FA-48206 / Delimited text / Open access
Transposed columns are emitted in reverse physical order · case 01
A structured table violates the declared record or column contract.
ROOT CAUSE
Transposed columns are emitted in reverse physical order.
VERIFIED REPAIR
Preserve the named invariant at the faulty decision: for col in range(width):
Unsuccessful approach: The alternate implementation still violates the same declared invariant: transposed columns are emitted in reverse physical order.
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 reversed(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 | [['b', None], ['a', None], ['cell', 'x']] | [['cell', 'x'], ['a', None], ['b', None]] | Failed |
| later wider | [[None, 'y'], ['cell', 'x']] | [['cell', 'x'], [None, 'y']] | Failed |
| explicit empty | [['', None], ['cell', 'x']] | [['cell', 'x'], ['', None]] | Failed |
| empty input | [] | [] | Passed |
| one empty | [['']] | [['']] | Passed |
| rectangular | [['b', 'd'], ['a', 'c']] | [['a', 'c'], ['b', 'd']] | Failed |
| unsorted | [['1', '2'], ['z', 'a']] | [['z', 'a'], ['1', '2']] | Failed |
| three rows | [['x', None, 'w'], ['cell', 'y', 'z']] | [['cell', 'y', 'z'], ['x', None, 'w']] | Failed |
SHA-256 / f2020dcca914a731336923f7b13205888e18aab343ec403828d25bb5671418d4
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(max(width-1,0)):
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]] | [['cell', 'x'], ['a', None], ['b', None]] | Failed |
| later wider | [['cell', 'x']] | [['cell', 'x'], [None, 'y']] | Failed |
| explicit empty | [['cell', 'x']] | [['cell', 'x'], ['', None]] | Failed |
| empty input | [] | [] | Passed |
| one empty | [] | [['']] | Failed |
| rectangular | [['a', 'c']] | [['a', 'c'], ['b', 'd']] | Failed |
| unsorted | [['z', 'a']] | [['z', 'a'], ['1', '2']] | Failed |
| three rows | [['cell', 'y', 'z']] | [['cell', 'y', 'z'], ['x', None, 'w']] | Failed |
SHA-256 / 5b34349324a32b9440b39a52eb99307e1e37740b263e64adcd1ffe5507e2dfa0
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 2026-09-29T14:44:48.704624+00:00.
Case digest / 48e4affd6af12095cb3ecba98562db08f036463e1f810c3cf1eecebb2d2ff8c1