Key Trends Shaping Automated Testing Today

"I just spent three days fighting this proprietary automation language, and ChatGPT just wrote better Playwright tests in three minutes." I believe we're witnessing the extinction of an entire category of QA tooling — and many teams are continuing to invest in exactly the wrong direction. For the past decade, the QA automation industry spawned hundreds, if not thousands, of products, services, and companies solving one core problem: the skills gap between manual testers and code-based automation. Tools like Katalon, TestComplete, and other no- or low-code platforms promised to bridge this gap with proprietary languages, visual interfaces, and record-to-playback features. But AI just made that entire value proposition obsolete. When the barrier to entry to test automation was knowing how to code, these abstractions made sense. But now, someone can describe test scenarios in plain language and get working (and increasingly maintainable) code in standard frameworks. It's the same macro-trend we're seeing across the industry. AI handling the mechanical work so people can focus on what actually matters. For QA teams, that means spending less time wrestling with automation syntax and more time on strategic testing: understanding user journeys, identifying edge cases, and ensuring quality at the product level. QA teams learning these proprietary automation platforms and languages are essentially investing in skills with expiration dates. The problem these tools are solving is disappearing. Meanwhile, the QA teams who are leveraging AI to automate tests using standard languages and frameworks using Cursor, Copilot, or Claude Code are building capabilities that will compound over time. Their teams develop portable skills, create maintainable codebases, and stay aligned with industry standards. Stop solving yesterday's problem. The coding skills gap that justified proprietary automation tools just vanished. The question isn't whether your manual testers can learn to code — it's whether they can learn to direct AI effectively. And remember: this is the worst these AI tools will ever be. 👋 Hi, I'm Nathan Broslawsky. Follow me here and subscribe to my newsletter above for more insights on leadership, product, and technology.

🚀 The Future of Software Testing: What We Need to Learn Today 🚀 As someone who's been in software testing for 25 years, I've seen how much has changed — from manual testing to automation, from Selenium to Playwright, and now, the next big thing: AI-powered testing. And if you're like me, you're probably thinking: what's next? Looking ahead to the next 5 years, here's what I believe testers should focus on: 👉 Shift-Left & CI/CD Testing Gone are the days when testing happens only at the end of the process. The future is all about testing early, continuously, and with CI/CD pipelines. It’s time for testers to dig deeper into automating at every stage, from unit tests to APIs. Testers should focus on: Integrating testing into CI/CD pipelines, collaborating closely with dev teams, and automating tests earlier in the cycle to get faster feedback. 👉 AI-Powered Test Automation AI is no longer just a buzzword—it’s already here, and it’s transforming how we design and run tests. From generating test cases automatically to fixing broken scripts, AI is going to make our lives easier. We’ll still need to validate and guide AI-generated results, but the tools are getting smarter. Testers should focus on: Exploring AI tools that assist in test generation, learning how to prompt these tools effectively, and validating AI-generated outputs to ensure quality. 👉 Security Testing Security is going to be at the core of everything we do. With more apps moving to the cloud and being built on microservices, knowing how to perform security tests (DevSecOps) will be crucial. Testers should focus on: Learning security testing techniques, familiarizing themselves with vulnerabilities, and integrating security checks into the testing process from day one. 👉 Data-Driven Testing In the future, data is king. Not just in terms of coverage and defect trends, but also using real-time data to understand system performance. Testers should focus on: Using tools that provide insights into system performance (metrics, logs, traces) and incorporating them into the testing process for more effective decision-making. 👉 Domain Knowledge & System Complexity Software is getting more complex. Whether it’s working in fintech, healthcare, or any other specialised field, understanding the domain and system complexity is key. The more testers know about how the system works, the better their testing becomes. Testers should focus on: Deepening their knowledge of specific domains (e.g., finance, healthcare) and understanding architectural complexities like microservices to better navigate testing challenges. 👉 Leadership & Soft Skills With automation doing a lot of the heavy lifting, the future of testing will need testers to take on leadership roles—designing strategies, mentoring teams, and driving quality discussions across the board. #SoftwareTesting #Automation #AI #ShiftLeft #DevOps #SecurityTesting #QualityEngineering #LearningJourney

"AI will replace QA engineers" was the hottest take of 2024. But that’s not what’s actually happening. Here’s what I see coming in 2025: Traditional test automation is struggling. Modern apps change constantly — dynamic UIs, frequent updates, complex user flows. Tests break all the time. AI-powered testing is stepping in and handling test creation, maintenance, and test script adaptation automatically. QA roles are shifting. With AI covering repetitive tests, QA engineers are focusing on strategy, risk analysis, and making sure AI actually works as expected. Exploratory testing and UX validation are becoming even more important. Quality metrics are changing too. It’s no longer just about passing tests. Speed and reliability are now competitive advantages. AI is being built into CI/CD to predict and prevent bugs, not just catch them after the fact. The gap between pre-release and production testing is getting smaller. AI-driven testing is creating a continuous feedback loop by learning from real user behavior, automatically generating new test cases, and preventing regressions before they happen. Cost-cutting is forcing teams to be more efficient. Instead of juggling multiple testing tools, they’re moving towards all-in-one platforms. The goal is to reduce overhead while increasing test coverage. Security and compliance testing aren’t optional anymore. AI-driven threats are evolving fast, and regulations are tightening. Automated security, accessibility, and compliance checks are now essential. 2025 won’t kill QA. It’ll make it a competitive advantage. The teams that adapt will ship faster and with higher confidence. How are your testing strategies shifting? #ai #automation #trends #technology 

AI + Playwright is evolving fast.   Studied 20 open-source “underdogs” -- here are 5 design patterns shaping the future of test automation:  🔹 Prompt → Code   Stop hand-writing every test. Describe flows in English, AI writes Playwright.   Examples: Zerostep, Playwright Mind, Playwright Copilot, PlaywrightGPT, playwright-ai, Playwright AI CLI.  🔹 Smarter Locators   Brittle CSS/XPath selectors are done. AI-driven queries adapt as UI changes.   Examples: AI Locators, AgentQL, Auto Playwright.  🔹 Vision Assist   DOM lies. Screenshots don’t. AI + OCR let agents *see* the UI when selectors fail.   Examples: Skyvern, coTestPilot, AIRAS Agent.  🔹 Human-in-the-loop   Agents need brakes. Add human checks for destructive steps, logs, or approvals.   Examples: Promptwright, AgentLite, BDD-Copilot-with-Playwright.  🔹 Behavior Caching   Don’t teach agents the same flow twice. Cache sub-flows (checkout, login) and re-use.   Examples: Agentic AI Browser, Playwright MCP Server, Auto Browse.  The shift is clear:   From scripts → agents.   From clicks → plans.   From QA overhead → production-ready pipelines.  Underdogs prove the patterns, but teams need reliability at scale.  That’s where Bug0 comes in:   ✅ Human-verified AI QA Engineer   ✅ 100% critical flows in 7 days   ✅ Fully managed, done-for-you solution for modern, lean teams Full breakdown with all 20 projects → https://lnkd.in/gvXcS8t8 #AI #Playwright #QualityAssurance #TestAutomation #DevTools

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

Am I hallucinating, or is reality just around the corner? The global software and application testing market is worth $60–65 billion, powered by 120–150 mature tools across 12 categories (SAST, DAST, MAST, etc.) — all built for a world where humans write insecure code. But that world is changing — fast. Today, 20–30% of enterprise code is AI-generated, thanks to tools like GitHub Copilot, Amazon CodeWhisperer, and Tabnine. By 2030, this could hit 80%+, especially for boilerplate, middleware, UI flows, and backend scaffolds. With better-trained models and reduced hallucinations, AI-generated code may soon surpass human-written code in both quality and security. So the question: ·      Do we still need to rely on expensive, periodic, and often noisy testing tools that miss business-logic issues and generate high false positives? Especially when: ·      60–70% of attacks target web applications, mainly due to insecure code and misconfigurations. ·      If AI generates secure, validated code, it is no longer a primary attack vector — and the cost of IR, SIEM, and SOC operations could decline drastically. Imagine a future where: ·      Every AI-generated code block is cryptographically signed. ·      Security, compliance, and logic checks run pre-commit, not post-deploy. ·      SBOMs come with LLM fingerprints and trust attestations. ·      Policy-as-code gates enforce GDPR, PDPL, and NCA directives automatically. ·      Every reused public code block is auto-verified for safety and license integrity. ·      Applications are self-testing, self-adapting, and self-healing. In the short term, testing will shift from scanning to inline AI validation — but what after that? This evolution will disrupt the entire ecosystem: ·      Tools stuck in legacy CI/CD pipelines and unaware of AI contexts will fade. ·      Pentesters who don’t adopt AI and automation may struggle to stay relevant. ·      Managed testing services, powered by AI-native platforms and scalable onboarding, will surge. Because in the future, we won’t test security after code is written — we’ll build trust into the way it’s created. Are we in sync on this thought? #AppSec #AIinSecurity #DevSecOps #Cybersecurity #CISO #AITransformation #LLM #SecureByDesign #pentest #cyber #SOC

🚀The real opportunity with AI isn't about building more - it’s about how humans can apply it in smarter, more innovative ways, especially in assessment. This year, let’s not ask "what now", let's ask ‘what if’: What if: 🖌️ AI could analyze examinee behavior in real-time to auto-adjust accommodations like font size, contrast, and voice prompts, ensuring accessibility without pre-requests? 🙏 ethically responsible facial recognition or voice sentiment analysis could adapt test pacing or provide calming cues for candidates showing signs of stress? 🔮 predictive models measured not only what candidates know today but also their capacity to learn and apply knowledge in the future? 🧠 AI detected cognitive fatigue and modified pacing or recommend breaks mid-assessment for optimal performance? 📈 models could detect anomalies like sudden difficulty spikes during exams and recalibrate on-the-fly to maintain fairness? 💻 AI could evaluate readiness through pre-tests and recommend optimal testing times based on mental alertness data? 🖱️ nuanced behaviors like hesitation patterns or mouse movements could identify cognitive processes and offer dynamic insights to content teams to improve task design? 🌐 automated item generation could localize questions and scenarios on the fly to make assessments more relevant and fair across diverse populations? 🔠 dynamic blueprints could evolve based on global candidate data, adapting to emerging trends and staying perpetually relevant? 🌳 near-infinite item banks could be created by continuously monitoring global knowledge databases to auto-generate highly contextualized, evergreen test items? 🤖 AI distributed the psychometric design, where thousands of micro-AIs independently optimized different parts of the testing process ensuring maximum precision and scalability while reducing systemic error risks? The future of assessment will be shaped by the bold “what ifs” humans are willing to explore today. This year, let’s aspire to solutions that not only responsibly push boundaries but also build trust and enhance equity. 🚀⚖️ What’s your “what if” in 2025? 🙏👇 🌚Do you find these aspirations helpful as a little inspo? Grab the PDF from the link in the comments. #PossibilityNotPrediction #AIforGood #InnovationInAssessment

🚀 Built an AI-Driven Test Automation Pipeline that generates, runs, and validates tests — all automatically. I recently designed and implemented a modern automation framework that goes way beyond writing manual test scripts. By combining AI-powered test generation with seamless CI/CD, we now have a true end-to-end intelligent testing system. What I Built: JSON-based Test Planner → Define test steps dynamically in a clean, structured format Auto-generated Playwright scripts → From structured JSON inputs straight to executable TypeScript tests Full CI/CD pipeline with GitHub Actions for continuous execution Automated browser setup, dependency management, and consistent environments Detailed test reporting with logs and artifacts Tech Stack: n8n → for powerful workflow automation Playwright + TypeScript → for reliable browser automation GitHub Actions → for CI/CD JSON-driven approach for flexible test planning Real Challenges I Solved: CI failures caused by dependency and lock file mismatches Git workflow issues (like detached HEAD state) Keeping environments consistent between local machines and CI runners Standardizing test execution across the pipeline These were frustrating at first, but overcoming them taught me a lot about building robust, production-ready systems. Key Takeaway: Modern test automation isn’t just about writing scripts anymore. It’s about creating intelligent, scalable systems that blend AI, workflow orchestration, and continuous delivery. The outcome? A fully working pipeline that can generate tests, execute them, and validate results with minimal human intervention — bringing us one step closer to truly AI-powered testing. If you're in QA, SDET, or DevOps, I’d love to hear your thoughts: Have you integrated AI into your test automation yet? What’s the biggest pain point in your current testing pipeline? Let’s discuss in the comments 👇 #Automation #TestAutomation #Playwright #n8n #AI #CI_CD #DevOps #SoftwareTesting #LearningInPublic Indraxy Jape Suraj Yadav Avinash Pingale

AI in Testing: The Next Evolution of Quality Engineering After 30 years at the U.S. Patent and Trademark Office, I’ve seen testing evolve from manual execution to automation, and now we’re entering the next major shift: AI-assisted quality engineering. While working with Scrum teams in a DevOps environment and mentoring SDETs, it’s becoming clear that AI isn’t replacing testers — it’s amplifying their impact. AI can help quality engineers: • Generate test scenarios and edge cases faster • Analyze large volumes of logs, telemetry, and test results • Detect patterns in failures and predict potential defects • Assist with test automation development and maintenance • Improve coverage across complex systems and integrations But the most important piece is still human engineering judgment. The role of SDETs and quality engineers is evolving from simply executing tests to designing intelligent testing strategies supported by AI tools. Organizations that embrace this shift will empower their engineering teams to move faster, detect issues earlier, and build more resilient software systems. AI won’t replace quality engineers. But quality engineers who effectively use AI will shape the future of software testing. #AIinTesting #QualityEngineering #SDET #DevOps #EngineeringLeadership #TestAutomation

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