Automated Testing Frameworks

🚨 BREAKING: McKinsey just revealed why 2/3 of “AI initiatives” never make it past the pilot stage. Everyone’s “using AI” now. But in most companies, most of it lives in: - One enthusiastic team’s sandbox - A few rogue Zapier automations - And a dead Slack channel called #ai-experiments McKinsey’s latest State of AI report shows: → ~2/3 of companies never scale beyond pilots → Only a small minority see real, enterprise-level impact Accenture and BCG are seeing the same thing with their clients: The problem isn’t tools. It’s the missing bridge between. When we go into companies (50–5000+ employees) and do AI automation audits, we see the same patterns on repeat: No owner after the pilot ends No success metric (beyond “it looks impressive”) No fallback when the AI gets it wrong No change management (so teams quietly ignore it) No monitoring (so the first failure kills trust) So we started asking a different question: “What if we treated AI automations like products, not like internal experiments?” For our clients, we now run every AI initiative through a Pilot → Production Pipeline: 1. Business Case First If we can’t tie the automation to revenue, margin, or a key SLA, we don’t build it. 2. Owner + KPI, Upfront One person. One metric. One timeline. No “innovation in general”. 3. Production-Ready Spec Trigger, data sources, edge cases, escalation rules, and what happens when it fails. 4. Shadow Mode Before Go-Live The AI runs in parallel to humans for 2–4+ weeks so we track everything and see real-world breakage before customers do. 5. Monitoring & Change Management Dashboards, feedback loops, and comms so the frontline knows what changed, why it’s better & how to escalate. To make this easier, I packaged the exact framework we use with clients into a Pilot-to-Production AI Checklist you can plug into your next project: ✅ 30-point Pilot → Production checklist ✅ The 5 non-negotiables before you ship any AI automation ✅ A one-page spec template you can send to any internal team or vendor ✅ A simple scoring model to kill weak pilots early Comment “CHECKLIST” and I’ll send it to you. (Make sure you’re connected)

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

LLMs are great for data processing, but using new techniques doesn't mean you get to abandon old best practices. The precision and accuracy of LLMs still need to be monitored and maintained, just like with any other AI model. Tips for maintaining accuracy and precision with LLMs: • Define within your team EXACTLY what the desired output looks like. Any area of ambiguity should be resolved with a concrete answer. Even if the business "doesn't care," you should define a behavior. Letting the LLM make these decisions for you leads to high variance/low precision models that are difficult to monitor. • Understand that the most gorgeously-written, seemingly clear and concise prompts can still produce trash. LLMs are not people and don't follow directions like people do. You have to test your prompts over and over and over, no matter how good they look. • Make small prompt changes and carefully monitor each change. Changes should be version tracked and vetted by other developers. • A small change in one part of the prompt can cause seemingly-unrelated regressions (again, LLMs are not people). Regression tests are essential for EVERY change. Organize a list of test case inputs, including those that demonstrate previously-fixed bugs and test your prompt against them. • Test cases should include "controls" where the prompt has historically performed well. Any change to the control output should be studied and any incorrect change is a test failure. • Regression tests should have a single documented bug and clearly-defined success/failure metrics. "If the output contains A, then pass. If output contains B, then fail." This makes it easy to quickly mark regression tests as pass/fail (ideally, automating this process). If a different failure/bug is noted, then it should still be fixed, but separately, and pulled out into a separate test. Any other tips for working with LLMs and data processing?

22 Test Automation Framework Practices That Separate Good SDETs from Great Ones Here's what actually works: 1. KISS Principle Break complex tests into smaller modules. Avoid singletons that kill parallel execution. Example: Simple initBrowser() method instead of static WebDriver instances. 2. Modular Approach Separate test data, utilities, page objects, and execution logic. Example: LoginPage class handles only login elements and actions. 3. Setup Data via API/DB Never use UI for test preconditions. It's slow and flaky. Example: RestAssured POST to create test users before running tests. 4. Ditch Excel for Test Data Use JSON, XML, or CSV. They're faster, easier to version control, and actually work. Example: Jackson ObjectMapper to read JSON into POJOs. 5. Design Patterns Factory: Create driver instances based on browser type Strategy: Switch between different browser setups Builder: Construct complex test objects step by step 6. Static Code Analysis SonarLint catches unused variables and potential bugs while you code. 7. Data-Driven Testing Run same test with multiple data sets using TestNG DataProvider. Example: One login test, 10 different user credentials. 8. Exception Handling + Logging Log failures properly. Future you will thank present you. Example: Logger.severe() with meaningful error messages. 9. Automate the Right Tests Focus on repetitive, critical tests. Each test must be independent. 10. Wait Utilities WebDriverWait with explicit conditions. Never Thread.sleep(). Example: wait.until(ExpectedConditions.visibilityOfElementLocated()) 11. POJOs for API Type-safe response handling using Gson or Jackson. Example: Convert JSON response directly to User object. 12. DRY Principle Centralize common locators and setup/teardown in BaseTest class. 13. Independent Tests Each test sets up and cleans up its own data. Enables parallel execution. 14. Config Files URLs, credentials, environment settings—all in external properties files. Example: ConfigReader class to load properties. 15. SOLID Principles Single responsibility per class. Test logic separate from data and helpers. 16. Custom Reporting ExtentReports with screenshots, logs, and environment details. 17. Cucumber Reality Check If you're not doing full BDD, skip Cucumber. It adds complexity without value. 18. Right Tool Selection Choose based on project needs, not trends. Evaluate maintenance cost. 19. Atomic Tests One test = one feature. Fast, reliable, easy to maintain. 20. Test Pyramid Many unit tests (fast) → Some API tests → Few UI tests (slow). 21. Clean Test Data Create in @BeforeMethod, delete in @AfterMethod. Zero data pollution. 22. Data-Driven API Tests Dynamic assertions, realistic data, POJO response validation. Which practice transformed your framework the most? -x-x- Most asked SDET Q&A for 2025 with SDET Coding Interview Prep (LeetCode): https://lnkd.in/gFvrJVyU #japneetsachdeva

Automation is more than just clicking a button While automation tools can simulate human actions, they don't possess human instincts to react to various situations. Understanding the limitations of automation is crucial to avoid blaming the tool for our own scripting shortcomings. 📌 Encountering Unexpected Errors: Automation tools cannot handle scenarios like intuitively handling error messages or auto-resuming test cases after failure. Testers must investigate execution reports, refer to screenshots or logs, and provide precise instructions to handle unexpected errors effectively. 📌 Test Data Management: Automation testing relies heavily on test data. Ensuring the availability and accuracy of test data is vital for reliable testing. Testers must consider how the automation script interacts with the test data, whether it retrieves data from databases, files, or APIs. Additionally, generating test data dynamically can enhance test coverage and provide realistic scenarios. 📌 Dynamic Elements and Timing: Web applications often contain dynamic elements that change over time, such as advertisements or real-time data. Testers need to use techniques like dynamic locators or wait to handle these dynamic elements effectively. Timing issues, such as synchronization problems between application responses and script execution, can also impact test results and require careful consideration. 📌 Maintenance and Adaptability: Automation scripts need regular maintenance to stay up-to-date with application changes. As the application evolves, UI elements, workflows, or data structures might change, causing scripts to fail. Testers should establish a process for script maintenance and ensure scripts are adaptable to accommodate future changes. 📌 Test Coverage and Risk Assessment: Automation testing should not aim for 100% test coverage in all scenarios. Testers should perform risk assessments and prioritize critical functionalities or high-risk areas for automation. Balancing automation and manual testing is crucial for achieving comprehensive test coverage. 📌 Test Environment Replication: Replicating the test environment ensures that the automation scripts run accurately and produce reliable results. Testers should pay attention to factors such as hardware, software versions, configurations, and network conditions to create a robust and representative test environment. 📌 Continuous Integration and Continuous Testing: Integrating automation testing into a continuous integration and continuous delivery (CI/CD) pipeline can accelerate the software development lifecycle. Automation scripts can be triggered automatically after each code commit, providing faster feedback on the application's stability and quality. Let's go beyond just clicking a button and embrace automation testing as a strategic tool for software quality and efficiency. #automationtesting #automation #testautomation #softwaredevelopment #softwaretesting #softwareengineering #testing

Machine Learning (ML) offers a unique way to prioritize the ASIC Verification task by bringing in automation and other flows to meet the market demands of the chip design. Some of the applications of ML include, but are not limited to, automated detection of unexpected behaviour of the design, predicting regression failures, bucketizing the failures, and filling the coverage holes by analyzing the data and patterns. The indicated ML-assisted flow looks like this: Data Collection --> Feature Extraction --> Model training ( PyTorch, scikit-learn, Tensorflow, Keras, Weights and biases) --> Feedback --> Inference and Insight. Applications of Machine Learning: [1] Anomaly Detection: Unsupervised models (Autoencoders, clustering) spot rare timing violations or glitch pattern. [2] Failure Prediction: Supervised classifiers rank testcases by failure probability. [3] Coverage Hole Identification: Dimensionality reduction (PCA, t-SNE) visualizes untested corner cases and guides the generation of new stimuli. [4] Testbench optimization: Reinforcement learning algorithms adapt stimulus generators to maximize functional coverage with fewer cycles. [5] Automated Assertion (SVA) Generation from Spec: Microarchitecture specification is the starting point for Design Architecture and Test Planning. There are tools already that use progressive regularization and post-processing to convert prompts written in English, extracted from the spec, into Assertions. All these are used using an LLM interface. #vlsi #asic #electricalengineering #MachineLearning

The best QA engineers I know hate testing. Not the work. The repetition. Running the same 300 regression cases after every deployment. Fixing scripts that break because a button moved 4px to the left. Spending 60-70% of their week on maintenance, not discovery. That's not QA. That's groundwork. And groundwork doesn't need a senior engineer. It needs automation that actually holds up. CoTester by TestGrid.io absorbs that layer. Tests built from real user workflows, not hand-written scripts. Self-healing when the UI shifts. Running continuously without anyone babysitting them. What's left for the engineer? The 20% of edge cases that cause 80% of production incidents. The flows nobody thought to test. The failure modes that don't show up in a happy path. That's where senior QA thinking actually lives. And right now, most teams are burying it under sprint after sprint of regression work. The tool handles the known. The tester hunts the unknown. Here's the reality, product requirements aren't slowing down. Every sprint adds new flows, new edge cases, new surfaces to break. The coverage gap widens every week you're still doing this manually. That's exactly why teams are moving to CoTester as their AI teammate. Not to replace QA but to make sure QA can actually keep up. If your regression suite is already struggling to match your release pace, it's worth a look: https://lnkd.in/ddP5t3Ta #testing #ai #productivity

AI won’t take your QA job. But a QA who knows AI probably will. Some QAs still think AI is far, but it is already testing your product, predicting bugs, and generating reports faster than you can log into Jira. Merely using ChatGPT to get suggestions doesn't mean you are leveraging AI; it’s about understanding how these tools are changing QA workflows. Here’s what’s actually happening in real teams right now: → Test case generation is getting automated. AI tools like Testim and Mabl are learning from your existing tests and suggesting new ones based on app behavior. → Defect prediction is real. Platforms like Applitools and Functionize use machine learning to spot patterns in flaky tests and failures. → Root cause analysis is getting faster. AI can now correlate logs, commits, and test results to tell you why something broke, not just what broke. → Regression testing is smarter. AI helps prioritize what to run, so instead of executing 500 test cases, you run the 50 that actually matter. You don’t need to be a data scientist to stay relevant. You just need to understand how to work with AI tools, not against them. Start here: ✅ Pick one AI-powered testing platform (Testim, Mabl, or Applitools). ✅ Learn how it integrates with your automation framework. ✅ Practice reading AI-generated insights and validating them manually. ✅ Build your judgment, AI will handle speed; you handle context. That’s how you future-proof your career in QA. AI won’t replace the QA mindset; it’ll reward the ones who evolve. So don’t wait until it’s everywhere in your company. Start learning before your company expects you to. Repost this if you agree. P.S. I'm Raghvendra, a QA-II at Amazon. I share real stories and practical lessons from my journey in QA and career growth. Follow along if that’s the path you’re on, too.

We shouldn’t ship #CopilotStudio agents without real tests. You can bake testing right into your Power Platform pipeline: pause the deploy, run tests with Copilot Studio Kit, and only continue if they pass. That keeps bad changes or bugs out of UAT/Prod and speeds things up long‑term. I've been exploring Copilot Studio Kit and one of the things that I really like about it is that it covers more than “does the bot answer?” It supports: ✅ Dev‑time checks while you build ✅ Knowledge tests (does it use the right sources?) ✅ Regression tests (did we break something that used to work?) ✅ Adversarial tests (hard or tricky prompts) And you can build once, reuse: ✅ Run tests in bulk ✅ Do end‑to‑end, multi‑turn scenarios ✅ Reuse the same test sets across agents (including adversarial) ✅ Plug the tests into your pipeline so they run on every deploy I was asked recently, “can we make automated testing part of the pipeline?” Yes, that is possible and that’s the direction I’m taking. I am also teaming up with Andreas Adner to explore LLM‑as‑judge so a model can score answers for relevance, completeness, and groundedness during these runs. PS: I’m nowhere near an ALM expert. Most of what I’m doing comes from learning from people like Matthew Devaney, Parvez Ghumra, and Sean Astrakhan. Grateful for them sharing so much. Image credit: Microsoft #copilotstudio #copilotstudiokit