Description
Append sampled exact and caller-defined one-cell near duplicates to supplied records, retaining source provenance and actual exact-duplicate groups including preexisting duplicates.
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.
Append sampled exact and caller-defined one-cell near duplicates to supplied records, retaining source provenance and actual exact-duplicate groups including preexisting duplicates.
When to use this tool
Use this generator to create reproducible csv duplicate record mutation 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 rows, exactCopies, nearCopies, nearColumn, replacement 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 rows, provenance, duplicateGroups, duplicateRowCount, csv. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.
Append exact copies and near copies of sampled original rows, changing the declared near column only for near copies. Retain source provenance, group complete equal rows, and delegate duplicate-row extraction/counting.1
Delegated operations used for this result: duplicate rows extractor. Their calculation or parsing conventions apply to the corresponding result fields.2
| Input field | Meaning and units | Accepted input |
|---|---|---|
| rows | Rows | Required. Array of Array of Text; maximum UTF-16 length 64.; minimum items 1; maximum items 8.; minimum items 1; maximum items 32. |
| exactCopies | Exact Copies | Required. Safe integer; minimum 0; maximum 16. |
| nearCopies | Near Copies | Required. Safe integer; minimum 0; maximum 16. |
| nearColumn | Near Column | Required. Safe integer; minimum 0; maximum 7. |
| replacement | Replacement | Required. Text; maximum UTF-16 length 64. |
| 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 |
|---|---|---|
| rows | Rows | Required. Array of Array of Text; maximum UTF-16 length 256.; minimum items 1; maximum items 10.; minimum items 0; maximum items 128. |
| provenance | Provenance | Required. Array of Object; no unknown keys.; minimum items 0; maximum items 64. |
| provenance[].sourceRow | Source Row | Required. Safe integer; minimum 0; maximum 31. |
| provenance[].kind | Kind | Required. Text; one of "original", "exact", "near". |
| duplicateGroups | Duplicate Groups | Required. Array of Array of Safe integer; minimum 0; maximum 63.; minimum items 0; maximum items 64.; minimum items 0; maximum items 32. |
| duplicateRowCount | Duplicate Row Count | Required. Safe integer; minimum 0; maximum 64. |
| csv | Csv | Required. Text; maximum UTF-16 length 100000. |
| randomness | Replay source and normalized 32-bit seed, or null seed for an advancing context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional safe-integer 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.
{
"input": {
"rows": [
[
"a",
"1"
],
[
"b",
"2"
]
],
"exactCopies": 1,
"nearCopies": 1,
"nearColumn": 1,
"replacement": "9",
"seed": 42
},
"output": {
"rows": [
[
"a",
"1"
],
[
"b",
"2"
],
[
"b",
"2"
],
[
"a",
"9"
]
],
"provenance": [
{
"sourceRow": 0,
"kind": "original"
},
{
"sourceRow": 1,
"kind": "original"
},
{
"sourceRow": 1,
"kind": "exact"
},
{
"sourceRow": 0,
"kind": "near"
}
],
"duplicateGroups": [
[
1,
2
]
],
"duplicateRowCount": 2,
"csv": "a,1\nb,2\nb,2\na,9",
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- Existing duplicates and repeated choices can increase duplicate groups beyond requested copy counts. Near replacements must differ from every source value in that column; near does not imply globally unique or nonduplicate.
- 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
Similar or alternative tools
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