Playwright Test Automation Framework Developed With Various AI Tools

In this article, we will discuss to generate frontend test automation framework with Playwright and JavaScript on saucedemo website using AI tools as listed below.

🧪 Experiment: Building a Playwright Automation Framework with Five AI Tools

Over the last few weeks, I’ve been exploring the evolving landscape of AI and its impact on software testing, particularly how AI can accelerate the creation of test automation frameworks. Given the traditional complexity and time investment involved, I was curious: Can AI really help us build a robust, maintainable framework in minutes instead of days?

To answer this, I conducted a small experiment by providing the same detailed prompt to five different AI tools and analyzed their outputs.

📝 The Prompt Given

“Create a test automation framework for Playwright (JavaScript) on Swag Labs with Page Object Design Pattern and tests.”

This prompt was designed to test each tool’s ability to generate not only reusable Page Object classes but also accompanying tests — all structured within a proper framework.

🤖 The AI Tools I Tested

• Perplexity.ai

• Warp.dev

• V0.app

• Manus.im

• Qwen.ai

Each tool offers a distinct approach to AI-assisted coding, from natural language understanding to code snippet generation and iterative refinement.

📋 What I Received — A Summary

1. Perplexity.ai
   This tool provided a clear and well-structured Page Object Model with classes that neatly encapsulate element locators and interaction methods. However, it stopped short of generating complete test scripts or setup/configuration files. So while the architectural guidance was solid, manual work remained to build out the full framework.

2. Warp.dev
   Warp impressed me with its speed and interactivity. It generated runnable test snippets that you can quickly validate inside a developer terminal, making it great for experimentation and quick checks. The downside is that it doesn’t produce a modular or maintainable project structure by itself — it’s more snippet-focused than framework-focused.

3. V0.app
   V0.app struck a balance by creating clean, modular Page Object files that follow good design principles. Yet, it didn’t include the Playwright configuration or test runner setup, which are essential for a working framework. It’s a solid starting point but requires additional setup.

4. Manus.im
   Manus impressed me with its conversational and adaptive approach. You can refine and adjust the generated code interactively, and it responds well to follow-up prompts, iterating towards a better structure. The challenge here is that you need to actively guide it to fill in missing parts like tests and configs.

5. Qwen.ai
   Qwen.ai provided the most comprehensive and near-production-ready code. It generated both Page Objects and test files integrated with Playwright’s official test runner, following best practices for folder structure and test syntax. This output required the least manual intervention to become runnable.

🔍 Key Observations

• No AI tool produced a 100% complete framework out of the box. Each had gaps requiring human input, such as configuration files, test runner setup, or utilities.

• Each tool has unique strengths:

  • Perplexity.ai excels in architecture clarity.

  • Warp.dev shines for quick snippet validation.

  • V0.app delivers modular code files.

  • Manus.im adapts well to iterative feedback.

  • Qwen.ai comes closest to a finished product.

• The best approach is a hybrid one: Combine the outputs, apply your expertise, and fill in the gaps for a robust, maintainable framework.

💡 Why This Matters to Test Automation Engineers

Back in the day, building a framework like this could easily take a full week or more of focused effort—writing Page Objects, setting up configuration files, writing tests, and ensuring everything works smoothly together.

With AI tools, the time to get a solid starting point has shrunk dramatically — to seconds or minutes. This frees up time to focus on more important tasks like designing meaningful test cases, integrating with CI/CD pipelines, and improving test reliability.

⚙️ Adapting CI/CD to AI-Generated Frameworks

• Perplexity.ai, V0.app, and Manus.im: Since these tools primarily generate Page Objects or partial framework components, you’ll need to manually add essential configuration files like package.json and create CI/CD workflow definitions (for example, GitHub Actions, Jenkins pipelines, or GitLab CI files) to integrate the tests into your automation pipeline.

• Warp.dev: As Warp.dev mainly produces executable snippets rather than full projects, you should incorporate these snippets into your existing framework and ensure your CI/CD process runs the full test suite including these new tests.

• Qwen.ai: Because Qwen.ai generates near-production-ready frameworks with configuration and test runner setup, you can directly hook these outputs into your CI/CD pipelines with minimal adjustments, enabling automated test runs on commits, pull requests, or schedule triggers.

📚 What I Learned

• No single AI tool created a 100% production-ready framework.

• Each tool offers unique advantages: some excel at architecture, others at code speed or adaptability.

• The fastest, most reliable approach is to combine AI outputs and refine manually.

• AI drastically speeds up framework creation — a task that once took me a full week can now be jumpstarted in seconds.

💭 Final Thoughts 

AI isn’t here to replace automation engineers; it’s here to supercharge them. Leveraging multiple AI tools lets us focus on high-value work — designing robust tests and maintaining quality — rather than reinventing the wheel.

💻 Happy automation testing guys! 🚀