README.md

metadata

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 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 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.