End-to-End Testing Automation

Playwright End-to-End Automation with Azure DevOps Multi-Stage CI/CD Modern test automation is not just about writing tests — it’s about running them reliably in CI/CD. Sharing a detailed reference that demonstrates how to build end-to-end Playwright automation and integrate it with a multi-stage Azure DevOps pipeline for real-world projects. What this guide covers: Playwright setup & configuration (JS / TS) UI automation: locators, waits, frames, popups, files Network interception & API testing with Playwright Authentication & storage state handling Data-driven tests, fixtures & Page Object Model (POM) Screenshots, videos, traces & reporting Parallel execution & retries Azure DevOps YAML pipelines Multi-stage CI (Build → Test → Publish) Cross-browser execution (Chromium, Firefox, WebKit) Publishing HTML & JUnit reports in ADO 🎯 This is especially useful for: ✔ QA Engineers ✔ Automation Testers ✔ SDETs ✔ Teams implementing Playwright in CI/CD ✔ Interview preparation for Playwright + DevOps roles 📌 Sharing this for professional learning — hoping it helps someone design scalable, CI-ready automation frameworks. 🔔 Follow me for more updates on Playwright, Automation, CI/CD, and QA learning resources. #Playwright #AutomationTesting #AzureDevOps #CICD #SDET #QA #TestAutomation #EndToEndTesting #DevOps #SoftwareTesting #QualityEngineering #LearningAndSharing

This is a post I’ve known I’d have to write for a long time, but I wasn’t sure when. Initially it felt a little doomy, but the longer I sit with it, the more excited I get. We’ve officially entered the era of instantaneous end-to-end test automation, and it’s good. Read this whole post before you judge me. Today, I used contitest to generate 150 tests in the exact format that Magnitude.run expects. The entire process took about an hour. Out of those 150 tests, 148 worked as one-shot e2e automated tests. I fixed the remaining two manually. I’ve never had anything close to this experience with Playwright locators, Cursor running MCPs, or traditional automation workflows. To summarize: I pulled structured, meaningful test cases out of the app using ContiTest, and then ran them with Magnitude. It just… worked. And before anyone says it: this is not like using a test recorder. Recorded tests are brittle, unmaintainable, and practically impossible to scale. What we’re doing here is fundamentally different: Cursor is writing actual code, not generating brittle recordings. The tests are maintainable, editable, and evolvable — not frozen artifacts. And the execution is locator-free, which means we aren’t constantly firefighting. This is code-driven test automation paired with AI-driven test planning, not some toy recorder outputting JSON spaghetti. It’s dramatically more maintainable and infinitely more adaptable. Now, the obvious criticisms: “It’s slow.” “It’s expensive.” “It won’t scale.” But here’s the point I made two days ago that annoyed a bunch of developers: Business people do not care whether the tests are slow or expensive. They care about coverage, risk reduction, and time-to-confidence. If the tradeoff is: Massive, immediate coverage that costs a bit more to run vs. Months of handcrafted automation that still doesn’t cover the real paths users take Businesses will choose coverage every single time. Slow tests are manageable. Expensive tests are manageable. No coverage is not. The next interesting shift is this: Now that we’ve removed locators as the bottleneck, the choke points move to test planning and test data. And that’s the part no one wants to talk about. When the generation and execution of tests take an hour instead of a quarter, test data suddenly becomes the critical path. You still need stable test data, seeded states, and infrastructure that can support these flows. The complexity doesn’t disappear, it just moves to a more meaningful place. But that’s exactly why this model works: The problems shift from low-value locator firefighting to high-value test orchestration. The next step for me will be migrating the very long flows into traditional Playwright, where optimization is worth the effort. I’m going to experiment with running most of our testing through this approach and will report back.

As a backend developer, my job doesn’t end at writing an API–it starts there.  Once the API is developed, the real challenge begins: testing. Thinking through every possible scenario where it could fail. Manually setting up test cases in Postman. Verifying if it works across different environments. Ensuring role-based access control behaves as expected. And if the API internally calls other APIs? That means setting up an entirely different set of tests.  It’s a time-consuming, tedious process. And let’s be honest, testing has never been the most exciting part of development.  Recently, I came across KushoAI, and it completely changed my perspective. Instead of manually writing and managing test cases, I could just provide my API details–a cURL request, Open API specs, Postman collection, or HTTP request–and Kusho would do the rest.  - It auto-generates test cases, covering even those edge cases I might have missed.   - It runs tests across different environments effortlessly.   - It handles user roles and access governance without extra setup.   - It even lets me create end-to-end test suites by simply defining API workflows, ensuring everything works together.  What used to take hours of effort now happens in minutes. And the best part? I no longer have to second-guess whether my API will work in production.  But that’s not all–Kusho.AI isn’t just transforming API testing, it’s redefining UI testing too. Imagine this: instead of manually writing long automation scripts or spending hours thinking of test scenarios, you just record your user journey using the Kusho.AI Chrome extension–and that’s it! Kusho automatically generates comprehensive Playwright test scripts for you. -  Use the app like a real user–Kusho does the rest. -  No need to write automation scripts–it’s all generated for you. -  Find UI bugs before they reach production. Now, software developers and testers can validate their front-end effortlessly–having to write a single line of automation code. Kusho.AI is making testing smarter and efficient. Whether you’re testing APIs or UIs, it’s a game-changer for developers who want to ship reliable software faster and with confidence. Check it out here: https://kusho.ai/ #SoftwareTesting #API #UITesting #Automation #KushoAI

𝐂𝐑𝐔𝐃 𝐢𝐧 𝐏𝐥𝐚𝐲𝐰𝐫𝐢𝐠𝐡𝐭 + 𝐓𝐲𝐩𝐞𝐒𝐜𝐫𝐢𝐩𝐭 𝐀𝐏𝐈 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 (𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝) Most teams use Playwright for UI automation, but it’s also a clean, fast way to do API testing—including CRUD (Create, Read, Update, Delete)—and then validate end-to-end (E2E) outcomes (API → system state → optional UI). Why Playwright for API CRUD? • One runner for API + UI (same assertions, reporting, retries) • Fast feedback (API tests run quicker than UI) • Easy setup/teardown (create data via API, validate via API/UI, clean up) 𝐌𝐚𝐤𝐞 𝐢𝐭 𝐭𝐫𝐮𝐥𝐲 “𝐄2𝐄” • Add auth (token/cookies) in a Playwright fixture so all tests reuse it. • Keep tests idempotent: generate unique data, and always clean up (even on failure—use test.step or try/finally). • Optionally validate the UI: create via API → open UI → confirm the record appears. If you’re already using Playwright for UI, you can get reliable API CRUD coverage with the same tooling—and even stitch it into full E2E flows without adding a separate framework. Note - We already covered the 𝐆𝐄𝐓 request in the previous post - please check it. Comment - "𝐂𝐑𝐔𝐃" - if you now understand how to dynamically pass objects in APIs. #Playwright #TypeScript #APITesting #E2ETesting #CRUD #QA #AutomationTesting