FA-48201 / Delimited text / Open access
Short records are omitted from a transposed column · case 01
A structured table violates the declared record or column contract.
ROOT CAUSE
Short records are omitted from a transposed column.
VERIFIED REPAIR
Preserve the named invariant at the faulty decision: row[col] if col<len(row) else None for row in rows
Unsuccessful approach: The alternate implementation still violates the same declared invariant: short records are omitted from a transposed column.
Case contract
Transpose comma records into physical columns. Pad missing trailing cells with null, preserving explicit empty strings. Width is the largest source row width; empty input has no columns. Output columns are in increasing physical position.
Why this case matters
Delimited interchange needs explicit framing, schema and field semantics at ingestion and emission boundaries.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
def _vary(value):
if value == '@END': return 3 + 4*N
if isinstance(value, str): return value.replace('@', 'cell' * N)
if isinstance(value, list): return [_vary(x) for x in value]
if isinstance(value, dict): return {_vary(k): _vary(v) for k,v in value.items()}
return value
N = 1
observations = []
def solve(data):
rows=[line.split(',') for line in data]
width=max((len(row) for row in rows),default=0)
out=[]
for col in range(width):
out.append([row[col] for row in rows if col<len(row)])
return out
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('ragged', solve(_vary(['@,a,b', 'x'])), _vary([['@', 'x'], ['a', None], ['b', None]]))
check('later wider', solve(_vary(['@', 'x,y'])), _vary([['@', 'x'], [None, 'y']]))
check('explicit empty', solve(_vary(['@,', 'x'])), _vary([['@', 'x'], ['', None]]))
check('empty input', solve(_vary([])), _vary([]))
check('one empty', solve(_vary([''])), _vary([['']]))
check('rectangular', solve(_vary(['a,b', 'c,d'])), _vary([['a', 'c'], ['b', 'd']]))
check('unsorted', solve(_vary(['z,1', 'a,2'])), _vary([['z', 'a'], ['1', '2']]))
check('three rows', solve(_vary(['@,x', 'y', 'z,w'])), _vary([['@', 'y', 'z'], ['x', None, 'w']]))
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| ragged | [['cell', 'x'], ['a'], ['b']] | [['cell', 'x'], ['a', None], ['b', None]] | Failed |
| later wider | [['cell', 'x'], ['y']] | [['cell', 'x'], [None, 'y']] | Failed |
| explicit empty | [['cell', 'x'], ['']] | [['cell', 'x'], ['', None]] | Failed |
| empty input | [] | [] | Passed |
| one empty | [['']] | [['']] | Passed |
| rectangular | [['a', 'c'], ['b', 'd']] | [['a', 'c'], ['b', 'd']] | Passed |
| unsorted | [['z', 'a'], ['1', '2']] | [['z', 'a'], ['1', '2']] | Passed |
| three rows | [['cell', 'y', 'z'], ['x', 'w']] | [['cell', 'y', 'z'], ['x', None, 'w']] | Failed |
SHA-256 / 1870d0ed7683b436f08645533c90c88d21eed4580cb3dc3aefd3f20982d0274a
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
def _vary(value):
if value == '@END': return 3 + 4*N
if isinstance(value, str): return value.replace('@', 'cell' * N)
if isinstance(value, list): return [_vary(x) for x in value]
if isinstance(value, dict): return {_vary(k): _vary(v) for k,v in value.items()}
return value
N = 1
observations = []
def solve(data):
rows=[line.split(',') for line in data]
width=max((len(row) for row in rows),default=0)
out=[]
for col in range(width):
out.append([row[col] for row in rows if col<len(row) and row[col]])
return out
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('ragged', solve(_vary(['@,a,b', 'x'])), _vary([['@', 'x'], ['a', None], ['b', None]]))
check('later wider', solve(_vary(['@', 'x,y'])), _vary([['@', 'x'], [None, 'y']]))
check('explicit empty', solve(_vary(['@,', 'x'])), _vary([['@', 'x'], ['', None]]))
check('empty input', solve(_vary([])), _vary([]))
check('one empty', solve(_vary([''])), _vary([['']]))
check('rectangular', solve(_vary(['a,b', 'c,d'])), _vary([['a', 'c'], ['b', 'd']]))
check('unsorted', solve(_vary(['z,1', 'a,2'])), _vary([['z', 'a'], ['1', '2']]))
check('three rows', solve(_vary(['@,x', 'y', 'z,w'])), _vary([['@', 'y', 'z'], ['x', None, 'w']]))
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| ragged | [['cell', 'x'], ['a'], ['b']] | [['cell', 'x'], ['a', None], ['b', None]] | Failed |
| later wider | [['cell', 'x'], ['y']] | [['cell', 'x'], [None, 'y']] | Failed |
| explicit empty | [['cell', 'x'], []] | [['cell', 'x'], ['', None]] | Failed |
| empty input | [] | [] | Passed |
| one empty | [[]] | [['']] | Failed |
| rectangular | [['a', 'c'], ['b', 'd']] | [['a', 'c'], ['b', 'd']] | Passed |
| unsorted | [['z', 'a'], ['1', '2']] | [['z', 'a'], ['1', '2']] | Passed |
| three rows | [['cell', 'y', 'z'], ['x', 'w']] | [['cell', 'y', 'z'], ['x', None, 'w']] | Failed |
SHA-256 / c8fd25b7d9744ade9e5bb41316e15bf2da423c5f72eb78fb82d84562794284c3
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
def _vary(value):
if value == '@END': return 3 + 4*N
if isinstance(value, str): return value.replace('@', 'cell' * N)
if isinstance(value, list): return [_vary(x) for x in value]
if isinstance(value, dict): return {_vary(k): _vary(v) for k,v in value.items()}
return value
N = 1
observations = []
def solve(data):
rows=[line.split(',') for line in data]
width=max((len(row) for row in rows),default=0)
out=[]
for col in range(width):
out.append([row[col] if col<len(row) else None for row in rows])
return out
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('ragged', solve(_vary(['@,a,b', 'x'])), _vary([['@', 'x'], ['a', None], ['b', None]]))
check('later wider', solve(_vary(['@', 'x,y'])), _vary([['@', 'x'], [None, 'y']]))
check('explicit empty', solve(_vary(['@,', 'x'])), _vary([['@', 'x'], ['', None]]))
check('empty input', solve(_vary([])), _vary([]))
check('one empty', solve(_vary([''])), _vary([['']]))
check('rectangular', solve(_vary(['a,b', 'c,d'])), _vary([['a', 'c'], ['b', 'd']]))
check('unsorted', solve(_vary(['z,1', 'a,2'])), _vary([['z', 'a'], ['1', '2']]))
check('three rows', solve(_vary(['@,x', 'y', 'z,w'])), _vary([['@', 'y', 'z'], ['x', None, 'w']]))
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| ragged | [['cell', 'x'], ['a', None], ['b', None]] | [['cell', 'x'], ['a', None], ['b', None]] | Passed |
| later wider | [['cell', 'x'], [None, 'y']] | [['cell', 'x'], [None, 'y']] | Passed |
| explicit empty | [['cell', 'x'], ['', None]] | [['cell', 'x'], ['', None]] | Passed |
| empty input | [] | [] | Passed |
| one empty | [['']] | [['']] | Passed |
| rectangular | [['a', 'c'], ['b', 'd']] | [['a', 'c'], ['b', 'd']] | Passed |
| unsorted | [['z', 'a'], ['1', '2']] | [['z', 'a'], ['1', '2']] | Passed |
| three rows | [['cell', 'y', 'z'], ['x', None, 'w']] | [['cell', 'y', 'z'], ['x', None, 'w']] | Passed |
SHA-256 / 6296caec6a781f64e40df8dd2aeb6f8b323ee72696f392451e1d19df3db97602
Verification & scope
Deterministic bounded in-memory model. No claim of complete CSV or external format conformance. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:44:48.620575+00:00.
Case digest / 02cb0e40e443c211f30845fb6d0f52d69a0b104d13af234065c4b9b7a36d0671