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Random CSV Aggregate Reconciliation Exercise Generator

Generate synthetic grouped integer observations, plant one declared subtotal discrepancy and retain exact decimal-string subtotal, grand-total and checksum-row reconciliation answers.

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Description

Generate synthetic grouped integer observations, plant one declared subtotal discrepancy and retain exact decimal-string subtotal, grand-total and checksum-row reconciliation answers.

CSV stores records as delimited text fields. Quoting can protect embedded delimiters and newlines, so a record need not equal one physical line. Cleaning field contents, interpreting their types, and preserving an exact import/export round trip are separate operations.

Generate synthetic grouped integer observations, plant one declared subtotal discrepancy and retain exact decimal-string subtotal, grand-total and checksum-row reconciliation answers.

When to use this tool

Use this generator to create reproducible csv aggregate reconciliation exercise 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 groupCount, rowsPerGroup, minimumValue, maximumValue, discrepancy 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 rows, subtotals, changedGroup, correctGrandTotal, reportedGrandTotal, answerCsv. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Draw integer rows in fixed-size groups, compute exact delegated subtotals, and alter one reported subtotal by discrepancy. Return correct and reported grand totals as exact decimal strings and an answer CSV with a checksum total row.1

Delegated operations used for this result: sum aggregate per numeric column; checksum total row appender. Their calculation or parsing conventions apply to the corresponding result fields.2, 3

Input fieldMeaning and unitsAccepted input
groupCountGroup CountRequired. Safe integer; minimum 1; maximum 8.
rowsPerGroupRows Per GroupRequired. Safe integer; minimum 1; maximum 8.
minimumValueMinimum ValueRequired. Safe integer.
maximumValueMaximum ValueRequired. Safe integer.
discrepancyDiscrepancyRequired. Safe integer.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
rowsRowsRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 64.
rows[].groupGroupRequired. Text; maximum UTF-16 length 64.
rows[].valueValueRequired. Safe integer.
subtotalsSubtotalsRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 8.
subtotals[].groupGroupRequired. Text; maximum UTF-16 length 64.
subtotals[].correctCorrectRequired. Text; maximum UTF-16 length 64.
subtotals[].reportedReportedRequired. Text; maximum UTF-16 length 64.
subtotals[].discrepancyDiscrepancyRequired. Text; maximum UTF-16 length 64.
changedGroupChanged GroupRequired. Safe integer; minimum 0; maximum 7.
correctGrandTotalCorrect Grand TotalRequired. Text; maximum UTF-16 length 64.
reportedGrandTotalReported Grand TotalRequired. Text; maximum UTF-16 length 64.
answerCsvAnswer CsvRequired. Text; maximum UTF-16 length 100000.
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": {
    "groupCount": 2,
    "rowsPerGroup": 1,
    "minimumValue": 1,
    "maximumValue": 9,
    "discrepancy": 2,
    "seed": 42
  },
  "output": {
    "rows": [
      {
        "group": "G1",
        "value": 6
      },
      {
        "group": "G2",
        "value": 5
      }
    ],
    "subtotals": [
      {
        "group": "G1",
        "correct": "6",
        "reported": "6",
        "discrepancy": "0"
      },
      {
        "group": "G2",
        "correct": "5",
        "reported": "7",
        "discrepancy": "2"
      }
    ],
    "changedGroup": 1,
    "correctGrandTotal": "11",
    "reportedGrandTotal": "13",
    "answerCsv": "group,value\nG1,6\nG2,5\nGrand,11",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • changedGroup identifies the selected group even if discrepancy is zero. Numeric strings avoid loss of large aggregate precision. This constructs a reconciliation exercise rather than auditing actual accounts.
  • 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 CSV Aggregate Reconciliation Exercise Generator — tool.io

  2. Calculation or validation: csv/sum-aggregate-per-numeric-column — tool.io

  3. Calculation or validation: csv/checksum-total-row-appender — tool.io

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