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Random JSON Null and Missing Value Fixture Generator

Construct absent, null, empty-string and supplied-value fields with caller-selected interpretation labels and explicit own-property presence.

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Description

Construct absent, null, empty-string and supplied-value fields with caller-selected interpretation labels and explicit own-property presence.

A parser corpus pairs literal input spellings or structured documents with observable parser results or explicitly constructed expectations. Successful parsing, semantic validity, and exact round-trip preservation are separate properties, so test records retain the relevant evidence.

Construct absent, null, empty-string and supplied-value fields with caller-selected interpretation labels and explicit own-property presence.

When to use this tool

Use this generator to create reproducible json null missing value fixture cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

  1. Supply fields, modes, count in the field shapes below. Set seed when you need an explicit replay.
  2. Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
  3. Read documents. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

For each supplied field, draw absent, null, empty, or value mode. Omit absent properties and retain the other scalar states, returning explicit presence and caller-defined interpretation labels beside each document.1

Input fieldMeaning and unitsAccepted input
fieldsFieldsRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
fields[].keyKeyRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
fields[].valueValueRequired. Text; maximum UTF-16 length 65536. or Finite number. or Boolean. or Null.
fields[].labelsLabelsRequired. Object; no unknown keys.
fields[].labels.absentAbsentRequired. Text; maximum UTF-16 length 65536.
fields[].labels.nullNullRequired. Text; maximum UTF-16 length 65536.
fields[].labels.emptyEmptyRequired. Text; maximum UTF-16 length 65536.
fields[].labels.valueValueRequired. Text; maximum UTF-16 length 65536.
modesModesRequired. Array of Text; one of "absent", "null", "empty", "value".; minimum items 1; maximum items 4.
countRequested count; the mechanics explain whether one draw expands into several returned records.Required. Safe integer; minimum 1; maximum 32.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
documentsDocumentsRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 32.
documents[].documentDocumentRequired. Object.
documents[].prettyPrettyRequired. Text; maximum UTF-16 length 65536.
documents[].fieldsFieldsRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 16.
documents[].fields[].keyKeyRequired. Text; maximum UTF-16 length 65536.
documents[].fields[].modeModeRequired. Text; maximum UTF-16 length 65536.
documents[].fields[].presentPresentRequired. Boolean.
documents[].fields[].interpretationInterpretationRequired. Text; maximum UTF-16 length 65536.
randomnessReplay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.Required. Object; no unknown keys.
randomness.seedOptional numeric replay seed; normalization and precedence are explained below.Required. Safe integer. or Null.
randomness.sourceSourceRequired. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json
{
  "input": {
    "fields": [
      {
        "key": "note",
        "value": "draft",
        "labels": {
          "absent": "inherit",
          "null": "clear",
          "empty": "blank",
          "value": "replace"
        }
      }
    ],
    "modes": [
      "absent",
      "null"
    ],
    "count": 1,
    "seed": 42
  },
  "output": {
    "documents": [
      {
        "document": {
          "note": null
        },
        "pretty": "{\n  \"note\": null\n}\n",
        "fields": [
          {
            "key": "note",
            "mode": "null",
            "present": true,
            "interpretation": "clear"
          }
        ]
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • Absent, JSON null, empty string, zero, and false are distinct states. Interpretation labels are supplied application semantics, not inferred schema defaults or universal missing-value meanings.
  • A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
  • The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

  1. Tool behavior and example: Random JSON Null and Missing Value Fixture Generator — tool.io

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