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API Error: 422 Validation (JSON)

A 422 validation-error response with a machine-readable list of field errors, for testing form-validation surfacing and error mapping.

API testingConversion testing
Preview, first 17 linesjson
{
  "type": "https://example.com/errors/validation",
  "title": "Unprocessable Entity",
  "status": 422,
  "detail": "The request body failed validation.",
  "errors": [
    {
      "field": "email",
      "message": "must be a valid email address"
    },
    {
      "field": "name",
      "message": "must not be empty"
    }
  ]
}

Specifications

Format
application/problem+json
Status
422
Field Errors
2

Testing contract

Expected to pass
Scenario
Exercise API Error: 422 Validation (JSON) in its api workflow. A 422 validation-error response with a machine-readable list of field errors, for testing form-validation surfacing and error mapping.
Expected result
top-level keys are type, title, status, detail, errors; array lengths: errors=2; selected values: {"type": "https://example.com/errors/validation", "status": 422}. Declared feature checks: status=422; fieldErrors=2.

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 Error: 422 Validation (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: application/problem+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("error-422.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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