Creating Test Fixtures for JSON Formatter Validation

When building or using a JSON formatter or validator, ensuring its accuracy is paramount. A poorly implemented tool can silently corrupt data or incorrectly reject valid JSON. The most effective way to guarantee correctness is through rigorous testing, and the cornerstone of such testing is a well-structured set of test fixtures.

This article explores what test fixtures are in this context, why they are crucial, and how to create a comprehensive suite covering various scenarios for validating your JSON processing logic.

What are Test Fixtures?

In software testing, a fixture is a fixed, known state or set of data used as a baseline for running tests. For JSON formatter or validator testing, test fixtures are specific JSON strings or files, paired with their expected outcomes.

These outcomes typically include:

• Whether the JSON is valid or invalid.
• If valid, the expected formatted output (e.g., with consistent indentation, spacing).
• If invalid, the expected error type and potentially the location (line/column number) of the error.

Why Are Comprehensive Fixtures Important?

• Ensure Correctness: Verify that the formatter correctly handles all valid JSON constructs according to the JSON specification (RFC 8259).
• Catch Errors Accurately: Confirm that the validator correctly identifies and reports invalid JSON, including subtle syntax or structural errors.
• Identify Edge Cases: Test how the tool behaves with complex nesting, empty structures, special characters, large inputs, and boundary conditions.
• Regression Prevention: Provide a safety net when refactoring or adding new features, ensuring that existing functionality remains unbroken.
• Specification Compliance: Help verify that the implementation strictly adheres to the official JSON standard.

Types of JSON Fixtures to Create

To achieve good test coverage, your fixtures should span both valid and invalid JSON examples.

Valid JSON Fixtures

These fixtures should cover all fundamental JSON data types and structures, both simple and complex.

{ "name": "Alice", "age": 30 }

[ 1, 2, 3, false, null, "hello" ]

{
  "person": {
    "name": "Bob",
    "address": {
      "street": "123 Main St",
      "city": "Anytown"
    }
  },
  "items": [
    { "id": 1, "quantity": 10 },
    { "id": 2, "quantity": 5 }
  ]
}

{ "emptyObject": {}, "emptyArray": [] }

{
  "string": "a string with \"quotes\" and newlines\n",
  "numberInt": 123,
  "numberFloat": -45.67e+8,
  "booleanTrue": true,
  "booleanFalse": false,
  "nullValue": null
}

{
  "key-with-hyphen": "value/with/slashes",
  "key with spaces": "\u0041 \u0042",
  "Русский ключ": "简体中文的值"
}

[ [1, 2], [3, 4], [] ]

{ "list1": [1, 2], "list2": ["a", "b"] }

{
  "level1": {
    "level2": {
      "level3": {
        "level4": "deep value"
      }
    }
  }
}

"just a string"
12345
true
null

Invalid JSON Fixtures

These fixtures are critical for testing the validator's ability to detect errors and the formatter's behavior when given bad input (it should typically throw an error). Cover common syntax errors and structural issues.

{ "a": 1, "b": 2, }

[ 1, 2, 3, ]

{ "a" 1 }

{ "a": 1 "b": 2 }

{ "a": 1

[ 1, 2

{ key: 1 }

{ 'a': 1 }

{ "a": "hello\x" }

[, 1, 2]

{ , "a": 1 }

{
  "a": 1
}

{ "a": 1.2.3 }
{ "b": 1e+ }

{ "a": "hello\" }

{ "a": "line1\nline2	" }
{ "a": "line1
line2" }

{} []

{ "a": 1 } extra_text

{ "value": NaN }
{ "value": Infinity }

{ "value": undefined }

Organizing Your Fixtures

How you organize your fixtures depends on the scale and complexity of your tests.

• In-code Objects: For smaller sets of fixtures, you can define them directly within your test files using JavaScript/TypeScript objects. Each object could contain the raw JSON string, a boolean indicating validity, and perhaps the expected formatted output or error details.

  In-code Fixture Example:

  interface JsonFixture {
    name: string;
    input: string;
    isValid: boolean;
    expectedOutput?: string; // For valid cases
    expectedError?: { type: string; line?: number; col?: number }; // For invalid cases
  }
  
  const jsonFixtures: JsonFixture[] = [
    {
      name: "valid_simple_object",
      input: `{"a":1, "b": "hello"}`,
      isValid: true,
      expectedOutput: `{\n  "a": 1,\n  "b": "hello"\n}` // Assuming 2-space indent
    },
    {
      name: "invalid_trailing_comma",
      input: `[1, 2,]`,
      isValid: false,
      expectedError: { type: "SyntaxError", line: 1, col: 6 } // Example error detail
    },
    // ... more fixtures
  ];

• Separate Files: For larger suites, keeping each fixture (or groups of related fixtures) in separate .json, .jsonc (JSON with comments, if your tools support reading it this way), or plain text files is more manageable. You would then have a corresponding file (or metadata) describing the expected outcome for each input file.

  File-based Fixture Example:

  Directory structure:
  /test/fixtures/
├─ /valid/
│ ├─ simple_object.json
│ ├─ nested.json
│ └─ ...
├─ /invalid/
│ ├─ trailing_comma.json
│ ├─ unquoted_key.json
│ └─ ...
/test/fixtures/expected/
├─ /formatted/
│ ├─ simple_object.formatted.json
│ └─ nested.formatted.json
├─ /errors/
│ └─ errors.json (maps invalid file names to expected error details)

Using Fixtures in Your Tests

With your fixtures defined, you can write automated tests using a testing framework (like Jest, Mocha, etc.):

1. Iterate through your collection of fixtures.
2. For each fixture:
   • If isValid is true:
     • Pass the input string to your formatter/validator.
     • Assert that no error is thrown.
     • Assert that the output matches the expectedOutput (e.g., compare strings or parsed objects).
   • If isValid is false:
     • Pass the input string to your formatter/validator.
     • Assert that an error *is* thrown.
     • Optionally, assert that the error type, message, line number, or column number matches the expectedError details.

Sources for Fixtures & Tips

• JSON Specification (RFC 8259): Read the specification carefully to understand all valid and invalid cases.
• Existing Test Suites: Look for test suites developed for other JSON parsers in different languages. Projects often open-source their test data.
• JSON Linter/Validator Tools: Use online or command-line tools to validate various JSON snippets and observe how they behave and report errors.
• Generate Edge Cases: Manually construct JSON that tests boundaries (maximum nesting depth your tool supports, very long strings/numbers, objects/arrays with thousands of items).
• Incremental Creation: Add new fixtures whenever you encounter a bug or an edge case that wasn't previously covered.

Conclusion

Creating a robust set of test fixtures is an indispensable step in building reliable JSON formatting and validation tools. By systematically covering a wide range of valid and invalid JSON structures, types, and edge cases, you can build confidence in your tool's correctness and maintain its quality over time. This investment in comprehensive fixtures pays off by catching bugs early and ensuring compliance with the JSON standard.