FAILURE MAP
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FA-59286 / Payroll withholding rules / Open access

Employment tax deposit due date: December rollover · case 01

December monthly deposits are dated January of the same year.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The month wraps from December to January without advancing the year.

THE FAILURE

The month wraps from December to January without advancing the year.

Unsuccessful approach: The attempt handles December explicitly but still keeps the old year.

Case contract

Input {pay_date, lookback_total, accumulated}. If accumulated liability is 100,000.00 or more, deposit the next weekday after pay date. Else if lookback liability is 50,000.00 or less, deposit monthly by the 15th of the following month. Else semiweekly: Wed/Thu/Fri paydays deposit the following Wednesday; Sat-Tue paydays the following Friday. Return [schedule, ISO due date].

Why this case matters

Deposit schedules depend on lookback liability and weekday of payment; a wrong due date means penalties.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(x):
    d = datetime.date(*x['pay_date'])
    if x['accumulated'] >= 10000000:
        nd = d + datetime.timedelta(days=1)
        while nd.weekday() >= 5:
            nd += datetime.timedelta(days=1)
        return ['next-day', nd.isoformat()]
    if x['lookback_total'] <= 5000000:
        y, m = (d.year, d.month % 12 + 1)
        return ['monthly', datetime.date(y, m, 15).isoformat()]
    wd = d.weekday()
    if wd in (2, 3, 4):
        delta = (2 - wd) % 7 or 7
    else:
        delta = (4 - wd) % 7
    return ['semiweekly', (d + datetime.timedelta(days=delta)).isoformat()]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 19], 'lookback_total': 5000000, 'accumulated': 7856645}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 24], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-08-15']), ('normal control', {'pay_date': [2025, 7, 23], 'lookback_total': 4030366, 'accumulated': 7676976}, ['monthly', '2025-08-15']), ('normal control', {'pay_date': [2026, 1, 10], 'lookback_total': 8559426, 'accumulated': 10000000}, ['next-day', '2026-01-12']), ('normal control', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15'])], [('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2025, 12, 19], 'lookback_total': 5000000, 'accumulated': 7856645}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 10, 17], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-22']), ('normal control', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('normal control', {'pay_date': [2025, 7, 6], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-07']), ('normal control', {'pay_date': [2026, 1, 1], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-02'])], [('regression', {'pay_date': [2025, 12, 19], 'lookback_total': 5000000, 'accumulated': 7856645}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 17], 'lookback_total': 5000000, 'accumulated': 3795275}, ['monthly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 12], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-14']), ('normal control', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('normal control', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15'])], [('regression', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 7], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 18], 'lookback_total': 5000000, 'accumulated': 29188}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 1, 28], 'lookback_total': 5000001, 'accumulated': 2892395}, ['semiweekly', '2025-01-31']), ('normal control', {'pay_date': [2026, 1, 11], 'lookback_total': 5000000, 'accumulated': 106033}, ['monthly', '2026-02-15']), ('normal control', {'pay_date': [2025, 1, 9], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-01-15']), ('normal control', {'pay_date': [2024, 12, 8], 'lookback_total': 7946403, 'accumulated': 200852}, ['semiweekly', '2024-12-13'])], [('regression', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2024, 12, 17], 'lookback_total': 5000000, 'accumulated': 3795275}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 31], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 13], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('normal control', {'pay_date': [2025, 9, 18], 'lookback_total': 5000001, 'accumulated': 171509}, ['semiweekly', '2025-09-24']), ('normal control', {'pay_date': [2024, 12, 27], 'lookback_total': 14968828, 'accumulated': 48129}, ['semiweekly', '2025-01-01']), ('normal control', {'pay_date': [2025, 7, 15], 'lookback_total': 7386577, 'accumulated': 7242712}, ['semiweekly', '2025-07-18']), ('normal control', {'pay_date': [2025, 6, 8], 'lookback_total': 5000001, 'accumulated': 136142}, ['semiweekly', '2025-06-13'])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (label, i), solve(args), 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 0['monthly', '2024-01-15']['monthly', '2025-01-15']Failed
regression 1['monthly', '2025-01-15']['monthly', '2026-01-15']Failed
partial-repair probe 2['monthly', '2025-01-15']['monthly', '2026-01-15']Failed
partial-repair probe 3['monthly', '2024-01-15']['monthly', '2025-01-15']Failed
normal control 4['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
normal control 5['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
normal control 6['next-day', '2026-01-12']['next-day', '2026-01-12']Passed
normal control 7['monthly', '2025-12-15']['monthly', '2025-12-15']Passed

SHA-256 / 4109d985ffb6554357d348d8e1a297db3145fb14d137c7810fb6e289e8d46fc9

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(x):
    d = datetime.date(*x['pay_date'])
    if x['accumulated'] >= 10000000:
        nd = d + datetime.timedelta(days=1)
        while nd.weekday() >= 5:
            nd += datetime.timedelta(days=1)
        return ['next-day', nd.isoformat()]
    if x['lookback_total'] <= 5000000:
        y, m = (d.year, 1) if d.month == 12 else (d.year, d.month + 1)
        return ['monthly', datetime.date(y, m, 15).isoformat()]
    wd = d.weekday()
    if wd in (2, 3, 4):
        delta = (2 - wd) % 7 or 7
    else:
        delta = (4 - wd) % 7
    return ['semiweekly', (d + datetime.timedelta(days=delta)).isoformat()]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 19], 'lookback_total': 5000000, 'accumulated': 7856645}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 24], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-08-15']), ('normal control', {'pay_date': [2025, 7, 23], 'lookback_total': 4030366, 'accumulated': 7676976}, ['monthly', '2025-08-15']), ('normal control', {'pay_date': [2026, 1, 10], 'lookback_total': 8559426, 'accumulated': 10000000}, ['next-day', '2026-01-12']), ('normal control', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15'])], [('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2025, 12, 19], 'lookback_total': 5000000, 'accumulated': 7856645}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 10, 17], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-22']), ('normal control', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('normal control', {'pay_date': [2025, 7, 6], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-07']), ('normal control', {'pay_date': [2026, 1, 1], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-02'])], [('regression', {'pay_date': [2025, 12, 19], 'lookback_total': 5000000, 'accumulated': 7856645}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 17], 'lookback_total': 5000000, 'accumulated': 3795275}, ['monthly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 12], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-14']), ('normal control', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('normal control', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15'])], [('regression', {'pay_date': [2024, 12, 2], 'lookback_total': 5000000, 'accumulated': 127118}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 7], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('partial-repair probe', {'pay_date': [2025, 12, 18], 'lookback_total': 5000000, 'accumulated': 29188}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 1, 28], 'lookback_total': 5000001, 'accumulated': 2892395}, ['semiweekly', '2025-01-31']), ('normal control', {'pay_date': [2026, 1, 11], 'lookback_total': 5000000, 'accumulated': 106033}, ['monthly', '2026-02-15']), ('normal control', {'pay_date': [2025, 1, 9], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-01-15']), ('normal control', {'pay_date': [2024, 12, 8], 'lookback_total': 7946403, 'accumulated': 200852}, ['semiweekly', '2024-12-13'])], [('regression', {'pay_date': [2025, 12, 14], 'lookback_total': 5000000, 'accumulated': 73540}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2024, 12, 17], 'lookback_total': 5000000, 'accumulated': 3795275}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 31], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2024, 12, 13], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('normal control', {'pay_date': [2025, 9, 18], 'lookback_total': 5000001, 'accumulated': 171509}, ['semiweekly', '2025-09-24']), ('normal control', {'pay_date': [2024, 12, 27], 'lookback_total': 14968828, 'accumulated': 48129}, ['semiweekly', '2025-01-01']), ('normal control', {'pay_date': [2025, 7, 15], 'lookback_total': 7386577, 'accumulated': 7242712}, ['semiweekly', '2025-07-18']), ('normal control', {'pay_date': [2025, 6, 8], 'lookback_total': 5000001, 'accumulated': 136142}, ['semiweekly', '2025-06-13'])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (label, i), solve(args), 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 0['monthly', '2024-01-15']['monthly', '2025-01-15']Failed
regression 1['monthly', '2025-01-15']['monthly', '2026-01-15']Failed
partial-repair probe 2['monthly', '2025-01-15']['monthly', '2026-01-15']Failed
partial-repair probe 3['monthly', '2024-01-15']['monthly', '2025-01-15']Failed
normal control 4['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
normal control 5['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
normal control 6['next-day', '2026-01-12']['next-day', '2026-01-12']Passed
normal control 7['monthly', '2025-12-15']['monthly', '2025-12-15']Passed

SHA-256 / dc15920a6ffc60d356a1ad190183d8969acd80b83a9a6891da020bdc31333304

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 8 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 teaching model of a stipulated payroll rule with toy thresholds and rates. It makes no claim of conformance to any tax authority, statute or jurisdiction and is not payroll software. 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 2026-09-29T14:46:34.820476+00:00.

Case digest / 138a21e00bcaed672445c80420cbe6e2e74372d13f54913ea78148a3375093ba