Automated Functional Testing Solutions

"Quality starts before code exists", This is how AI can be used to reimagine the Testing workflow Most teams start testing after the build. But using AI, we can start it in design phase Stage - 1: WHAT: Interactions, font-size, contrast, accessibility checks etc. can be validated using GPT-4o / Claude / Gemini (LLM design review prompts) - WAVE (accessibility validation) How we use them: Design files → exported automatically → checked by accessibility scanners → run through LLM agents to evaluate interaction states, spacing, labels, copy clarity, and UX risks. Stage - 2: Tools: • LLMs (GPT-4o / Claude 3.5 Sonnet) for requirement parsing • Figma API + OCR/vision models for flow extraction • GitHub Copilot for converting scenarios to code skeletons • TestRail / Zephyr for structured test storage How we use them: PRDs + user stories + Figma flows → AI generates: ✔ functional tests ✔ negative tests ✔ boundary cases ✔ data permutations SDETs then refine domain logic instead of writing from scratch. Stage - 3: Tools: • SonarQube + Semgrep (static checks) • LLM test reviewers (custom prompt agents) • GitHub PR integration How we use them: Every test case or automation file passes through: SonarQube: static rule checks LLM quality gate that flags: - missing assertions - incomplete edge coverage - ambiguous expected outcomes - inconsistent naming or structure We focus on strategy -> AI handles structural review. Stage - 4: Tools: • Playwright, WebDriver + REST Assured • GitHub Copilot for scaffold generation • OpenAPI/Swagger + AI for API test generation How we use them: Engineers describe intent → Copilot generates: ✔ Page objects / fixtures ✔ API client definitions ✔ Custom commands ✔ Assertion scaffolding SDETs optimise logic instead of writing boilerplate. THE RESULT - Test design time reduced 60% - Visual regressions detected with near-pixel accuracy - Review overhead for SDETs significantly reduced - AI hasn’t replaced SDETs. It removed mechanical work so humans can focus on: • investigation • creativity • user empathy • product risk understanding -x-x- Learn & Implement the fundamentals required to become a Full Stack SDET in 2026: https://lnkd.in/gcFkyxaK #japneetsachdeva

Testing with AI: Post 4 ------------------------- Continuing my Testing with AI series, today I’m sharing another amazing feature recently introduced by the KushoAI team, which is UI Testing. Their innovative Chrome extension simplifies end-to-end testing workflows by allowing you to select UI elements directly from your browser. With the power of large language models (LLMs), it enables you to: (i) Generate test ideas tailored to your application's functionality. (ii) Write automation scripts—currently supporting Playwright (.ts files), with plans to expand to Selenium and Cypress soon. (iii) Download the complete test project to your local machine for execution. My Experience with Kusho AI’s UI Testing Here’s what impressed me the most: (1) Smart Element Detection Simply click on UI elements in your application, and Kusho AI instantly identifies and processes them for testing. (2) Comprehensive Test Case Generation It automatically generates both functional and edge test cases, eliminating the need for manual test creation. (3) Automation Code Ready to Use For each test case, Kusho AI provides ready-made Playwright scripts. Support for Cypress, Selenium, and other tools is coming soon! (4) Real-Time Test Enhancements (A standout feature!) You can generate additional test cases on demand, based on your selected page or component. (5) Great for Beginners & Experts If you're new to automation, Kusho AI provides a solid starting point. For experienced testers, it saves time by automating repetitive tasks and ensuring robust test coverage. Instead of worrying about AI taking over, let’s leverage tools like Kusho AI to automate tedious tasks and focus on higher-value testing strategies. Try it out and see how much time and effort you can save! If you find it useful, repost to help others in the testing community. Link - https://kusho.ai/ P.S. Kusho AI is free for individual users, with an Enterprise model available for larger teams. I’ve attached some screenshots to give you a glimpse of its capabilities!

As part of my test automation exploration, I recently worked with `https://www.saucedemo.com` using testRigor, a no-code/low-code AI-driven test automation platform. I initially wrote a simple functional test case: "Verify user can login to the application with 'standard_user' role. Later, I explored Corner Test Case Generation – an AI-powered feature that intelligently creates edge case tests. testRigor generated: 1. "Verify user cannot login to the application with skipping 'Password'." 2. "Verify user cannot login to the application with 'standard_user' role skipping 'Username'." These were auto-labeled as 'Corner Case', helping to strengthen test coverage without manual effort. Later, I tried the "Generate Test Cases Based on Feature Description" option by uploading a screenshot of the product page. From this, testRigor generated: - "Verify that a user can successfully add a product to the cart and view it in the cart page." What’s in the demo video? - Live walk-through of test suite execution of login related test cases. If you're looking to accelerate QA and reduce manual effort, this is definitely worth exploring!

Traditional automated testing promises efficiency, but the reality is that tests crumble at the slightest UI change. It’s an all too common scenario: Spend weeks writing the perfect test, only for a minor button update to make half your test flash red. This ensues a cycle of constant firefighting that leaves QA teams exhausted and quality taking a hit. But what if tests could evolve as your product does? 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝗵𝗶𝗻𝗲𝘀. At testRigor, we’ve helped companies like Netflix and Cisco reduce their reliance on implementation details and make their tests more stable and easier to maintain. We do this by marrying AI’s adaptability with human context. How? By allowing tests to be written in plain English. This approach doesn’t just make tests more stable — it captures nuances that often slip through the cracks of traditional automation. Product managers gain direct visibility into test cases, finally bridging the gap between vision and execution. Developers receive clear, actionable feedback, pinpointing issues accurately. QA team tackles complex edge cases and lets AI handle the grunt work. The result? A virtuous cycle of faster iterations, better products, and happier customers. Make your QA process an accelerator, not a bottleneck >> https://lnkd.in/eijgpWTj #AI #Automation #softwareengineering #softwareengineering