Using Auto-Generated Test Frameworks in Software Development

I migrated a legacy Selenium C# framework to Playwright TypeScript using 5 AI agents built in Claude. No manual rewrites. No endless maintenance cycles. No weekend migration war rooms. Just an agentic pipeline that analyzed, converted, validated, and healed the framework automatically. Here’s how it worked 👇 The first agent was the Audit Agent. It scanned the entire Selenium framework, analyzed dependencies, identified duplicate patterns, evaluated migration complexity, and generated a prioritized execution plan. Instead of blindly converting everything, it created an intelligent migration roadmap. Next came the Conversion Agent. It transformed Selenium C# test cases into Playwright TypeScript while preserving assertions, workflows, and business logic — adapting everything to Playwright-native patterns automatically. Then came the most interesting part — the Selector Intelligence Agent. This wasn’t simple find-and-replace automation. The agent opened the actual application, inspected the live DOM, analyzed accessibility attributes, and intelligently decided whether each element should use: • getByRole() • getByLabel() • getByText() • getByTestId() That’s not script conversion. That’s contextual reasoning. The POM Generation Agent automatically created scalable Page Object Models across the framework. Consistent naming conventions. Reusable methods. Playwright-native waits. Cleaner architecture. What normally takes days of repetitive framework work was generated in minutes. Finally, the Self-Healing Agent continuously monitored failures after execution. If a selector changed or synchronization broke, the agent analyzed the failure context, identified the probable root cause, and proposed or generated fixes automatically. Instead of debugging from scratch, teams reviewed AI-generated solutions. Five focused agents. Each specialized. Each feeding context into the next. That’s what made the system reliable. Not one giant AI prompt trying to do everything — but multiple focused agents collaborating like an engineering team. Most QA teams are still migrating frameworks manually in 2026. AI is no longer just helping testers write scripts. It’s starting to redesign how entire QA transformations happen. If you’re also exploring AI agents in test automation or framework modernization, drop a comment — would love to hear your thoughts and experiences. #Playwright #Selenium #DotNet #CSharp #TypeScript #AIAgents #ClaudeAI #TestAutomation #QAAutomation #SoftwareTesting #QualityEngineering #AIinTesting #AgenticAI #AutomationFramework #TestArchitecture #QAEngineering #DevOps #GenerativeAI #LLM #EngineeringLeadership #ShiftLeft #ContinuousTesting #PageObjectModel #SoftwareEngineering #TechLeadership #FutureOfTesting #Innovation #CloudTesting #AutomationEngineering #AI Hope it helps anyone exploring AI-driven test automation migration. 🚀 GitHub Repository-https://lnkd.in/gBJh9vKt

🎭 Implementing Playwright Test Agents: My Journey & Insights I recently implemented an AI-driven test automation framework using Playwright Test Agents to automate flight booking flows on BlazeDemo. 🛠️ What I Built I created a multi-agent Playwright automation framework that mimics how a human QA analyst, developer, and maintainer would collaborate: 🧭 Planner Agent → Explores the app and generates a Markdown-based test plan with multiple scenarios and user flows. ⚙️ Generator Agent → Converts the plan into executable Playwright tests, validating selectors and assertions live. 🩺 Healer Agent → When a test fails, it replays, diagnoses, and suggests patches (locator fix, wait, or data tweak) to self-heal the test. ✅ Automated end-to-end flight booking flow 💡 Key Benefits Discovered Accelerated STLC ⏱️ Reduced test planning time by ~40% 🤖 Auto-generated test scripts from Markdown plans 🛡️ Built-in self-healing for failing tests Enhanced Test Coverage 🔍 Broader and deeper scenario coverage ⚡️ Automatic edge case detection 📋 Consistent structure through AI-guided plans 📈 What Worked Well 🌟 Generator Agent delivered reliable, structured test cases with selectors. 🗂️ Markdown-based planning improved visibility and reusability of scenarios. 🧩 AI coordination between agents reduced manual QA effort significantly. ⚡️ Pro Tips 🔧 Ensure your MCP server is properly initialized before running the Planner Agent. 🧭 Review and refine Markdown test plans before execution. 🧪 Start with small, focused scenarios. 📝 Document your setup for reproducibility. 📚 Resources That Helped 📖 Official Docs → https://lnkd.in/gEii8fNU 🎥 Tutorial → YouTube: Playwright Test Agents Overview 👉 How AI-Powered Playwright Agents Fit Into the Traditional STLC — kailash-pathak.medium.com 🤔 Personal Takeaway This is my initial analysis — I still have a lot to learn to get a more mature, well-rounded understanding. But early results show promising potential in how AI can reshape test automation. 🙏 Special thanks to Debbie O'Brien and Kailash Pathak for guiding through the implementation of this framework. Your insights and support were invaluable! Git Repo : https://lnkd.in/gEP-9ceH #Playwright #TestAutomation #QA #Testing #STLC #TypeScript #QualityAssurance #AutomationTesting #AIinTesting #TechInnovation #SoftwareTesting

"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

In modern software development, writing code is only half the job — testing it is just as critical. But as codebases grow, maintaining strong unit test coverage becomes increasingly challenging. A recent engineering blog from The New York Times explores an interesting approach: using generative AI tools to help scale unit test creation across a large frontend codebase. - The team built an AI-assisted workflow that systematically identifies gaps in test coverage and generates unit tests to fill them. Using a custom coverage analysis tool and carefully designed prompts, the AI proposes new test cases while following strict guardrails — such as never modifying the underlying source code. Engineers then review and refine the generated tests before merging them. - This human-in-the-loop approach proved surprisingly effective. In several projects, test coverage increased from the low double digits to around 80%, while the time engineers spent writing repetitive test scaffolding dropped significantly. The process also follows a simple iterative loop: measure coverage, generate tests, validate results, and repeat. The experiment also highlighted some limitations. AI can hallucinate tests, lose context in large codebases, or produce outputs that require careful review. The takeaway: AI works best as an accelerator — not a replacement — for engineering judgment. As these tools mature, this kind of collaborative workflow may become a practical way for teams to scale reliability without slowing down development. #DataScience #MachineLearning #SoftwareEngineering #AIinEngineering #GenerativeAI #DeveloperProductivity #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gj9fc322

Testing with AI: Post 3 ------------------------- Continuing my Testing with AI series, today I’m sharing another amazing AI-powered assistant in software testing—TestCraft. What is TestCraft? In their own words: ============================ TestCraft is a Chrome extension designed to be a companion to your software testing. With TestCraft, you can select UI elements directly from your browser and leverage the capabilities of large language models (LLMs) to generate innovative test ideas, write automation scripts across various frameworks and programming languages, and perform accessibility checks. What makes TestCraft special? ============================ 1) It’s open source—easy to start, and free to explore. 2) You can install it as a Chrome extension, select elements directly on the page, and automate them effortlessly. 3) It supports popular frameworks like Cypress, Selenium, and Playwright. 4) Programming languages supported: JavaScript, TypeScript, Java, C#, and Python. My experience with TestCraft: ====================== I tried using it with Cypress and JavaScript, and here’s what I found: (1) It automatically generated a variety of test cases, including edge cases, which saved me a lot of time. (2) The tool provided a great starting point, though some minor tweaks were needed—particularly around error messages. But honestly, that wasn’t a big deal. (3) For beginners in automation, this tool is fantastic, offering a solid base on which to build. (4) Even experienced testers will find it useful, as it generates a comprehensive list of test cases, saving time on repetitive tasks. The best part? It’s so simple to use! Just install the extension, select elements on the page, and let TestCraft do the rest. While it’s not perfect yet (minor adjustments are required), it’s worth trying out. I’ve attached some screenshots from my experience with the tool to give you an idea. Give it a shot—you might just save yourself hours of work! Link -> https://lnkd.in/gZityxRR #TestingWithAI #TestCraft #AutomationTools #Cypress #Selenium #Playwright #Efficiency #ABAutomationHub

💡Meta's research introduces 𝗔𝗖𝗛 (𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗖𝗵𝗲𝗰𝗸 𝗳𝗼𝗿 𝗛𝗮𝗿𝗱𝗲𝗻𝗶𝗻𝗴), a new 𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 using LLMs for generating more 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝘂𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀. ACH uses 𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 to generate 𝘁𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝘀 that can detect specific issues, like privacy vulnerabilities, and ensures they are buildable, reliable, and meaningful. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴? • 𝗠𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 helps identify gaps in test coverage by introducing small changes (mutants) to the code, which are then checked by the test cases. • 𝗟𝗟𝗠𝘀 are used to 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝘁𝗲𝘀𝘁𝘀, making the process faster and more efficient, with a focus on issues like privacy and security. • The method results in 𝗯𝗲𝘁𝘁𝗲𝗿 𝗰𝗼𝘃𝗲𝗿𝗮𝗴𝗲, ensuring that tests are actually catching bugs and improving code quality before release. As someone building in this space, this research is a great reminder of 𝗵𝗼𝘄 𝗔𝗜 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁𝗲𝗿—𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗳𝗮𝘀𝘁𝗲𝗿. We're on it to make it generally available with Keploy 🐰 🔜.🔥 The idea of hardening code against potential vulnerabilities through automated, AI-driven tests sounds promising, let's take testing beyond traditional approaches. 🚀 Check out the full paper: “Mutation-Guided LLM-based Test Generation at Meta” https://lnkd.in/gUWgbvgB #AI #MutationTesting #LLM #SoftwareTesting #Security #Keploy

💡 Building an Agent to Automate Testing and Documentation Last month, during our team offsite, we took a step back from our usual tasks to tackle a challenge all developers know too well—writing unit tests and documentation. However, this time, we weren’t doing it manually. Instead, we built an AI agent (a favorite of all, these days) using aiXplain’s agentic framework to do the heavy lifting for us. Our goal was to create an agent that could dive into a codebase, auto-generate comprehensive unit tests, and even write the README documentation for a GitHub repo. In a few days, we had a prototype running that transformed a folder of Python files into fully tested, documented code with just a few commands. Along the way, we ran into the usual hurdles—prompt engineering, output parsing, and handling large directories but overcoming these was part of the fun. It gave us a chance to see just how far we could push automation with aiXplain’s tools. Check out the full story [LINK IN COMMENTS] of how we built it, the challenges, and what are we building next, to streamline developer workflows through AI. Thanks to my team Lucas, Muhammad Elmallah and Daniel Nelson. What other development tasks would you want to see automated with AI agent? Let’s hear your thoughts!

🚀 EPISODE 2 — GitHub Copilot for Test Automation (AI for QA Engineers) 🤖💻 Welcome to Episode 2 of my series: #TestingSmarterWithAI 🚀 Today’s AI tool is one of the most practical and widely used tools for Automation Engineers: 🔥 GitHub Copilot If you work on Selenium / API Automation / Framework development, you already know how much time goes into writing repetitive code like: ✅ Locators & Page methods ✅ Test scripts ✅ Assertions ✅ Utility functions ✅ API request payloads ✅ JSON parsing ✅ Wait conditions ✅ Test data handling That’s exactly where Copilot becomes a real productivity booster. 🧠 My Real-Time Experience Using Copilot in Automation In real projects, we often deal with: 📌 tight sprint deadlines 📌 multiple test cases to automate quickly 📌 framework enhancements along with testing ✅ How I Use Copilot Step-by-Step in Real Time Here’s my practical workflow: 🔹 Step 1: Create the test file / class structure Example: test_login.py or LoginTests.java Copilot automatically suggests the full template with imports, class name, and structure. 🔹 Step 2: Start writing Page Object methods As soon as I type something like: def enter_username(): Copilot suggests: ✔️ locator ✔️ action method ✔️ return statement ✔️ exception handling This saves a lot of time when building Page Object Model. 🔹 Step 3: Generate reusable utilities Whenever I need utilities like: 📌 wait functions 📌 scroll methods 📌 screenshot capture 📌 retry logic 📌 common assertions I just write a method name and a short comment like: # wait until element is clickable Copilot suggests the complete code block with best practices. 🔹 Step 4: Framework Enhancement & Refactoring One of the best things I noticed is how Copilot helps while enhancing frameworks, like: 📌 converting repeated code into reusable functions 📌 improving exception handling 📌 suggesting clean design patterns 📌 creating config/environment handling logic This is very helpful when frameworks grow sprint by sprint. 🧪 Realistic Automation Example 📌 Requirement: “Verify user can login successfully and invalid login shows correct error message.” Instead of manually writing everything, Copilot helped generate: ✅ loginPage.enterUsername() ✅ loginPage.enterPassword() ✅ loginPage.clickLogin() ✅ wait conditions + expected text assertions From my experience, Copilot is most useful in: 📌 Framework development 📌 Creating Page Object Model faster 📌 Writing reusable utility functions 📌 Refactoring framework code 📌 BDD step definition templates 🎯 Key Takeaway GitHub Copilot is like a coding assistant that helps automation engineers work smarter. 💬 Question for the community: Have you tried GitHub Copilot in Selenium/API automation or framework development? If yes, which part of your work did it help the most? #AutomationTesting #Selenium #GitHubCopilot #AIinTesting #QA #SDET #SoftwareTesting #TestAutomation #FrameworkDevelopment #TestingSmarterWithAI #TechCommunity

MCP Server Delivers AI-Generated Unit Tests and Advanced Fuzz Testing The server generates intelligent unit tests with proper edge cases, performs AI-powered fuzz testing to identify potential crashes, and conducts advanced coverage testing for maximum code path analysis. Each function receives 4-6 test cases while boundary testing uses 20 diverse inputs to probe system limits. The server combines BAML's structured generation with Gemini's language understanding, built on the FastMCP framework. It performs AST-based code analysis to detect branches, loops, and exception paths while integrating coverage.py for real-time reporting. The modular architecture allows teams to extend testing capabilities as needed. Software reliability becomes measurable and achievable at scale. Automated testing reduces manual QA overhead while catching edge cases that human testers might miss. Development cycles accelerate without sacrificing code quality, making robust software testing accessible to teams of any size. Vaibhav Gupta 👩💻https://lnkd.in/eHqRAJ38