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Intentionally Invalid JSON

An intentionally invalid JSON file with a trailing comma and a missing closing brace, for testing parser error handling and messages. Not valid JSON by design.

JSON parsingError handling
Preview, first 8 linesjson
{
  "id": 1,
  "name": "Intentionally Invalid",
  "items": [1, 2, 3,],
  "note": "trailing comma above, missing closing brace below",
  "nested": { "a": 1, "b": 2
}

Specifications

Valid
false
Errors
trailing comma, missing closing brace
Intentionally Corrupt
true

Testing contract

Expected to fail
Scenario
Exercise Intentionally Invalid JSON in its json workflow. An intentionally invalid JSON file with a trailing comma and a missing closing brace, for testing parser error handling and messages.
Expected result
Report the intended validation failure (trailing comma, missing closing brace); never silently treat it as a conforming input. the structural probe reports JSONDecodeError; evaluate the documented feature or defect with its format-specific reader. Declared feature checks: valid=False; errors=trailing comma, missing closing brace; intentionallyCorrupt=True.

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

“Intentionally Invalid JSON” is a deterministic Testaroo fixture for JSON parsing, Error handling. Flat, deeply nested, JSON Lines, and intentionally invalid JSON for testing parsers and error handling.

Documented properties for this file: intentionally corrupt · intentionally invalid. 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.

Feed the file to your parser and assert it handles the documented quirks, quoted delimiters, embedded newlines, ragged rows, or invalid syntax; the valid↔invalid distinction is labelled in the title.

Code examples

import json

with open("invalid.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/data_structured.py. Free for any use, no attribution required, license.

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