Random Product Variant Comparison Fixture Generator

Description

Construct every bounded supplied attribute combination with synthetic ordinal SKUs and an exact randomly chosen unavailable subset.

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 every bounded supplied attribute combination with synthetic ordinal SKUs and an exact randomly chosen unavailable subset.

When to use this tool

Use this generator to create reproducible product variant comparison 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 axes, skuPrefix, unavailableCount 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 variants, totalCount, availableCount, unavailableCount. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Deduplicate each axis's options, build their complete Cartesian product, and assign sequential prefixed SKUs. Sample exactly unavailableCount distinct combinations and mark them unavailable; return all variants and availability totals.^[1]

Delegated operations used for this result: cartesian product; unique. Their calculation or parsing conventions apply to the corresponding result fields.^{[2, 3]}

Input field	Meaning and units	Accepted input
axes	Axes	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 6.
axes[].name	Name	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
axes[].options	Options	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 128.
skuPrefix	Sku Prefix	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 96.
unavailableCount	Unavailable Count	Required. Safe integer; minimum 0; maximum 128.
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
variants	Variants	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
variants[].sku	Sku	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
variants[].attributes	Attributes	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 6.
variants[].attributes[].name	Name	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
variants[].attributes[].value	Value	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
variants[].available	Available	Required. Boolean.
totalCount	Total Count	Required. Safe integer.
availableCount	Available Count	Required. Safe integer.
unavailableCount	Unavailable Count	Required. Safe integer.
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": {
    "axes": [
      {
        "name": "size",
        "options": [
          "S",
          "M"
        ]
      },
      {
        "name": "color",
        "options": [
          "red",
          "blue"
        ]
      }
    ],
    "skuPrefix": "DEMO",
    "unavailableCount": 2,
    "seed": 42
  },
  "output": {
    "variants": [
      {
        "sku": "DEMO-1",
        "attributes": [
          {
            "name": "size",
            "value": "S"
          },
          {
            "name": "color",
            "value": "red"
          }
        ],
        "available": true
      },
      {
        "sku": "DEMO-2",
        "attributes": [
          {
            "name": "size",
            "value": "S"
          },
          {
            "name": "color",
            "value": "blue"
          }
        ],
        "available": false
      },
      {
        "sku": "DEMO-3",
        "attributes": [
          {
            "name": "size",
            "value": "M"
          },
          {
            "name": "color",
            "value": "red"
          }
        ],
        "available": false
      },
      {
        "sku": "DEMO-4",
        "attributes": [
          {
            "name": "size",
            "value": "M"
          },
          {
            "name": "color",
            "value": "blue"
          }
        ],
        "available": true
      }
    ],
    "totalCount": 4,
    "availableCount": 2,
    "unavailableCount": 2,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

The product is limited to 128 combinations. SKU strings and availability are fixture data, not inventory allocation, global uniqueness, or a live product catalog.
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 Product Variant Comparison Fixture Generator — tool.io
Calculation or validation: list/cartesian-product — tool.io
Calculation or validation: list/unique — tool.io

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