Description
Generate before/after nested JSON documents from explicit bounded field templates, delegate add/remove/replace diff and application, and verify reconstructed structure; inherited-property paths and sparse dot-path arrays are rejected and patches are not claimed globally minimal.
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.
Generate before/after nested JSON documents from explicit bounded field templates, delegate add/remove/replace diff and application, and verify reconstructed structure; inherited-property paths and sparse dot-path arrays are rejected and patches are not claimed globally minimal.
When to use this tool
Use this generator to create reproducible json patch test case 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 caseCount, beforeFields, afterFields 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 cases. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Generate before and after documents from supplied field models, delegate JSON diff and patch application, and independently verify the rebuilt document and applied operation count against the target.1
Delegated operations used for this result: diff; apply patch. Their calculation or parsing conventions apply to the corresponding result fields.2, 3
| Input field | Meaning and units | Accepted input |
|---|---|---|
| caseCount | Case Count | Required. Safe integer; minimum 1; maximum 64. |
| beforeFields | Before Fields | Required. Array of Object; no unknown keys. or Object; no unknown keys. or Object; no unknown keys. or Object; no unknown keys.; minimum items 1; maximum items 32. |
| afterFields | After Fields | Required. Array of Object; no unknown keys. or Object; no unknown keys. or Object; no unknown keys. or Object; no unknown keys.; minimum items 1; maximum items 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 |
|---|---|---|
| cases | Cases | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 64. |
| cases[].before | Before | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 1000000. |
| cases[].after | After | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 1000000. |
| cases[].patch | Patch | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 1000000. |
| cases[].applied | Applied | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 1000000. |
| cases[].opCount | Op Count | Required. Safe integer; minimum 0; maximum 4096. |
| cases[].verified | Verified | Required. Boolean; exactly true. |
| 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": {
"caseCount": 1,
"beforeFields": [
{
"key": "profile.name",
"kind": "text",
"choices": [
"Ada",
"Grace"
]
},
{
"key": "scores.0",
"kind": "integer",
"minimum": 1,
"maximum": 5
},
{
"key": "scores.1",
"kind": "integer",
"minimum": 1,
"maximum": 5
}
],
"afterFields": [
{
"key": "profile.name",
"kind": "text",
"choices": [
"Lin",
"Hedy"
]
},
{
"key": "scores.0",
"kind": "integer",
"minimum": 5,
"maximum": 9
},
{
"key": "status.enabled",
"kind": "boolean",
"trueProbability": 0.7
}
],
"seed": 42
},
"output": {
"cases": [
{
"before": "{\"profile\":{\"name\":\"Grace\"},\"scores\":[3,5]}",
"after": "{\"profile\":{\"name\":\"Hedy\"},\"scores\":[5],\"status\":{\"enabled\":true}}",
"patch": "[{\"op\":\"replace\",\"path\":\"/profile/name\",\"value\":\"Hedy\"},{\"op\":\"remove\",\"path\":\"/scores/1\"},{\"op\":\"replace\",\"path\":\"/scores/0\",\"value\":5},{\"op\":\"add\",\"path\":\"/status\",\"value\":{\"enabled\":true}}]",
"applied": "{\"profile\":{\"name\":\"Hedy\"},\"scores\":[5],\"status\":{\"enabled\":true}}",
"opCount": 4,
"verified": true
}
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- before, after, patch, and applied are serialized JSON text under the output contract. Round-trip verification covers the generated supported subset, not every patch operation, application conflict, or arbitrary JSON document.
- 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.