FAILURE MAP
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FA-57871 / Double-entry ledger accounting / Open access

Trial balance columns: column selection · case 01

An overdrawn cash account appears as a negative number in the debit column.

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

ROOT CAUSE

The column is chosen from the account type instead of the sign of the net balance.

THE FAILURE

The column is chosen from the account type instead of the sign of the net balance.

Unsuccessful approach: Also sending every debit-normal account to the debit column still prints negative debit amounts.

Case contract

x = [[account_code (digit string), type, total_debits, total_credits], ...]. Net = debits - credits. Zero-net accounts are omitted. A positive net goes in the debit column, a negative net (as a positive number) in the credit column. Rows are ordered by numeric account code. An account is abnormal when its column differs from its normal side (asset, expense, dividend debit-normal; others credit-normal). Return {'rows': [[code, debit, credit]], 'totals': [debits, credits], 'balanced': totals equal exactly, 'abnormal': codes in numeric order}.

Why this case matters

Ledger software must keep debits equal to credits and apply normal-balance, period and cutoff rules exactly; small sign or boundary slips silently misstate financial statements.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    debit_normal = ('asset', 'expense', 'dividend')
    rows = []
    td = tc = 0
    abnormal = []
    for acct, kind, dr, cr in x:
        net = dr - cr
        if net == 0:
            continue
        if kind in debit_normal:
            rows.append([acct, net, 0])
            td += net
        else:
            rows.append([acct, 0, -net])
            tc += -net
        if (net > 0) != (kind in debit_normal):
            abnormal.append(acct)
    rows.sort(key=lambda r: int(r[0]))
    return {'rows': rows, 'totals': [td, tc], 'balanced': td == tc, 'abnormal': sorted(abnormal, key=int)}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression: column selection', [['101', 'expense', 900, 0], ['2100', 'revenue', 0, 1], ['510', 'liability', 0, 250], ['7200', 'expense', 1234, 5000], ['1500', 'expense', 1234, 1235], ['150', 'expense', 250, 249], ['4000', 'expense', 0, 0]], {'rows': [['101', 900, 0], ['150', 1, 0], ['510', 0, 250], ['1500', 0, 1], ['2100', 0, 1], ['7200', 0, 3766]], 'totals': [901, 4018], 'balanced': False, 'abnormal': ['1500', '7200']}], ['control 1', [['4000', 'dividend', 250, 0], ['7200', 'dividend', 5000, 0], ['1500', 'equity', 0, 1]], {'rows': [['1500', 0, 1], ['4000', 250, 0], ['7200', 5000, 0]], 'totals': [5250, 1], 'balanced': False, 'abnormal': []}], ['control 2', [['150', 'liability', 1234, 1234], ['101', 'asset', 0, -1], ['205', 'equity', 0, 0], ['7200', 'dividend', 900, 250]], {'rows': [['101', 1, 0], ['7200', 650, 0]], 'totals': [651, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['2100', 'revenue', 0, 0], ['4000', 'revenue', 0, 0], ['99', 'equity', 5000, 5000], ['510', 'asset', 0, 0]], {'rows': [], 'totals': [0, 0], 'balanced': True, 'abnormal': []}], ['control 4', [['101', 'equity', 0, 5000], ['30', 'equity', 0, 0], ['205', 'liability', 0, 1], ['2100', 'liability', 250, 251], ['4000', 'asset', 1234, 900], ['1010', 'equity', 0, 1234]], {'rows': [['101', 0, 5000], ['205', 0, 1], ['1010', 0, 1234], ['2100', 0, 1], ['4000', 334, 0]], 'totals': [334, 6236], 'balanced': False, 'abnormal': []}], ['control 5', [['4000', 'dividend', 250, 249], ['150', 'equity', 900, 5000], ['1500', 'liability', 0, 0], ['2100', 'liability', 100, 100], ['6', 'asset', 5000, 0], ['99', 'expense', 100, 0], ['7200', 'equity', 5000, 5001]], {'rows': [['6', 5000, 0], ['99', 100, 0], ['150', 0, 4100], ['4000', 1, 0], ['7200', 0, 1]], 'totals': [5101, 4101], 'balanced': False, 'abnormal': []}], ['control 6', [['2100', 'liability', 0, 1], ['6', 'asset', 5000, 4999], ['7200', 'equity', 0, 0], ['1500', 'revenue', 0, 5000]], {'rows': [['6', 1, 0], ['1500', 0, 5000], ['2100', 0, 1]], 'totals': [1, 5001], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['6', 'expense', 0, 0], ['30', 'asset', 250, 249], ['7200', 'expense', 5000, 5000], ['150', 'expense', 100, 100], ['2100', 'dividend', 900, 901], ['205', 'equity', 100, 101]], {'rows': [['30', 1, 0], ['205', 0, 1], ['2100', 0, 1]], 'totals': [1, 2], 'balanced': False, 'abnormal': ['2100']}], ['control 1', [['205', 'revenue', 0, 5000], ['150', 'revenue', 250, 1234], ['510', 'asset', 1234, 1234], ['1500', 'asset', 0, 0], ['2100', 'expense', 100, 100], ['1010', 'expense', 1234, 0], ['7200', 'equity', 0, 5000]], {'rows': [['150', 0, 984], ['205', 0, 5000], ['1010', 1234, 0], ['7200', 0, 5000]], 'totals': [1234, 10984], 'balanced': False, 'abnormal': []}], ['control 2', [['1010', 'revenue', 0, 250], ['101', 'dividend', 250, 250], ['205', 'equity', 250, 5000], ['99', 'asset', 5000, 900]], {'rows': [['99', 4100, 0], ['205', 0, 4750], ['1010', 0, 250]], 'totals': [4100, 5000], 'balanced': False, 'abnormal': []}], ['control 3', [['1010', 'asset', 250, 0], ['510', 'expense', 5000, 1234], ['7200', 'dividend', 100, 100]], {'rows': [['510', 3766, 0], ['1010', 250, 0]], 'totals': [4016, 0], 'balanced': False, 'abnormal': []}], ['control 4', [['30', 'expense', 5000, 250], ['4000', 'liability', 0, 250], ['205', 'equity', 0, 100]], {'rows': [['30', 4750, 0], ['205', 0, 100], ['4000', 0, 250]], 'totals': [4750, 350], 'balanced': False, 'abnormal': []}], ['control 5', [['1500', 'liability', 250, 900], ['99', 'revenue', 0, 5000], ['1010', 'revenue', 1234, 5000]], {'rows': [['99', 0, 5000], ['1010', 0, 3766], ['1500', 0, 650]], 'totals': [0, 9416], 'balanced': False, 'abnormal': []}], ['control 6', [['6', 'dividend', 1234, 1233], ['2100', 'dividend', 900, 0], ['1010', 'equity', 900, 5000], ['30', 'asset', 0, 0], ['7200', 'equity', 0, 250]], {'rows': [['6', 1, 0], ['1010', 0, 4100], ['2100', 900, 0], ['7200', 0, 250]], 'totals': [901, 4350], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['2100', 'expense', 100, 5000], ['7200', 'liability', 5000, 100], ['6', 'dividend', 250, 1234], ['99', 'dividend', 0, 1234], ['205', 'expense', 5000, 5001], ['101', 'equity', 0, 1234], ['4000', 'revenue', 250, 5000]], {'rows': [['6', 0, 984], ['99', 0, 1234], ['101', 0, 1234], ['205', 0, 1], ['2100', 0, 4900], ['4000', 0, 4750], ['7200', 4900, 0]], 'totals': [4900, 13103], 'balanced': False, 'abnormal': ['6', '99', '205', '2100', '7200']}], ['control 1', [['99', 'liability', 0, 0], ['510', 'liability', 900, 900], ['205', 'equity', 250, 5000]], {'rows': [['205', 0, 4750]], 'totals': [0, 4750], 'balanced': False, 'abnormal': []}], ['control 2', [['7200', 'revenue', 250, 250], ['99', 'asset', 1234, 250], ['205', 'asset', 5000, 0]], {'rows': [['99', 984, 0], ['205', 5000, 0]], 'totals': [5984, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['1010', 'asset', 1234, 0], ['205', 'expense', 1234, 900], ['150', 'equity', 0, 900], ['510', 'expense', 100, 100]], {'rows': [['150', 0, 900], ['205', 334, 0], ['1010', 1234, 0]], 'totals': [1568, 900], 'balanced': False, 'abnormal': []}], ['control 4', [['510', 'equity', 1234, 1234], ['150', 'liability', 0, 100], ['6', 'equity', 1234, 1234], ['205', 'revenue', 900, 1234], ['2100', 'dividend', 0, 0], ['101', 'liability', 0, 1]], {'rows': [['101', 0, 1], ['150', 0, 100], ['205', 0, 334]], 'totals': [0, 435], 'balanced': False, 'abnormal': []}], ['control 5', [['30', 'equity', 250, 251], ['4000', 'liability', 0, 0], ['150', 'dividend', 5000, 0]], {'rows': [['30', 0, 1], ['150', 5000, 0]], 'totals': [5000, 1], 'balanced': False, 'abnormal': []}], ['control 6', [['4000', 'equity', 250, 250], ['1010', 'expense', 1234, 1233], ['30', 'dividend', 100, 100], ['510', 'asset', 250, 0], ['7200', 'liability', 0, 0]], {'rows': [['510', 250, 0], ['1010', 1, 0]], 'totals': [251, 0], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['99', 'liability', 1234, 1233], ['30', 'dividend', 5000, 900], ['150', 'liability', 1234, 1234], ['1010', 'liability', 250, 1234], ['510', 'liability', 100, 0], ['4000', 'expense', 100, 100]], {'rows': [['30', 4100, 0], ['99', 1, 0], ['510', 100, 0], ['1010', 0, 984]], 'totals': [4201, 984], 'balanced': False, 'abnormal': ['99', '510']}], ['regression: column selection, partial-repair probe', [['30', 'dividend', 250, 100], ['4000', 'expense', 100, 5000], ['205', 'dividend', 100, 100], ['510', 'expense', 0, 1234], ['99', 'asset', 900, 901], ['101', 'revenue', 100, 99]], {'rows': [['30', 150, 0], ['99', 0, 1], ['101', 1, 0], ['510', 0, 1234], ['4000', 0, 4900]], 'totals': [151, 6135], 'balanced': False, 'abnormal': ['99', '101', '510', '4000']}], ['control 1', [['30', 'liability', 250, 1234], ['6', 'dividend', 250, 250], ['7200', 'revenue', 100, 101], ['510', 'revenue', 100, 100], ['1010', 'liability', 0, 0], ['2100', 'asset', 5000, 0], ['4000', 'expense', 1234, 100]], {'rows': [['30', 0, 984], ['2100', 5000, 0], ['4000', 1134, 0], ['7200', 0, 1]], 'totals': [6134, 985], 'balanced': False, 'abnormal': []}], ['control 2', [['6', 'asset', 5000, 900], ['30', 'asset', 100, 0], ['1500', 'liability', 100, 100]], {'rows': [['6', 4100, 0], ['30', 100, 0]], 'totals': [4200, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['150', 'dividend', 100, 0], ['6', 'dividend', 900, 0], ['1500', 'revenue', 1234, 1234], ['30', 'equity', 100, 100]], {'rows': [['6', 900, 0], ['150', 100, 0]], 'totals': [1000, 0], 'balanced': False, 'abnormal': []}], ['control 4', [['101', 'dividend', 5000, 4999], ['99', 'asset', 100, 100], ['30', 'expense', 250, 249]], {'rows': [['30', 1, 0], ['101', 1, 0]], 'totals': [2, 0], 'balanced': False, 'abnormal': []}], ['control 5', [['1500', 'expense', 900, 900], ['1010', 'revenue', 0, 0], ['101', 'equity', 1234, 5000], ['7200', 'dividend', 250, 0], ['150', 'liability', 0, 0], ['4000', 'expense', 5000, 900]], {'rows': [['101', 0, 3766], ['4000', 4100, 0], ['7200', 250, 0]], 'totals': [4350, 3766], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['101', 'equity', 100, 900], ['4000', 'equity', 0, 1], ['7200', 'expense', 100, 0], ['2100', 'dividend', 0, 5000], ['6', 'liability', 250, 250], ['150', 'equity', 0, 250]], {'rows': [['101', 0, 800], ['150', 0, 250], ['2100', 0, 5000], ['4000', 0, 1], ['7200', 100, 0]], 'totals': [100, 6051], 'balanced': False, 'abnormal': ['2100']}], ['control 1', [['4000', 'revenue', 0, 1], ['30', 'expense', 250, 250], ['2100', 'asset', 900, 100]], {'rows': [['2100', 800, 0], ['4000', 0, 1]], 'totals': [800, 1], 'balanced': False, 'abnormal': []}], ['control 2', [['150', 'expense', 100, 0], ['101', 'liability', 250, 250], ['99', 'liability', 100, 100], ['205', 'equity', 100, 100], ['510', 'expense', 100, 0], ['7200', 'asset', 5000, 5000]], {'rows': [['150', 100, 0], ['510', 100, 0]], 'totals': [200, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['1500', 'expense', 1234, 100], ['205', 'expense', 100, 100], ['510', 'equity', 250, 1234]], {'rows': [['510', 0, 984], ['1500', 1134, 0]], 'totals': [1134, 984], 'balanced': False, 'abnormal': []}], ['control 4', [['6', 'revenue', 1234, 5000], ['30', 'asset', 900, 900], ['1500', 'equity', 250, 250]], {'rows': [['6', 0, 3766]], 'totals': [0, 3766], 'balanced': False, 'abnormal': []}], ['control 5', [['4000', 'expense', 5000, 5000], ['510', 'liability', 250, 251], ['101', 'liability', 0, 250], ['1500', 'dividend', 5000, 900], ['6', 'asset', 1234, 0], ['150', 'equity', 0, 900], ['99', 'liability', 250, 251]], {'rows': [['6', 1234, 0], ['99', 0, 1], ['101', 0, 250], ['150', 0, 900], ['510', 0, 1], ['1500', 4100, 0]], 'totals': [5334, 1152], 'balanced': False, 'abnormal': []}], ['control 6', [['99', 'dividend', 0, 0], ['1010', 'liability', 0, 1234], ['30', 'equity', 100, 5000]], {'rows': [['30', 0, 4900], ['1010', 0, 1234]], 'totals': [0, 6134], 'balanced': False, 'abnormal': []}]]]
for label, args, expected in fixtures[N-1]:
    try:
        actual = solve(args)
    except Exception as exc:
        actual = 'raised ' + type(exc).__name__
    check(label, actual, expected)
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 fixtureActualExpectedOutcome
regression: column selection{'abnormal': ['1500', '7200'], 'balanced': False, 'rows': [['101', 900, 0], ['150', 1, 0], ['510', 0, 250], ['1500', -1, 0], ['2100', 0, 1], ['7200', -3766, 0]], 'totals': [-2866, 251]}{'abnormal': ['1500', '7200'], 'balanced': False, 'rows': [['101', 900, 0], ['150', 1, 0], ['510', 0, 250], ['1500', 0, 1], ['2100', 0, 1], ['7200', 0, 3766]], 'totals': [901, 4018]}Failed
control 1{'abnormal': [], 'balanced': False, 'rows': [['1500', 0, 1], ['4000', 250, 0], ['7200', 5000, 0]], 'totals': [5250, 1]}{'abnormal': [], 'balanced': False, 'rows': [['1500', 0, 1], ['4000', 250, 0], ['7200', 5000, 0]], 'totals': [5250, 1]}Passed
control 2{'abnormal': [], 'balanced': False, 'rows': [['101', 1, 0], ['7200', 650, 0]], 'totals': [651, 0]}{'abnormal': [], 'balanced': False, 'rows': [['101', 1, 0], ['7200', 650, 0]], 'totals': [651, 0]}Passed
control 3{'abnormal': [], 'balanced': True, 'rows': [], 'totals': [0, 0]}{'abnormal': [], 'balanced': True, 'rows': [], 'totals': [0, 0]}Passed
control 4{'abnormal': [], 'balanced': False, 'rows': [['101', 0, 5000], ['205', 0, 1], ['1010', 0, 1234], ['2100', 0, 1], ['4000', 334, 0]], 'totals': [334, 6236]}{'abnormal': [], 'balanced': False, 'rows': [['101', 0, 5000], ['205', 0, 1], ['1010', 0, 1234], ['2100', 0, 1], ['4000', 334, 0]], 'totals': [334, 6236]}Passed
control 5{'abnormal': [], 'balanced': False, 'rows': [['6', 5000, 0], ['99', 100, 0], ['150', 0, 4100], ['4000', 1, 0], ['7200', 0, 1]], 'totals': [5101, 4101]}{'abnormal': [], 'balanced': False, 'rows': [['6', 5000, 0], ['99', 100, 0], ['150', 0, 4100], ['4000', 1, 0], ['7200', 0, 1]], 'totals': [5101, 4101]}Passed
control 6{'abnormal': [], 'balanced': False, 'rows': [['6', 1, 0], ['1500', 0, 5000], ['2100', 0, 1]], 'totals': [1, 5001]}{'abnormal': [], 'balanced': False, 'rows': [['6', 1, 0], ['1500', 0, 5000], ['2100', 0, 1]], 'totals': [1, 5001]}Passed

SHA-256 / d18a694cd3573e32b3d50f15fa6e09b7005db5b97e08f27c1a0af7dd557277e3

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    debit_normal = ('asset', 'expense', 'dividend')
    rows = []
    td = tc = 0
    abnormal = []
    for acct, kind, dr, cr in x:
        net = dr - cr
        if net == 0:
            continue
        if net > 0 or kind in debit_normal:
            rows.append([acct, net, 0])
            td += net
        else:
            rows.append([acct, 0, -net])
            tc += -net
        if (net > 0) != (kind in debit_normal):
            abnormal.append(acct)
    rows.sort(key=lambda r: int(r[0]))
    return {'rows': rows, 'totals': [td, tc], 'balanced': td == tc, 'abnormal': sorted(abnormal, key=int)}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression: column selection', [['101', 'expense', 900, 0], ['2100', 'revenue', 0, 1], ['510', 'liability', 0, 250], ['7200', 'expense', 1234, 5000], ['1500', 'expense', 1234, 1235], ['150', 'expense', 250, 249], ['4000', 'expense', 0, 0]], {'rows': [['101', 900, 0], ['150', 1, 0], ['510', 0, 250], ['1500', 0, 1], ['2100', 0, 1], ['7200', 0, 3766]], 'totals': [901, 4018], 'balanced': False, 'abnormal': ['1500', '7200']}], ['control 1', [['4000', 'dividend', 250, 0], ['7200', 'dividend', 5000, 0], ['1500', 'equity', 0, 1]], {'rows': [['1500', 0, 1], ['4000', 250, 0], ['7200', 5000, 0]], 'totals': [5250, 1], 'balanced': False, 'abnormal': []}], ['control 2', [['150', 'liability', 1234, 1234], ['101', 'asset', 0, -1], ['205', 'equity', 0, 0], ['7200', 'dividend', 900, 250]], {'rows': [['101', 1, 0], ['7200', 650, 0]], 'totals': [651, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['2100', 'revenue', 0, 0], ['4000', 'revenue', 0, 0], ['99', 'equity', 5000, 5000], ['510', 'asset', 0, 0]], {'rows': [], 'totals': [0, 0], 'balanced': True, 'abnormal': []}], ['control 4', [['101', 'equity', 0, 5000], ['30', 'equity', 0, 0], ['205', 'liability', 0, 1], ['2100', 'liability', 250, 251], ['4000', 'asset', 1234, 900], ['1010', 'equity', 0, 1234]], {'rows': [['101', 0, 5000], ['205', 0, 1], ['1010', 0, 1234], ['2100', 0, 1], ['4000', 334, 0]], 'totals': [334, 6236], 'balanced': False, 'abnormal': []}], ['control 5', [['4000', 'dividend', 250, 249], ['150', 'equity', 900, 5000], ['1500', 'liability', 0, 0], ['2100', 'liability', 100, 100], ['6', 'asset', 5000, 0], ['99', 'expense', 100, 0], ['7200', 'equity', 5000, 5001]], {'rows': [['6', 5000, 0], ['99', 100, 0], ['150', 0, 4100], ['4000', 1, 0], ['7200', 0, 1]], 'totals': [5101, 4101], 'balanced': False, 'abnormal': []}], ['control 6', [['2100', 'liability', 0, 1], ['6', 'asset', 5000, 4999], ['7200', 'equity', 0, 0], ['1500', 'revenue', 0, 5000]], {'rows': [['6', 1, 0], ['1500', 0, 5000], ['2100', 0, 1]], 'totals': [1, 5001], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['6', 'expense', 0, 0], ['30', 'asset', 250, 249], ['7200', 'expense', 5000, 5000], ['150', 'expense', 100, 100], ['2100', 'dividend', 900, 901], ['205', 'equity', 100, 101]], {'rows': [['30', 1, 0], ['205', 0, 1], ['2100', 0, 1]], 'totals': [1, 2], 'balanced': False, 'abnormal': ['2100']}], ['control 1', [['205', 'revenue', 0, 5000], ['150', 'revenue', 250, 1234], ['510', 'asset', 1234, 1234], ['1500', 'asset', 0, 0], ['2100', 'expense', 100, 100], ['1010', 'expense', 1234, 0], ['7200', 'equity', 0, 5000]], {'rows': [['150', 0, 984], ['205', 0, 5000], ['1010', 1234, 0], ['7200', 0, 5000]], 'totals': [1234, 10984], 'balanced': False, 'abnormal': []}], ['control 2', [['1010', 'revenue', 0, 250], ['101', 'dividend', 250, 250], ['205', 'equity', 250, 5000], ['99', 'asset', 5000, 900]], {'rows': [['99', 4100, 0], ['205', 0, 4750], ['1010', 0, 250]], 'totals': [4100, 5000], 'balanced': False, 'abnormal': []}], ['control 3', [['1010', 'asset', 250, 0], ['510', 'expense', 5000, 1234], ['7200', 'dividend', 100, 100]], {'rows': [['510', 3766, 0], ['1010', 250, 0]], 'totals': [4016, 0], 'balanced': False, 'abnormal': []}], ['control 4', [['30', 'expense', 5000, 250], ['4000', 'liability', 0, 250], ['205', 'equity', 0, 100]], {'rows': [['30', 4750, 0], ['205', 0, 100], ['4000', 0, 250]], 'totals': [4750, 350], 'balanced': False, 'abnormal': []}], ['control 5', [['1500', 'liability', 250, 900], ['99', 'revenue', 0, 5000], ['1010', 'revenue', 1234, 5000]], {'rows': [['99', 0, 5000], ['1010', 0, 3766], ['1500', 0, 650]], 'totals': [0, 9416], 'balanced': False, 'abnormal': []}], ['control 6', [['6', 'dividend', 1234, 1233], ['2100', 'dividend', 900, 0], ['1010', 'equity', 900, 5000], ['30', 'asset', 0, 0], ['7200', 'equity', 0, 250]], {'rows': [['6', 1, 0], ['1010', 0, 4100], ['2100', 900, 0], ['7200', 0, 250]], 'totals': [901, 4350], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['2100', 'expense', 100, 5000], ['7200', 'liability', 5000, 100], ['6', 'dividend', 250, 1234], ['99', 'dividend', 0, 1234], ['205', 'expense', 5000, 5001], ['101', 'equity', 0, 1234], ['4000', 'revenue', 250, 5000]], {'rows': [['6', 0, 984], ['99', 0, 1234], ['101', 0, 1234], ['205', 0, 1], ['2100', 0, 4900], ['4000', 0, 4750], ['7200', 4900, 0]], 'totals': [4900, 13103], 'balanced': False, 'abnormal': ['6', '99', '205', '2100', '7200']}], ['control 1', [['99', 'liability', 0, 0], ['510', 'liability', 900, 900], ['205', 'equity', 250, 5000]], {'rows': [['205', 0, 4750]], 'totals': [0, 4750], 'balanced': False, 'abnormal': []}], ['control 2', [['7200', 'revenue', 250, 250], ['99', 'asset', 1234, 250], ['205', 'asset', 5000, 0]], {'rows': [['99', 984, 0], ['205', 5000, 0]], 'totals': [5984, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['1010', 'asset', 1234, 0], ['205', 'expense', 1234, 900], ['150', 'equity', 0, 900], ['510', 'expense', 100, 100]], {'rows': [['150', 0, 900], ['205', 334, 0], ['1010', 1234, 0]], 'totals': [1568, 900], 'balanced': False, 'abnormal': []}], ['control 4', [['510', 'equity', 1234, 1234], ['150', 'liability', 0, 100], ['6', 'equity', 1234, 1234], ['205', 'revenue', 900, 1234], ['2100', 'dividend', 0, 0], ['101', 'liability', 0, 1]], {'rows': [['101', 0, 1], ['150', 0, 100], ['205', 0, 334]], 'totals': [0, 435], 'balanced': False, 'abnormal': []}], ['control 5', [['30', 'equity', 250, 251], ['4000', 'liability', 0, 0], ['150', 'dividend', 5000, 0]], {'rows': [['30', 0, 1], ['150', 5000, 0]], 'totals': [5000, 1], 'balanced': False, 'abnormal': []}], ['control 6', [['4000', 'equity', 250, 250], ['1010', 'expense', 1234, 1233], ['30', 'dividend', 100, 100], ['510', 'asset', 250, 0], ['7200', 'liability', 0, 0]], {'rows': [['510', 250, 0], ['1010', 1, 0]], 'totals': [251, 0], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['99', 'liability', 1234, 1233], ['30', 'dividend', 5000, 900], ['150', 'liability', 1234, 1234], ['1010', 'liability', 250, 1234], ['510', 'liability', 100, 0], ['4000', 'expense', 100, 100]], {'rows': [['30', 4100, 0], ['99', 1, 0], ['510', 100, 0], ['1010', 0, 984]], 'totals': [4201, 984], 'balanced': False, 'abnormal': ['99', '510']}], ['regression: column selection, partial-repair probe', [['30', 'dividend', 250, 100], ['4000', 'expense', 100, 5000], ['205', 'dividend', 100, 100], ['510', 'expense', 0, 1234], ['99', 'asset', 900, 901], ['101', 'revenue', 100, 99]], {'rows': [['30', 150, 0], ['99', 0, 1], ['101', 1, 0], ['510', 0, 1234], ['4000', 0, 4900]], 'totals': [151, 6135], 'balanced': False, 'abnormal': ['99', '101', '510', '4000']}], ['control 1', [['30', 'liability', 250, 1234], ['6', 'dividend', 250, 250], ['7200', 'revenue', 100, 101], ['510', 'revenue', 100, 100], ['1010', 'liability', 0, 0], ['2100', 'asset', 5000, 0], ['4000', 'expense', 1234, 100]], {'rows': [['30', 0, 984], ['2100', 5000, 0], ['4000', 1134, 0], ['7200', 0, 1]], 'totals': [6134, 985], 'balanced': False, 'abnormal': []}], ['control 2', [['6', 'asset', 5000, 900], ['30', 'asset', 100, 0], ['1500', 'liability', 100, 100]], {'rows': [['6', 4100, 0], ['30', 100, 0]], 'totals': [4200, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['150', 'dividend', 100, 0], ['6', 'dividend', 900, 0], ['1500', 'revenue', 1234, 1234], ['30', 'equity', 100, 100]], {'rows': [['6', 900, 0], ['150', 100, 0]], 'totals': [1000, 0], 'balanced': False, 'abnormal': []}], ['control 4', [['101', 'dividend', 5000, 4999], ['99', 'asset', 100, 100], ['30', 'expense', 250, 249]], {'rows': [['30', 1, 0], ['101', 1, 0]], 'totals': [2, 0], 'balanced': False, 'abnormal': []}], ['control 5', [['1500', 'expense', 900, 900], ['1010', 'revenue', 0, 0], ['101', 'equity', 1234, 5000], ['7200', 'dividend', 250, 0], ['150', 'liability', 0, 0], ['4000', 'expense', 5000, 900]], {'rows': [['101', 0, 3766], ['4000', 4100, 0], ['7200', 250, 0]], 'totals': [4350, 3766], 'balanced': False, 'abnormal': []}]], [['regression: column selection', [['101', 'equity', 100, 900], ['4000', 'equity', 0, 1], ['7200', 'expense', 100, 0], ['2100', 'dividend', 0, 5000], ['6', 'liability', 250, 250], ['150', 'equity', 0, 250]], {'rows': [['101', 0, 800], ['150', 0, 250], ['2100', 0, 5000], ['4000', 0, 1], ['7200', 100, 0]], 'totals': [100, 6051], 'balanced': False, 'abnormal': ['2100']}], ['control 1', [['4000', 'revenue', 0, 1], ['30', 'expense', 250, 250], ['2100', 'asset', 900, 100]], {'rows': [['2100', 800, 0], ['4000', 0, 1]], 'totals': [800, 1], 'balanced': False, 'abnormal': []}], ['control 2', [['150', 'expense', 100, 0], ['101', 'liability', 250, 250], ['99', 'liability', 100, 100], ['205', 'equity', 100, 100], ['510', 'expense', 100, 0], ['7200', 'asset', 5000, 5000]], {'rows': [['150', 100, 0], ['510', 100, 0]], 'totals': [200, 0], 'balanced': False, 'abnormal': []}], ['control 3', [['1500', 'expense', 1234, 100], ['205', 'expense', 100, 100], ['510', 'equity', 250, 1234]], {'rows': [['510', 0, 984], ['1500', 1134, 0]], 'totals': [1134, 984], 'balanced': False, 'abnormal': []}], ['control 4', [['6', 'revenue', 1234, 5000], ['30', 'asset', 900, 900], ['1500', 'equity', 250, 250]], {'rows': [['6', 0, 3766]], 'totals': [0, 3766], 'balanced': False, 'abnormal': []}], ['control 5', [['4000', 'expense', 5000, 5000], ['510', 'liability', 250, 251], ['101', 'liability', 0, 250], ['1500', 'dividend', 5000, 900], ['6', 'asset', 1234, 0], ['150', 'equity', 0, 900], ['99', 'liability', 250, 251]], {'rows': [['6', 1234, 0], ['99', 0, 1], ['101', 0, 250], ['150', 0, 900], ['510', 0, 1], ['1500', 4100, 0]], 'totals': [5334, 1152], 'balanced': False, 'abnormal': []}], ['control 6', [['99', 'dividend', 0, 0], ['1010', 'liability', 0, 1234], ['30', 'equity', 100, 5000]], {'rows': [['30', 0, 4900], ['1010', 0, 1234]], 'totals': [0, 6134], 'balanced': False, 'abnormal': []}]]]
for label, args, expected in fixtures[N-1]:
    try:
        actual = solve(args)
    except Exception as exc:
        actual = 'raised ' + type(exc).__name__
    check(label, actual, expected)
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 fixtureActualExpectedOutcome
regression: column selection{'abnormal': ['1500', '7200'], 'balanced': False, 'rows': [['101', 900, 0], ['150', 1, 0], ['510', 0, 250], ['1500', -1, 0], ['2100', 0, 1], ['7200', -3766, 0]], 'totals': [-2866, 251]}{'abnormal': ['1500', '7200'], 'balanced': False, 'rows': [['101', 900, 0], ['150', 1, 0], ['510', 0, 250], ['1500', 0, 1], ['2100', 0, 1], ['7200', 0, 3766]], 'totals': [901, 4018]}Failed
control 1{'abnormal': [], 'balanced': False, 'rows': [['1500', 0, 1], ['4000', 250, 0], ['7200', 5000, 0]], 'totals': [5250, 1]}{'abnormal': [], 'balanced': False, 'rows': [['1500', 0, 1], ['4000', 250, 0], ['7200', 5000, 0]], 'totals': [5250, 1]}Passed
control 2{'abnormal': [], 'balanced': False, 'rows': [['101', 1, 0], ['7200', 650, 0]], 'totals': [651, 0]}{'abnormal': [], 'balanced': False, 'rows': [['101', 1, 0], ['7200', 650, 0]], 'totals': [651, 0]}Passed
control 3{'abnormal': [], 'balanced': True, 'rows': [], 'totals': [0, 0]}{'abnormal': [], 'balanced': True, 'rows': [], 'totals': [0, 0]}Passed
control 4{'abnormal': [], 'balanced': False, 'rows': [['101', 0, 5000], ['205', 0, 1], ['1010', 0, 1234], ['2100', 0, 1], ['4000', 334, 0]], 'totals': [334, 6236]}{'abnormal': [], 'balanced': False, 'rows': [['101', 0, 5000], ['205', 0, 1], ['1010', 0, 1234], ['2100', 0, 1], ['4000', 334, 0]], 'totals': [334, 6236]}Passed
control 5{'abnormal': [], 'balanced': False, 'rows': [['6', 5000, 0], ['99', 100, 0], ['150', 0, 4100], ['4000', 1, 0], ['7200', 0, 1]], 'totals': [5101, 4101]}{'abnormal': [], 'balanced': False, 'rows': [['6', 5000, 0], ['99', 100, 0], ['150', 0, 4100], ['4000', 1, 0], ['7200', 0, 1]], 'totals': [5101, 4101]}Passed
control 6{'abnormal': [], 'balanced': False, 'rows': [['6', 1, 0], ['1500', 0, 5000], ['2100', 0, 1]], 'totals': [1, 5001]}{'abnormal': [], 'balanced': False, 'rows': [['6', 1, 0], ['1500', 0, 5000], ['2100', 0, 1]], 'totals': [1, 5001]}Passed

SHA-256 / f394e6c87aee44533714d2475bda68da852ff814a96b34581be8b9b74bd805b8

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 7 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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Verification & scope

A deterministic bounded teaching model with stipulated toy bookkeeping rules stated in the contract; amounts are integer cents; it makes no claim of conformance to any accounting standard or product. 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 2026-09-29T14:46:21.305539+00:00.

Case digest / 9f1afd8824adc550abdcbaca7692e394a51a5acc5eb2902ea3454de056f63f3d