Random Landmark Region Layout Fixture Generator

Description

Assemble caller landmark templates in sampled order with optional repeated-role label controls, retaining lexical attributes and exact same-tag label collisions without a document accessibility verdict.

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.

When to use this tool

Use this generator to create reproducible landmark region layout 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 regions, duplicateLabel 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 regions, duplicateLabelIds, html. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Shuffle supplied landmark regions and optionally append a copy with a nonempty label. Serialize escaped labels, parse opening tags, and identify every region sharing the same tag and nonempty label.^[1]

Input field	Meaning and units	Accepted input
regions	Regions	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
regions[].tag	Tag	Required. Text; one of "nav", "main", "aside", "section".
regions[].label	Caller-facing text label; not a globally unique identifier.	Required. Text; maximum UTF-16 length 256.
duplicateLabel	Duplicate Label	Required. Boolean.
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
regions	Regions	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 17.
regions[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 32.
regions[].tag	Tag	Required. Text; one of "nav", "main", "aside", "section".
regions[].label	Caller-facing text label; not a globally unique identifier.	Required. Text; maximum UTF-16 length 256.
regions[].parsed	Parsed	Required. Object; no unknown keys.
regions[].parsed.name	Name	Required. Text; maximum UTF-16 length 256.
regions[].parsed.attributes	Attributes	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 16.
regions[].parsed.attributes[].name	Name	Required. Text; maximum UTF-16 length 256.
regions[].parsed.attributes[].value	Value	Required. Text; maximum UTF-16 length 2048.
duplicateLabelIds	Duplicate Label Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 32.; minimum items 0; maximum items 17.
html	Html	Required. Text; maximum UTF-16 length 131072.
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.

json

{
  "input": {
    "regions": [
      {
        "tag": "nav",
        "label": "Primary"
      },
      {
        "tag": "main",
        "label": "Content"
      }
    ],
    "duplicateLabel": true,
    "seed": 42
  },
  "output": {
    "regions": [
      {
        "id": "region-0",
        "tag": "main",
        "label": "Content",
        "parsed": {
          "name": "main",
          "attributes": [
            {
              "name": "aria-label",
              "value": "Content"
            }
          ]
        }
      },
      {
        "id": "region-1",
        "tag": "nav",
        "label": "Primary",
        "parsed": {
          "name": "nav",
          "attributes": [
            {
              "name": "aria-label",
              "value": "Primary"
            }
          ]
        }
      },
      {
        "id": "region-2",
        "tag": "nav",
        "label": "Primary",
        "parsed": {
          "name": "nav",
          "attributes": [
            {
              "name": "aria-label",
              "value": "Primary"
            }
          ]
        }
      }
    ],
    "duplicateLabelIds": [
      "region-1",
      "region-2"
    ],
    "html": "<main aria-label=\"Content\"></main><nav aria-label=\"Primary\"></nav><nav aria-label=\"Primary\"></nav>",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Duplicate detection uses both tag and label. Repeated labels across different tags are not flagged by this model. This is markup evidence rather than a complete landmark accessibility audit.
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

Tool behavior and example: Random Landmark Region Layout Fixture Generator — tool.io

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