AI-assisted test infrastructure
S.M.A.R.T.
A team project with CHECK24 combining natural-language Playwright generation, deterministic mock data and execution feedback.
Not just prompt in, test out
The interesting part is the feedback loop: a user describes test intent, generated Playwright code is validated against deterministic mock data, execution returns feedback, and the flow can be refined.
Interface, validation and data handling
My work crossed UI flows, prompt validation, storage-connected paths and documentation. That makes the project useful in the portfolio as AI-assisted developer tooling, not as another generic chatbot example.
Execution feedback loop
From test intent to reproducible run
S.M.A.R.T. is best explained as a loop, because the value is in connecting generation, mock data and execution feedback.
Roadmap
Project lens
Intent
User-facing flow for describing test goals and validating generated output.
Data
Mock-data paths and storage-connected handling for reproducible test contexts.
Execution
Playwright-centered feedback loop around generated tests.
Showcase
Demonstrates AI-assisted developer tooling with real workflow constraints.
Portfolio angle
What to show next
- A short demo should show prompt, generated Playwright test and execution result in one loop.
- The case page should stay focused on workflow, not on listing every backend dependency.
- Screenshots from the UI will matter more than extra architecture text.