Description
Sample supplied optional-field presence independently while retaining required fields and exact present/absent coverage counts.
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.
Sample supplied optional-field presence independently while retaining required fields and exact present/absent coverage counts.
When to use this tool
Use this generator to create reproducible json optional field coverage 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
- Supply fields, probability, count in the field shapes below. Set seed when you need an explicit replay.
- 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.
- Read documents, coverage. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Keep every required field and independently include optional fields according to probability, retaining supplied scalar values. Return present keys and per-field present/absent totals across all generated documents.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| fields | Fields | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 16. |
| fields[].key | Key | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128. |
| fields[].value | Value | Required. Text; maximum UTF-16 length 65536. or Finite number. or Boolean. or Null. |
| fields[].required | Required | Required. Boolean. |
| probability | Probability | Required. Finite number; minimum 0; maximum 1. |
| count | Requested count; the mechanics explain whether one draw expands into several returned records. | Required. Safe integer; minimum 1; maximum 32. |
| seed | Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed. | Optional. Safe integer. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| documents | Documents | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 32. |
| documents[].document | Document | Required. Object. |
| documents[].pretty | Pretty | Required. Text; maximum UTF-16 length 65536. |
| documents[].presentKeys | Present Keys | Required. Array of Text; maximum UTF-16 length 65536.; minimum items 0; maximum items 16. |
| coverage | Coverage | Required. Array of Object; no unknown keys.; minimum items 0; maximum items 16. |
| coverage[].key | Key | Required. Text; maximum UTF-16 length 65536. |
| coverage[].present | Present | Required. Safe integer; minimum 0; maximum 32. |
| coverage[].absent | Absent | Required. Safe integer; minimum 0; maximum 32. |
| randomness | Replay 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.seed | Optional numeric replay seed; normalization and precedence are explained below. | Required. Safe integer. or Null. |
| randomness.source | Source | Required. 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.
{
"input": {
"fields": [
{
"key": "id",
"value": 7,
"required": true
},
{
"key": "note",
"value": "draft",
"required": false
}
],
"probability": 0.5,
"count": 1,
"seed": 42
},
"output": {
"documents": [
{
"document": {
"id": 7,
"note": "draft"
},
"pretty": "{\n \"id\": 7,\n \"note\": \"draft\"\n}\n",
"presentKeys": [
"id",
"note"
]
}
],
"coverage": [
{
"key": "id",
"present": 1,
"absent": 0
},
{
"key": "note",
"present": 1,
"absent": 0
}
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- Required fields always appear even though a random draw is consumed for each. Finite samples need not exhibit every optional-field combination; this is coverage reporting rather than exhaustive schema testing.
- 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
Similar or alternative tools
- Random JSON Duplicate ID Dataset Generator
Construct linked records with an exact duplicate insertion count and retain missing, unique and ambiguous reference candidate indices.
- Random JSON Flattening Round-Trip Corpus Generator
Reuse the random nested-document generator for its safe dot-path/contiguous-array subset, then actually flatten and rebuild every generated document with both existing cores; retain intermediate leaves and original/rebuilt forms, excluding sparse/mixed containers, reserved segments, literal-dot keys and empty-container loss cases rather than claiming arbitrary JSON round trips.