---
license: agpl-3.0
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - training_data_playwright_clean.jsonl
    - training_data_cypress_clean.jsonl
language:
- en
task_categories:
- text-generation
pretty_name: NL-to-Test Training Dataset
size_categories:
- n<1K
tags:
- test-generation
- code-generation
- test-automation
- web-automation
- synthetic
- qa
---

# NL-to-Test Training Dataset

Training data for fine-tuning a code model that generates **Cypress** and **Playwright**
end-to-end tests from natural-language requirements.

Each example is a chat pair: a user message containing a plain-English test requirement
and target URL, and an assistant message containing a complete, runnable test file that
follows the conventions of the [AI Natural Language Tests](https://github.com/aiqualitylab/ai-natural-language-tests)
platform. Playwright examples embed a top-level `testData` object with a `resolveLocator`
helper and iterate over `test_cases`; Cypress examples follow the platform's
fixture-driven pattern (`cy.fixture('url_test_data')`).

## Dataset Structure

- **Format:** JSONL, one example per line, chat format (`messages: [user, assistant]`)
  compatible with `trl`'s `SFTTrainer`
- **Fields:** `requirement`, `url`, `messages`
- **Split:** `train` (202 examples: 95 Playwright/TypeScript + 107 Cypress/JavaScript)

| Source site | Playwright | Cypress |
|---|---|---|
| the-internet.herokuapp.com | 41 | 49 |
| demoqa.com | 44 | 48 |
| saucedemo.com | 10 | 10 |

Coverage spans login/auth, forms, text inputs, dropdowns, checkboxes/radios, buttons/links,
alerts/modals, hover/drag-and-drop, tables, tabs/accordions, dynamic content and waits,
iframes/windows, widgets (sliders, date pickers), and multi-step e-commerce flows.

## Dataset Creation

Examples were generated by running a categorized requirement bank through the
[ai-natural-language-tests](https://github.com/aiqualitylab/ai-natural-language-tests)
generation pipeline (gpt-4o-mini backend) against three public demo websites, then
filtered with mechanical quality gates (no invalid selectors for the target framework,
no hard-coded sleeps, no fake file paths, no markdown fences, valid JSON).
Tests were **not** execution-verified in this version; a future version will add an
execution-pass gate.

## Limitations

- Generated tests follow one platform's conventions and may not reflect general
  test-writing styles.
- Expected values (messages, texts) were produced by an LLM without running the tests,
  so some assertions may not match the live sites.
- All data comes from public demo websites; no personal, sensitive, or user-provided
  data is included. User inputs to the platform are never collected for training.

## License

AGPL-3.0, matching the source project.