FA-48106 / Delimited text / Open access

Default application coerces structured values to text · case 01

A structured table violates the declared record or column contract.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Typed schema defaults are passed through the text-cell conversion path.

VERIFIED REPAIR

Preserve the named invariant at the faulty decision: else: out.append(data['defaults'][i])

Unsuccessful approach: Special-casing null still stringifies structured defaults.

Case contract

Bind a two-column comma row to [a,b] with per-column defaults. An absent cell uses its default only if its required flag is false; a present empty cell stays empty. Surplus cells reject. Return ordered values. Defaults may be null or lists and must remain typed.

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):
    values = data['row'].split(',')
    if len(values) > 2: return None
    out = []
    for i in range(2):
        if i < len(values): out.append(values[i])
        elif data['required'][i]: return None
        else: out.append(str(data['defaults'][i]))
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('missing optional', solve(_vary({'row': '@', 'required': [True, False], 'defaults': ['x', 'y']})), _vary(['@', 'y']))
check('present empty', solve(_vary({'row': '@,', 'required': [True, False], 'defaults': ['x', 'y']})), _vary(['@', '']))
check('missing required', solve(_vary({'row': '@', 'required': [False, True], 'defaults': ['x', 'y']})), _vary(None))
check('typed default', solve(_vary({'row': '@', 'required': [False, False], 'defaults': [0, [1, 2]]})), _vary(['@', [1, 2]]))
check('null default', solve(_vary({'row': '@', 'required': [False, False], 'defaults': [0, None]})), _vary(['@', None]))
check('extra', solve(_vary({'row': 'a,b,c', 'required': [False, False], 'defaults': [0, 1]})), _vary(None))
check('both present', solve(_vary({'row': '@,z', 'required': [True, True], 'defaults': [0, 1]})), _vary(['@', 'z']))
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
missing optional	`['cell', 'y']`	`['cell', 'y']`	Passed
present empty	`['cell', '']`	`['cell', '']`	Passed
missing required	`None`	`None`	Passed
typed default	`['cell', '[1, 2]']`	`['cell', [1, 2]]`	Failed
null default	`['cell', 'None']`	`['cell', None]`	Failed
extra	`None`	`None`	Passed
both present	`['cell', 'z']`	`['cell', 'z']`	Passed

SHA-256 / 4a1f93a449bc405e1db58477b7c3dd148ada1e8cbbaac79099d6faa72e4811a4

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):
    values = data['row'].split(',')
    if len(values) > 2: return None
    out = []
    for i in range(2):
        if i < len(values): out.append(values[i])
        elif data['required'][i]: return None
        else: out.append('' if data['defaults'][i] is None else str(data['defaults'][i]))
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('missing optional', solve(_vary({'row': '@', 'required': [True, False], 'defaults': ['x', 'y']})), _vary(['@', 'y']))
check('present empty', solve(_vary({'row': '@,', 'required': [True, False], 'defaults': ['x', 'y']})), _vary(['@', '']))
check('missing required', solve(_vary({'row': '@', 'required': [False, True], 'defaults': ['x', 'y']})), _vary(None))
check('typed default', solve(_vary({'row': '@', 'required': [False, False], 'defaults': [0, [1, 2]]})), _vary(['@', [1, 2]]))
check('null default', solve(_vary({'row': '@', 'required': [False, False], 'defaults': [0, None]})), _vary(['@', None]))
check('extra', solve(_vary({'row': 'a,b,c', 'required': [False, False], 'defaults': [0, 1]})), _vary(None))
check('both present', solve(_vary({'row': '@,z', 'required': [True, True], 'defaults': [0, 1]})), _vary(['@', 'z']))
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
missing optional	`['cell', 'y']`	`['cell', 'y']`	Passed
present empty	`['cell', '']`	`['cell', '']`	Passed
missing required	`None`	`None`	Passed
typed default	`['cell', '[1, 2]']`	`['cell', [1, 2]]`	Failed
null default	`['cell', '']`	`['cell', None]`	Failed
extra	`None`	`None`	Passed
both present	`['cell', 'z']`	`['cell', 'z']`	Passed

SHA-256 / 565278616489732ce736b99d7440c60d9cf404eb8d132383e89891c74d70c1c1

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):
    values = data['row'].split(',')
    if len(values) > 2: return None
    out = []
    for i in range(2):
        if i < len(values): out.append(values[i])
        elif data['required'][i]: return None
        else: out.append(data['defaults'][i])
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('missing optional', solve(_vary({'row': '@', 'required': [True, False], 'defaults': ['x', 'y']})), _vary(['@', 'y']))
check('present empty', solve(_vary({'row': '@,', 'required': [True, False], 'defaults': ['x', 'y']})), _vary(['@', '']))
check('missing required', solve(_vary({'row': '@', 'required': [False, True], 'defaults': ['x', 'y']})), _vary(None))
check('typed default', solve(_vary({'row': '@', 'required': [False, False], 'defaults': [0, [1, 2]]})), _vary(['@', [1, 2]]))
check('null default', solve(_vary({'row': '@', 'required': [False, False], 'defaults': [0, None]})), _vary(['@', None]))
check('extra', solve(_vary({'row': 'a,b,c', 'required': [False, False], 'defaults': [0, 1]})), _vary(None))
check('both present', solve(_vary({'row': '@,z', 'required': [True, True], 'defaults': [0, 1]})), _vary(['@', 'z']))
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
missing optional	`['cell', 'y']`	`['cell', 'y']`	Passed
present empty	`['cell', '']`	`['cell', '']`	Passed
missing required	`None`	`None`	Passed
typed default	`['cell', [1, 2]]`	`['cell', [1, 2]]`	Passed
null default	`['cell', None]`	`['cell', None]`	Passed
extra	`None`	`None`	Passed
both present	`['cell', 'z']`	`['cell', 'z']`	Passed

SHA-256 / f801dbaa204d403ef7cce6a57d5ee283120fddf28c218c32fcb182f32d2c5088

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 2026-09-29T14:44:47.696555+00:00.

Case digest / 5f968dc3f16ef339640551587d28cefa83090f4c1b5d35d13de68b4f4174ff5d