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Random Semantic Version Ordering Corpus Generator

Generate synthetic safe-component semantic versions with prereleases, build precedence ties, exact duplicates, and a stable sorted answer key.

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Description

Generate synthetic safe-component semantic versions with prereleases, build precedence ties, exact duplicates, and a stable sorted answer key.

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 synthetic safe-component semantic versions with prereleases, build precedence ties, exact duplicates, and a stable sorted answer key.

When to use this tool

Use this generator to create reproducible semantic version ordering corpus 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 count, minimumComponent, maximumComponent, prereleaseLabels, buildLabels 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 versions, sortedIndices, precedenceGroups. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

For each draw, create a prerelease, the corresponding release, and two identical build-metadata versions. Parse every version, sort indexes by delegated semantic precedence with original-index tie breaking, and group equal-precedence entries.1

Delegated operations used for this result: version parse; semantic version compare. Their calculation or parsing conventions apply to the corresponding result fields.2, 3

Input fieldMeaning and unitsAccepted input
countRequested count; the mechanics explain whether one draw expands into several returned records.Required. Safe integer; minimum 1; maximum 32.
minimumComponentMinimum ComponentRequired. Safe integer; minimum 0.
maximumComponentMaximum ComponentRequired. Safe integer; minimum 0.
prereleaseLabelsPrerelease LabelsRequired. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 32.
buildLabelsBuild LabelsRequired. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 32.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
versionsVersionsRequired. Array of Text.; minimum items 4; maximum items 128.
sortedIndicesSorted IndicesRequired. Array of Safe integer.; minimum items 4; maximum items 128.
precedenceGroupsPrecedence GroupsRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 128.
precedenceGroups[].indicesIndicesRequired. Array of Safe integer.; minimum items 0; maximum items 128.
precedenceGroups[].distinctSpellingsDistinct SpellingsRequired. Safe integer.
randomnessReplay 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.seedOptional numeric 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": {
    "count": 1,
    "minimumComponent": 1,
    "maximumComponent": 3,
    "prereleaseLabels": [
      "alpha",
      "rc"
    ],
    "buildLabels": [
      "ci",
      "local"
    ],
    "seed": 42
  },
  "output": {
    "versions": [
      "2.2.3-rc.1",
      "2.2.3",
      "2.2.3+local.2",
      "2.2.3+local.2"
    ],
    "sortedIndices": [
      0,
      1,
      2,
      3
    ],
    "precedenceGroups": [
      {
        "indices": [
          0
        ],
        "distinctSpellings": 1
      },
      {
        "indices": [
          1,
          2,
          3
        ],
        "distinctSpellings": 2
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • Build metadata does not distinguish precedence, while exact duplicate spellings are retained. count produces four versions per draw. The stable key is an ordering fixture rather than package resolution or dependency solving.
  • 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

  1. Tool behavior and example: Random Semantic Version Ordering Corpus Generator — tool.io

  2. Calculation or validation: development/version-parse — tool.io

  3. Calculation or validation: development/semantic-version-compare — tool.io

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