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Random Details Disclosure State Fixture Generator

Construct nested supplied disclosure content with open-presence keys and visibility derived from ancestor state; browser interaction and rendering remain app-tested.

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Description

Construct nested supplied disclosure content with open-presence keys and visibility derived from ancestor state; browser interaction and rendering remain app-tested.

An interface fixture makes content and state explicit so a consumer can test a rendering or interaction. Literal labels, modeled states, and calculated geometry remain separate from what a browser or assistive technology actually displays or announces.

Construct nested supplied disclosure content with open-presence keys and visibility derived from ancestor state; browser interaction and rendering remain app-tested.

When to use this tool

Use this generator to create reproducible details disclosure state 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 sections, nodeCount 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 nodes, html. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Construct a bounded-depth tree of sampled disclosure sections, draw each open state, and mark a node visible only when all ancestor nodes are visible and open. Return parent references, parsed start tags, and escaped nested details markup.1

Input fieldMeaning and unitsAccepted input
sectionsSectionsRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
sections[].summarySummaryRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
sections[].bodyBodyRequired. Text; maximum UTF-16 length 256.
nodeCountNode CountRequired. Safe integer; minimum 1; maximum 16.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
nodesNodesRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
nodes[].idLiteral identifier used independently of display labels.Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 32.
nodes[].parentIdParent IdRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 32. or Null.
nodes[].summarySummaryRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
nodes[].bodyBodyRequired. Text; maximum UTF-16 length 256.
nodes[].openOpenRequired. Boolean.
nodes[].visibleVisibleRequired. Boolean.
nodes[].parsedParsedRequired. Object; no unknown keys.
nodes[].parsed.nameNameRequired. Text; maximum UTF-16 length 256.
nodes[].parsed.attributesAttributesRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 16.
nodes[].parsed.attributes[].nameNameRequired. Text; maximum UTF-16 length 256.
nodes[].parsed.attributes[].valueValueRequired. Text; maximum UTF-16 length 2048.
htmlHtmlRequired. Text; maximum UTF-16 length 131072.
randomnessReplay source and normalized 32-bit seed, or null seed for an advancing context source.Required. Object; no unknown keys.
randomness.seedOptional safe-integer 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": {
    "sections": [
      {
        "summary": "More",
        "body": "Details"
      }
    ],
    "nodeCount": 2,
    "seed": 42
  },
  "output": {
    "nodes": [
      {
        "id": "disclosure-0",
        "parentId": null,
        "summary": "More",
        "body": "Details",
        "open": true,
        "visible": true,
        "parsed": {
          "name": "details",
          "attributes": [
            {
              "name": "id",
              "value": "disclosure-0"
            },
            {
              "name": "open",
              "value": ""
            }
          ]
        }
      },
      {
        "id": "disclosure-1",
        "parentId": "disclosure-0",
        "summary": "More",
        "body": "Details",
        "open": true,
        "visible": true,
        "parsed": {
          "name": "details",
          "attributes": [
            {
              "name": "id",
              "value": "disclosure-1"
            },
            {
              "name": "open",
              "value": ""
            }
          ]
        }
      }
    ],
    "html": "<details id=\"disclosure-0\" open><summary>More</summary><p>Details</p><details id=\"disclosure-1\" open><summary>More</summary><p>Details</p></details></details>",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • A closed details element itself can be visible while its descendants are hidden. This is a simplified ancestry model, not CSS visibility, keyboard behavior, or complete browser disclosure semantics.
  • A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
  • 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 Details Disclosure State Fixture Generator — tool.io

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