API Response: Users Page 1 (JSON)
Page 1 of a paginated JSON API response (users), with page metadata and a `hasMore` flag. Paired with page 2 for testing pagination and infinite-scroll logic.
{
"page": 1,
"pageSize": 3,
"total": 5,
"hasMore": true,
"items": [
{
"id": 1,
"name": "Ada Lovelace",
"email": "ada.lovelace@example.com",
"role": "admin"
},
{
"id": 2,
"name": "Alan Turing",
"email": "alan.turing@example.com",
"role": "member"
},
{
"id": 3,
"name": "Grace Hopper",
"email": "grace.hopper@example.com",
"role": "guest"
}
]
}
Specifications
- Format
- JSON
- Kind
- paginated response
- Page
- 1
- Items
- 3
- Total
- 5
Testing contract
Expected to pass- Scenario
- Exercise API Response: Users Page 1 (JSON) in its api workflow. Page 1 of a paginated JSON API response (users), with page metadata and a `hasMore` flag.
- Expected result
- top-level keys are page, pageSize, total, hasMore, items; array lengths: items=3; selected values: {"page": 1, "pageSize": 3, "total": 5, "hasMore": true}. Declared feature checks: kind=paginated response; page=1; items=3; total=5.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“API Response: Users Page 1 (JSON)” is a deterministic Testaroo fixture for API testing, Conversion testing. OpenAPI/Swagger specs, GraphQL SDL, JSON Schema, paginated and problem+json error payloads, and webhook samples, for testing API clients, mock servers, contract tests, and schema validators.
Documented properties for this file: JSON. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("users-page-1.json") as f:
data = json.load(f)
print(type(data), len(data))Generated by generation/data_dev_fixtures.py. Free for any use, no attribution required, license.
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