Automated Testing Procedures

I Spent 6 Hours Manually Testing One VRM's Transient Response. PulseLoad Pro Did It Better in 60 Seconds. ⚡ That's right. What used to take me an entire afternoon now takes literally one minute. And the data quality? Superior in every way. Here's what manual testing looks like: • Adjust waveform generator frequency • Tweak oscilloscope trigger settings • Capture waveform (if you're lucky) • Screenshot and save • Repeat 20-50 times • Compare results manually • Miss critical data between test points The reality? Most engineers give up after 5-10 measurements. You're flying blind on your VRM's actual performance envelope. Enter PulseLoad Pro - our new test automation software that's changing everything: • Fully adjustable frequency sweep (you set the range) • Intelligent triggering that maximizes dynamic range • Captures unlimited waveforms (we showed 24 in our demo) • Auto-scales to prevent clipping • Identifies worst-case response automatically • Generates comprehensive data plots instantly In our latest demo (video in the blog), we tested a standard buck converter. The software found critical instability at 40kHz that manual testing would have missed entirely. It displayed the problematic frequency plus 7 surrounding waveforms for context - all automatically. The killer feature? Automatic triggering. No more fiddling with trigger levels or missing transients because your scope settings weren't optimal. The software handles it all, ensuring you capture every critical event with maximum resolution. Real numbers from our lab: • Manual testing: 6 hours for 20 data points • PulseLoad Pro: 1 minute for 24+ data points • Time saved: 99.7% • Data quality improvement: Immeasurable This isn't just about saving time (though saving 5+ hours per test is huge). It's about getting data you'd never capture manually. Complete frequency response profiles. Automated worst-case identification. Publication-ready plots. Ready to see it in action? Check out our detailed blog with the demo video showing the entire automated process: https://lnkd.in/eAb_A-9B Want the complete solution? Get the PulseLoad Pro Product Bundle - everything you need for automated power supply testing in one package: https://lnkd.in/e8A7PbmJ Stop wasting time on manual measurements. Start getting better data in less time. 💪 #powerintegrity #testautomation #vrm #measurementsolutions #signaledgesolutions #powerelectronics #hardwareengineers #electricalengineers #oscilloscope #testandmeasurement

Before you automate API tests, you need to understand the API first. APIs don’t have a UI. You can’t just click around and see what happens. That’s why most QA engineers struggle with API automation - they skip the exploration phase and jump straight to writing code. The right workflow: 1. Explore the API Use these tools to understand what the endpoints do: 📌 Browser Network Tab - See real API calls your app makes ∙ Right-click → Inspect → Network tab ∙ Watch live requests/responses ∙ Copy exact headers, payloads, status codes 📌 Swagger UI - Interactive API documentation ∙ Auto-generated from backend code ∙ Shows all available endpoints ∙ Try requests directly in the browser ∙ See example responses 📌 Postman - Manual API testing tool ∙ User-friendly interface for building requests ∙ Set headers, params, request bodies ∙ View responses in detail ∙ Save and organize API calls 2. Verify with Postman Once you understand the endpoint: ∙ Recreate the request in Postman ∙ Verify it works as expected ∙ Test different scenarios manually ∙ Document the expected behavior 3. Write automation code Now you can automate with confidence: ∙ You know what the endpoint does ∙ You know what success looks like ∙ You know what edge cases to test ∙ Your tests will be realistic and reliable The mistake most QAs make: Writing API tests without understanding the API first. Then wondering why tests are flaky or don’t catch real bugs. Bottom line: Manual exploration → Postman verification → Automation code Skip the first two steps, and your automation will be guesswork. Learn API testing + automation with Playwright in our free community 👉 https://lnkd.in/gqSnguXu #QA #TestAutomation #APITesting #Postman #Swagger #SDET #SoftwareTesting #AutomationTesting #Playwright

🚀 How to Automate SOX Testing With RPA (Robotic Process Automation) SOX testing doesn’t have to feel like a quarterly fire drill. With RPA, you can automate evidence collection, control testing, and documentation — freeing your IT, Finance, and Audit teams to focus on analysis, not admin work. Here’s how forward-thinking audit and risk teams are doing it 👇 1️⃣ Map and Prioritize Controls Identify repetitive, rule-based SOX tests — like access reviews, change management, and key report validations — that can be automated first. 2️⃣ Design “Audit-Proof” Bots Document every bot like a control: purpose, inputs/outputs, logs, and approvals. Treat bot logic changes as in-scope for SOX. 3️⃣ Build Securely Use vaults for credentials, enforce least privilege, and integrate bots into your GRC or evidence repository. 4️⃣ Test and Validate Compare bot outputs to human results (UAT). Capture logs, screenshots, and timestamps for every run. 5️⃣ Monitor and Improve Set quarterly “Bot Health Reviews” to track exceptions, false positives, and ROI. ⚙️ Common RPA Use Cases for SOX User Access Reviews — auto-pull users, compare to HR, generate exceptions Change Management — match commits to approvals and deployments Key Report Testing — re-execute reports and hash results Backups/Job Monitoring — verify completion and collect evidence ⚠️ Key Challenges Data quality issues → fix upstream, validate populations Credential sprawl → dedicated bot IDs + vaulting Change control gaps → ticket every update Auditor reliance → document bot design + test scripts ✅ Outcome: Organizations are cutting SOX testing time by 50–70%, reducing human error, and providing auditors with complete, timestamped evidence bundles every quarter. 💡 Pro tip: Start small — automate 3–5 high-ROI controls first, measure results, and scale. #SOXCompliance #InternalAudit #RPA #TechRisk #Automation #AuditInnovation #CISO #GRC #ITAudit #DigitalTransformation

🛠️ What Running Test Automation Involves 🔎 📌 On-Demand Test Automation: This approach allows teams to execute test automation whenever there is a requirement to do so. It can be integrated into various stages of the development process, such as during product development, the addition of new features, or when there are new developments in testing methodologies. 📌 Timed Test Automation: Test automation can be triggered based on time. Initially, automation may take minutes due to fewer iterations, but as the number of iterations and version numbers increases, it may take hours. Running automation tests overnight is a common practice to analyze new changes to the software. 📌 Activity-Based Test Automation: As the application grows, developers shift from time-based triggers to activity-based triggers. The goal here is to target changes in the application, which can include updates, new features, or modifications to the existing features. 📌 Regression Testing: Test automation is particularly useful for regression testing, where previously implemented functionalities are tested to ensure that new changes or updates haven't introduced any unintended side effects or regressions. 📌 Parallel Execution: To speed up the testing process, automation tools often support parallel execution of test cases across multiple environments or devices. Parallel execution helps reduce the overall testing time, allowing teams to achieve faster feedback cycles and accelerate time-to-market for their products. 📌 Integration with Continuous Integration/Continuous Deployment (CI/CD): Test automation can be seamlessly integrated into CI/CD pipelines to automate the testing process as part of the overall software delivery pipeline. Automated tests can be triggered automatically whenever new code changes are committed, ensuring that each code change is thoroughly tested before deployment to production. 📌 Reporting and Analysis: Test automation tools often provide detailed reports and analytics on test execution results, including test coverage, pass/fail status, execution time, and more. These reports help stakeholders make informed decisions about the quality of the software and prioritize areas for improvement. 📌 Maintenance and Refactoring: Test automation requires ongoing maintenance and refactoring to keep test suites up to date with changes in the application codebase. As the application evolves, test scripts may need to be updated or refactored to accommodate new features or changes in functionality. 📌 Scalability and Flexibility: Test automation frameworks should be scalable and flexible to accommodate the evolving needs of the organization and the application. Scalable automation frameworks can handle large test suites efficiently, while flexible frameworks allow for easy customization and extension to support new testing requirements.

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.

Robot + Test Automation Tool + Microcontroller = Relief Hardware automation testing challenges had me in a chokehold for years. Until I discovered this approach: The problem: Manual hardware testing controlled my workflow. Cost me: Time, accuracy, and scalability Affected: Product development cycles and quality assurance Tried everything: Manual checks, partial automation, inconsistent results The breakthrough process: 1. Integrated microcontroller-based automated testing systems Immediate relief: Consistent and rapid evaluations with reduced human error. 2. Leveraged robotic automation for physical device testing Momentum built: Enhanced device verification through simulation of real-world conditions. 3. Combined software and hardware testing automation frameworks Freedom achieved: Streamlined processes with scalable, repeatable testing protocols. Result after implementation: → Improved accuracy and reliability in hardware validation → Reduced testing time and operational costs → Accelerated product development and higher quality standards The secret? Harnessing the synergy of microcontrollers and robotics to automate complex hardware testing tasks, enabling precise, efficient, and scalable verification. Your testing challenges have a solution. You just haven't implemented it yet. What hardware-related challenges does your automation testing process face? #TestAutomation #SoftwareTesting #QualityAssurance #TestMetry

500+ tests running daily. Zero manual trigger. Confidence up. Stress down. When I joined PLANOLY, the product complexity was growing fast. But the automation coverage wasn’t keeping up. Releases started to feel risky and took long. I built a scalable Cypress framework from scratch. It followed the Page Object Model and supported data-driven testing. It was designed to be maintainable for the long haul. Next, I integrated it with GitHub Actions. Full test suites now run automatically on nightly builds so I can see the result first thing in the morning. The result? Over 500 automated tests running multiple times a week. They provide fast feedback, catch regressions early, and boost confidence across the team. If you’re working on scaling test automation, I’m happy to share lessons from this experience. What’s the biggest challenge you’ve faced with automation at scale?

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

📌 Manual ETL testing in data warehouse projects can lead to delays in project timelines, accumulation of bugs, and increased project costs. Whereas, Automated ETL testing can: → significantly streamline the testing process,  → save considerable time and resources,  → ensure more reliable and efficient project outcomes. Here is how you can automate ETL testing: 1️⃣ Choose the right tools: When selecting tools, consider: → Data Comparison Tools: Identify discrepancies between source and target data sets. → ETL Testing Frameworks: Provide structure and reusability to automate test cases, scenarios, and workflows. 2️⃣ Outline the test strategy and scope: → Determine Test Coverage: Identify which ETL components, data elements, and transformations to test and frequency → Use Realistic Test Data: Reflect real-world data source/target conditions; synthetic, sample or production data. → Set Up Test Environment: Mimic production environment closely with cloud or on-premise servers, databases, ETL tools. 3️⃣ Develop & implement test cases: Well-designed test cases are key to cover ETL functionality, performance, and security: → Data Quality Checks: Validate data validity, consistency, completeness, and accuracy → Data Transformation Checks: Assess correctness of ETL logic and mappings → Data Loading Checks: Verify efficiency and reliability of loading process (e.g. load times, volumes, errors) 4️⃣ Execute and monitor test cases: → Schedule Test Runs: Automate execution of test scripts → Review Progress Dashboards: Monitor status and results in one view → Follow Best Practices: Use integrated tools, customize test parameters, enable debugging, etc 5️⃣ Review and Report Test Results: → Generate Test Reports: Highlight key findings and insights through charts and graphs. → Utilize Visualization Tools: Connect to test data and ETL tools; enable drilling down into metrics. → Share Interactive Reports: Support collaboration; allow exporting and publishing of final reports Have you considered automating your ETL testing process to save time and resources?