automation? #AutomationTesting #QualityEngineering #AITesting #DevOps #SoftwareQuality #ContinuousTesting #TestingMindset

Everyone is pushing to release faster through CI/CD pipelines. But tech debt and manual test backlogs keep slowing teams down. Here’s why intelligent automation is changing the game and what to watch out for. In a recent meeting with a prospective customer, we were asked: "Can an AI-based system help us generate tests for every net-new user story in a sprint and keep pace with our release velocity?" We developed an **AI-augmented software testing system specifically for use with our clients** to address exactly this challenge. It analyzes sprint stories, product documents, and application workflows to generate new test cases and automation scripts, achieving **70–80% first-pass coverage** in real-world conditions. However, human review is essential. Without expert oversight, even the best models can miss the nuances behind evolving business logic. The teams seeing the biggest impact treat AI as an "acceleration engine," not a replacement for engineering expertise. Curious how teams are embedding AI into their QA without losing control? Let’s connect. #TestBacklogs #AIaugmentedSoftwareTesting #QAautomation

AI-augmented QA can boost your automation output by 3x. But it won't be 100% accurate out of the box. Here’s what you need to know about real-world results. In a recent meeting with a prospective customer, we were asked: *"When using an AI-based system to generate tests from user stories, how accurate is the output, and how much human review is needed?"* Through our **AI-augmented software testing system developed specifically for client projects**, we consistently see: - **First-pass accuracy** around **70–80%** for newly developed user stories. - **Initial accuracy** around **60–65%** when processing legacy backlogs. Over time, with human-in-the-loop reviews and iterative learning, both coverage and precision improve dramatically, helping engineering teams **triple** their automation delivery rates compared to traditional methods. The key isn't removing people from QA, it's using AI to amplify their expertise and free up strategic bandwidth. Would you want to see benchmarks from recent deployments? #SoftwareTesting #AIAugmentation #TestEngineering

In today’s Agile-DevOps world, test automation shouldn’t be gated by coding skills. Our latest article on Test Automation Forum authored by Abhishek kumar explores how scriptless automation enables business analysts, manual testers, and product owners to build, run, and maintain robust tests using drag-and-drop, record-and-playback, and visual workflows—no code required. Key wins: ✅ Faster releases via parallel testing ✅ Wider coverage by domain experts ✅ Lower costs with in-house staff ✅ CI/CD-ready with self-maintaining suites Top tools in 2025: Testsigma, ACCELQ, Katalon, TestGrid & more. Perfect for teams scaling quality without automation engineers. 📖 https://lnkd.in/gv_9qVT2 #ScriptlessTesting #NoCodeQA #TestAutomation #DevOps #QualityAtSpeed

😮 BUSTED: 5 Testing Myths That Could Be Sabotaging Your Software Quality! We've all heard them... those "common knowledge" testing myths that persist in the tech world. Let's set the record straight: 🤑 MYTH: "Automated testing is too expensive" ➡️ TRUTH: Manual testing actually costs WAY more over time! Our clients save an average of 70% on testing costs through strategic automation. 🎯 MYTH: "100% test coverage means bug-free software" ➡️ TRUTH: Coverage metrics can be misleading! What matters is testing the RIGHT things with the RIGHT scenarios. 🤖 MYTH: "AI will replace QA teams" ➡️ TRUTH: AI AMPLIFIES human testers, not replaces them! Our AI-powered testing frameworks make your QA team 5x more productive. ⏰ MYTH: "Performance testing can wait until after launch" ➡️ TRUTH: By then it's too late! Our pre-launch performance testing prevents costly outages and reputation damage. 🐌 MYTH: "Testing slows down releases" ➡️ TRUTH: With our CI/CD testing integration, our clients actually INCREASED deployment frequency by 300%! At RMT, we've helped enterprises transform their testing strategies from bottlenecks to business accelerators. Want to see how much faster (and more reliably) you could be shipping code? Let's talk! #QualityAssurance #TestingMyths #DevOps #TestAutomation #SoftwareTesting

The evolution of software testing is a journey we've all witnessed. From manual testers documenting every click to today's AI-powered solutions that practically think for themselves. I've been reflecting on how far we've come, so I created this quick comparison chart to visualize the transformation. What strikes me most? While manual testing served us well for decades, its high maintenance requirements simply can't keep pace with modern development cycles. Traditional automation brought improvements but still lacks true adaptability when applications change (and they ALWAYS change, right?). The emergence of AI-driven testing frameworks is a game-changer. With automatic adaptation, reduced maintenance needs, and even predictive capabilities, it's revolutionizing how we approach quality. At RMT Services, we've implemented these AI-driven solutions for several enterprise clients, reducing their testing maintenance costs by over 70% while improving coverage. Have you made the shift to AI-powered testing yet? What's been your experience with maintenance overhead in your current approach? Drop your thoughts below! And if you're curious about implementing a step-by-step framework for AI testing at your organization, let's connect. #SoftwareTesting #QualityAssurance #AITesting #TestAutomation #DigitalTransformation

🚨 Automation QA is dead. AI didn’t just replace it — it rebuilt it from the ground up. Scripts crack. APIs drift. Environments break. And suddenly, your “automated” QA feels anything but reliable. Time for something smarter! 💡 Our new ebook breaks down the Agentic Testing Stack — 9 layers that transform QA from reactive to intelligent, from brittle to bulletproof. 🚀 If you’re building enterprise resilience, this is your blueprint. 📘 Death of Automation QA — AI Testing Takes Over: https://lnkd.in/gPCBXQZt #AgenticAI #QA #TestAutomation #TestGrid

From Manual to Autonomous — The New Era of Software Testing Manual testing can’t keep pace with today’s speed, scale, and complexity. That’s why leading enterprises are making the shift to AI-powered, autonomous testing and Tek Leaders Inc Leaders is helping them get there. At Tek Leaders Inc, we enable organizations to move from traditional QA to intelligent, self-learning testing ecosystems that deliver: ⚡ Faster release cycles 🎯 Predictive defect detection 🤖 Self-healing test scripts 🔁 Continuous learning and improvement Our AI-driven frameworks combine machine learning, predictive analytics, and continuous testing to ensure agility, precision, and innovation at scale. 🔗 Read the full blog: https://lnkd.in/gijju--m The era of autonomous testing has begun — are you ready to evolve? #AITesting #Automation #SoftwareQuality #DigitalTransformation #TekLeaders

3 Ways AI is Making Test Automation Smarter (and QA Life Easier!): When we talk about AI in Testing, it’s not just about fancy buzzwords — it’s about solving those everyday automation struggles that every QA engineer knows too well. Here are three ways AI is quietly transforming our automation workflows: Scenario 1: Self-Healing Locators in Selenium/Appium:- Ever had a test fail just because one button ID changed? But this time, we didn’t panic. Our self-healing automation framework (integrated with an AI-based locator engine) scanned DOM patterns and automatically matched similar elements. No manual rework. No broken pipelines. Result: Less maintenance. More stable test runs. Scenario 2: AI-Driven Test Case Prioritization in CI/CD:- In our CI pipeline (Jenkins + GitHub), we’ve trained an ML-based model to study historical build data — which tests fail most often, which modules get frequent code changes, and which areas are risk-prone. Now, instead of running all 1,200 tests, AI selects only the 400 most relevant ones for each deployment. Result? Builds that finish in 40% less time and defects that surface earlier in the cycle. The pipeline thinks before it tests. Scenario 3: Auto-Generated Automation Scripts from Requirements:- Our BA team updates acceptance criteria in Confluence. With an API hook to a GenAI-powered script generator, we can now feed those requirements to an LLM. Results: within seconds, it outputs: * Gherkin test scenarios * Selenium snippets * And sample test data sets All we do is review, refine, and run. What used to take a week, now takes a few hours. AI isn’t replacing automation engineers — it’s enhancing their capabilities. It’s helping QA evolve from reactive test execution to intelligent, data-driven quality engineering. This shift feels less like a trend and more like the next logical step for mature automation practices. #AIinQA #TestAutomation #GenAI #MachineLearning #CICD #QualityEngineering #AutomationEngineering

Beyond Automation: How AI Agents Are Revolutionizing QA When I first stepped into the world of software testing, automation tools like Selenium and JMeter felt like magic. They saved time, caught bugs, and scaled testing beyond what manual testing ever could. But today, we stand at the edge of a new era in QA: AI Agents. Unlike traditional automation scripts, AI agents don’t just execute instructions — they perceive, decide, and act. They mimic human intelligence in testing workflows and can evolve with the product they’re testing. Let’s dive into what AI agents mean, how they work in QA, and why they might soon become your smartest “team members.” What Exactly Are AI Agents? Think of AI agents as digital testers with a brain. They observe their environment (logs, APIs, test results). They process data using AI/ML models. They take intelligent actions like generating test cases, healing broken scripts, or predicting defects. Unlike static automation frameworks, AI agents can be: Autonomous → Fully capable of decision-making and execution. Semi-Autonomous → Work with human testers, assisting in decision-making. And they follow a continuous loop: Perception → Processing → Action → Learning → Re-Action. For More Details :-- https://lnkd.in/dVaZ8ThT