Philip Wong | 🚧 In progress
This repo tracks my hands-on work testing BuggyBoard, a bug-tracker web app, using Playwright and AI-assisted test automation techniques (AI-powered test generation, self-healing scripts, predictive test selection, CI/CD integration). My active work is on the coursework branch.
It started as a fork of the LinkedIn Learning course Playwright with AI: Agents, Context, and Test Automation Patterns by Andrew Knight — credit to him and LinkedIn Learning for the original project scaffold and curriculum. I'm still working through it, so expect this README and the codebase to keep evolving as I go.
- Building reliable end-to-end Playwright tests across browsers and environments.
- Writing AI-enhanced, self-healing test scripts that adapt to UI changes.
- Generating and refining test cases from natural-language prompts.
- Integrating Playwright into CI/CD pipelines for continuous testing.
BuggyBoard is the web app under test. This repository contains its source code as well as the Playwright test code I've written and adapted while working through the course material.
BuggyBoard is a small web app for tracking bug reports. It is:
- full-stack Node.js
- written in TypeScript
- with a React frontend
- and an Express backend
- and a SQLite database
backend/ Express + TypeScript API, SQLite data
frontend/ React + TypeScript UI (Vite, Tailwind)
tests/ Playwright test specs (AI generated and human-reviewed)
tests-vibed/ Playwright test specs (AI generated)
specs/ Specification docs (product, design, engineering, features)
playwright.config.ts Playwright test runner config
SETUP.md Local setup instructions
This repo also retains the original course's example-code branches (start, main, CHAPTER#_MOVIE#) for reference. See SETUP.md for details on how those work and how to resolve branch-switching git errors.
Original course by Andrew Knight (The Automation Panda), available on LinkedIn Learning.