Using Auto-Generated Test Frameworks in Software Development

🎭 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

💡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

The first open-source implementation of the paper that will change automatic test generation is now available! In February, Meta published a paper introducing a tool to automatically increase test coverage, guaranteeing improvements over an existing code base. This is a big deal, but Meta didn't release the code. Fortunately, we now have Cover-Agent, an open-source tool you can install that implements Meta's paper to generate unit tests automatically: https://lnkd.in/eCitDjin I recorded a quick video showing Cover-Agent in action. There are two things I want to mention: 1. Automatically generating unit tests is not new, but doing it right is difficult. If you ask ChatGPT to do it, you'll get duplicate, non-working, and meaningless tests that don't improve your code. Meta's solution only generates unique tests that run and increase code coverage. 2. People who write tests before writing the code (TDD) will find this less helpful. That's okay. Not everyone does TDD, but we all need to improve test coverage. There are many good and bad applications of AI, but this is one I'm looking forward to make part of my life.