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Random Query Parameter Duplicate Corpus Generator

Construct ordered repeated query keys and compare explicit first, last or all duplicate policies with legacy last-wins parsing.

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Description

Construct ordered repeated query keys and compare explicit first, last or all duplicate policies with legacy last-wins parsing.

A protocol fixture is local test data representing headers, messages, identifiers, or request decisions. Serialization and parsing can be checked without contacting a server, while authentication, delivery, and actual client behavior require separate tests.

Construct ordered repeated query keys and compare explicit first, last or all duplicate policies with legacy last-wins parsing.

When to use this tool

Use this generator to create reproducible query parameter duplicate 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 keys, values, pairCount, policy 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 pairs, query, parameterCount, resolved. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Guarantee a repeated key in the first two sampled query pairs, URI-encode all pairs, and delegate the legacy last-wins parser. Return first, last, or all values according to policy, alongside legacyLast and an actual parameter count.1

Delegated operations used for this result: query string; url query count. Their calculation or parsing conventions apply to the corresponding result fields.2, 3

Input fieldMeaning and unitsAccepted input
keysKeysRequired. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 16.
valuesValuesRequired. Array of Text; maximum UTF-16 length 65536.; minimum items 1; maximum items 16.
pairCountPair CountRequired. Safe integer; minimum 2; maximum 16.
policyPolicyRequired. Text; one of "first", "last", "all".
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
pairsPairsRequired. Array of Object; no unknown keys.; minimum items 2; maximum items 16.
pairs[].keyKeyRequired. Text; maximum UTF-16 length 65536.
pairs[].valueValueRequired. Text; maximum UTF-16 length 65536.
queryQueryRequired. Text; maximum UTF-16 length 65536.
parameterCountParameter CountRequired. Safe integer; minimum 2; maximum 16.
resolvedResolvedRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 16.
resolved[].keyKeyRequired. Text; maximum UTF-16 length 65536.
resolved[].valuesValuesRequired. Array of Text; maximum UTF-16 length 65536.; minimum items 0; maximum items 16.
resolved[].legacyLastLegacy LastRequired. Text; maximum UTF-16 length 65536.
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": {
    "keys": [
      "q"
    ],
    "values": [
      "red",
      "blue"
    ],
    "pairCount": 2,
    "policy": "all",
    "seed": 42
  },
  "output": {
    "pairs": [
      {
        "key": "q",
        "value": "red"
      },
      {
        "key": "q",
        "value": "blue"
      }
    ],
    "query": "q=red&q=blue",
    "parameterCount": 2,
    "resolved": [
      {
        "key": "q",
        "values": [
          "red",
          "blue"
        ],
        "legacyLast": "blue"
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • Policy resolution is an explicit comparison fixture, not a change to the legacy parser. Unsafe object keys are rejected and encoded length is bounded; repeated-key semantics differ among applications.
  • 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 Query Parameter Duplicate Corpus Generator — tool.io

  2. Calculation or validation: parse/query-string — tool.io

  3. Calculation or validation: web/url-query-count — tool.io

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