FAILURE MAP
← Case archive

FA-58966 / Payroll withholding rules / Open access

Tip credit and minimum-wage makeup: overtime tip credit · case 01

Tipped employees working overtime receive less cash than the overtime minimum requires.

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

ROOT CAUSE

The overtime multiplier is applied to the reduced cash wage instead of to the full minimum wage before subtracting the credit.

VERIFIED REPAIR

Restore the contract rule at the overtime tip credit step: use `ot * (mw * 3 // 2 - credit_rate)`.

Unsuccessful approach: The attempt drops the tip credit on overtime hours, overpaying overtime cash wages.

Case contract

Input weekly {hours, cash_rate, tips, min_wage (even cents), tip_credit_max}. Tip credit per hour = max(0, min(min_wage - cash_rate, tip_credit_max)). Hours over 40 are paid at 1.5*min_wage minus the same credit. Credit actually taken = min(tips, hours*credit); the unmet credit is paid as makeup. Return [cash_wages, makeup].

Why this case matters

Tip credits are capped by tips actually received and do not grow on overtime hours.

1 / The failure

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

N = 1
observations = []
def solve(x):
    h = x['hours']
    mw = x['min_wage']
    cash = x['cash_rate']
    reg = min(h, 40)
    ot = max(0, h - 40)
    credit_rate = max(0, min(mw - cash, x['tip_credit_max']))
    credit_total = min(x['tips'], h * credit_rate)
    makeup = h * credit_rate - credit_total
    cash_wages = reg * (mw - credit_rate) + ot * (mw - credit_rate) * 3 // 2 + makeup
    return [cash_wages, makeup]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('partial-repair probe', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('normal control', {'hours': 47, 'cash_rate': 1200, 'tips': 1983, 'min_wage': 1200, 'tip_credit_max': 300}, [60600, 0]), ('normal control', {'hours': 23, 'cash_rate': 776, 'tips': 1403, 'min_wage': 726, 'tip_credit_max': 800}, [16698, 0]), ('normal control', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('normal control', {'hours': 27, 'cash_rate': 726, 'tips': 397, 'min_wage': 726, 'tip_credit_max': 300}, [19602, 0])], [('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('normal control', {'hours': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('normal control', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('normal control', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0])], [('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 46, 'cash_rate': 213, 'tips': 3880, 'min_wage': 1200, 'tip_credit_max': 300}, [54920, 9920]), ('normal control', {'hours': 13, 'cash_rate': 300, 'tips': 3600, 'min_wage': 1600, 'tip_credit_max': 512}, [17200, 3056]), ('normal control', {'hours': 15, 'cash_rate': 300, 'tips': 777, 'min_wage': 726, 'tip_credit_max': 800}, [10113, 5613]), ('normal control', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 12, 'cash_rate': 213, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 512}, [8712, 6144])], [('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 60, 'cash_rate': 300, 'tips': 25281, 'min_wage': 1200, 'tip_credit_max': 300}, [66000, 0]), ('partial-repair probe', {'hours': 53, 'cash_rate': 300, 'tips': 45712, 'min_wage': 1600, 'tip_credit_max': 512}, [68064, 0]), ('normal control', {'hours': 43, 'cash_rate': 1600, 'tips': 32479, 'min_wage': 1600, 'tip_credit_max': 300}, [71200, 0]), ('normal control', {'hours': 36, 'cash_rate': 500, 'tips': 27434, 'min_wage': 1000, 'tip_credit_max': 300}, [25200, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 9, 'cash_rate': 1050, 'tips': 1770, 'min_wage': 1000, 'tip_credit_max': 800}, [9000, 0])], [('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('regression', {'hours': 46, 'cash_rate': 213, 'tips': 3880, 'min_wage': 1200, 'tip_credit_max': 300}, [54920, 9920]), ('partial-repair probe', {'hours': 57, 'cash_rate': 500, 'tips': 4387, 'min_wage': 1000, 'tip_credit_max': 512}, [61113, 24113]), ('partial-repair probe', {'hours': 41, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 512}, [41500, 20992]), ('normal control', {'hours': 8, 'cash_rate': 726, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 300}, [5808, 0]), ('normal control', {'hours': 19, 'cash_rate': 300, 'tips': 13502, 'min_wage': 1200, 'tip_credit_max': 800}, [9298, 1698]), ('normal control', {'hours': 1, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [1600, 800]), ('normal control', {'hours': 23, 'cash_rate': 1200, 'tips': 3995, 'min_wage': 1200, 'tip_credit_max': 800}, [27600, 0])]]
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[68000, 33600][68800, 33600]Failed
regression 1[60300, 0][63000, 0]Failed
partial-repair probe 2[52200, 0][54000, 0]Failed
partial-repair probe 3[57240, 43608][63536, 43608]Failed
normal control 4[60600, 0][60600, 0]Passed
normal control 5[16698, 0][16698, 0]Passed
normal control 6[14448, 0][14448, 0]Passed
normal control 7[19602, 0][19602, 0]Passed

SHA-256 / 55666200ffd3c3578ac2dbb55927080d6d32e9d86ca1d3e5cafc0677d1bd4d11

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(x):
    h = x['hours']
    mw = x['min_wage']
    cash = x['cash_rate']
    reg = min(h, 40)
    ot = max(0, h - 40)
    credit_rate = max(0, min(mw - cash, x['tip_credit_max']))
    credit_total = min(x['tips'], h * credit_rate)
    makeup = h * credit_rate - credit_total
    cash_wages = reg * (mw - credit_rate) + ot * (mw * 3 // 2) + makeup
    return [cash_wages, makeup]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('partial-repair probe', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('normal control', {'hours': 47, 'cash_rate': 1200, 'tips': 1983, 'min_wage': 1200, 'tip_credit_max': 300}, [60600, 0]), ('normal control', {'hours': 23, 'cash_rate': 776, 'tips': 1403, 'min_wage': 726, 'tip_credit_max': 800}, [16698, 0]), ('normal control', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('normal control', {'hours': 27, 'cash_rate': 726, 'tips': 397, 'min_wage': 726, 'tip_credit_max': 300}, [19602, 0])], [('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('normal control', {'hours': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('normal control', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('normal control', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0])], [('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 46, 'cash_rate': 213, 'tips': 3880, 'min_wage': 1200, 'tip_credit_max': 300}, [54920, 9920]), ('normal control', {'hours': 13, 'cash_rate': 300, 'tips': 3600, 'min_wage': 1600, 'tip_credit_max': 512}, [17200, 3056]), ('normal control', {'hours': 15, 'cash_rate': 300, 'tips': 777, 'min_wage': 726, 'tip_credit_max': 800}, [10113, 5613]), ('normal control', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 12, 'cash_rate': 213, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 512}, [8712, 6144])], [('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 60, 'cash_rate': 300, 'tips': 25281, 'min_wage': 1200, 'tip_credit_max': 300}, [66000, 0]), ('partial-repair probe', {'hours': 53, 'cash_rate': 300, 'tips': 45712, 'min_wage': 1600, 'tip_credit_max': 512}, [68064, 0]), ('normal control', {'hours': 43, 'cash_rate': 1600, 'tips': 32479, 'min_wage': 1600, 'tip_credit_max': 300}, [71200, 0]), ('normal control', {'hours': 36, 'cash_rate': 500, 'tips': 27434, 'min_wage': 1000, 'tip_credit_max': 300}, [25200, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 9, 'cash_rate': 1050, 'tips': 1770, 'min_wage': 1000, 'tip_credit_max': 800}, [9000, 0])], [('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('regression', {'hours': 46, 'cash_rate': 213, 'tips': 3880, 'min_wage': 1200, 'tip_credit_max': 300}, [54920, 9920]), ('partial-repair probe', {'hours': 57, 'cash_rate': 500, 'tips': 4387, 'min_wage': 1000, 'tip_credit_max': 512}, [61113, 24113]), ('partial-repair probe', {'hours': 41, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 512}, [41500, 20992]), ('normal control', {'hours': 8, 'cash_rate': 726, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 300}, [5808, 0]), ('normal control', {'hours': 19, 'cash_rate': 300, 'tips': 13502, 'min_wage': 1200, 'tip_credit_max': 800}, [9298, 1698]), ('normal control', {'hours': 1, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [1600, 800]), ('normal control', {'hours': 23, 'cash_rate': 1200, 'tips': 3995, 'min_wage': 1200, 'tip_credit_max': 800}, [27600, 0])]]
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[70400, 33600][68800, 33600]Failed
regression 1[68400, 0][63000, 0]Failed
partial-repair probe 2[57600, 0][54000, 0]Failed
partial-repair probe 3[76128, 43608][63536, 43608]Failed
normal control 4[60600, 0][60600, 0]Passed
normal control 5[16698, 0][16698, 0]Passed
normal control 6[14448, 0][14448, 0]Passed
normal control 7[19602, 0][19602, 0]Passed

SHA-256 / 0c5dbdf576a1174ed8d277f23a6b5143dfe47ec5f6d9764b0d337c21f6bfb4df

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    h = x['hours']
    mw = x['min_wage']
    cash = x['cash_rate']
    reg = min(h, 40)
    ot = max(0, h - 40)
    credit_rate = max(0, min(mw - cash, x['tip_credit_max']))
    credit_total = min(x['tips'], h * credit_rate)
    makeup = h * credit_rate - credit_total
    cash_wages = reg * (mw - credit_rate) + ot * (mw * 3 // 2 - credit_rate) + makeup
    return [cash_wages, makeup]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('partial-repair probe', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('normal control', {'hours': 47, 'cash_rate': 1200, 'tips': 1983, 'min_wage': 1200, 'tip_credit_max': 300}, [60600, 0]), ('normal control', {'hours': 23, 'cash_rate': 776, 'tips': 1403, 'min_wage': 726, 'tip_credit_max': 800}, [16698, 0]), ('normal control', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('normal control', {'hours': 27, 'cash_rate': 726, 'tips': 397, 'min_wage': 726, 'tip_credit_max': 300}, [19602, 0])], [('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('normal control', {'hours': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('normal control', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('normal control', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0])], [('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 46, 'cash_rate': 213, 'tips': 3880, 'min_wage': 1200, 'tip_credit_max': 300}, [54920, 9920]), ('normal control', {'hours': 13, 'cash_rate': 300, 'tips': 3600, 'min_wage': 1600, 'tip_credit_max': 512}, [17200, 3056]), ('normal control', {'hours': 15, 'cash_rate': 300, 'tips': 777, 'min_wage': 726, 'tip_credit_max': 800}, [10113, 5613]), ('normal control', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 12, 'cash_rate': 213, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 512}, [8712, 6144])], [('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 60, 'cash_rate': 300, 'tips': 25281, 'min_wage': 1200, 'tip_credit_max': 300}, [66000, 0]), ('partial-repair probe', {'hours': 53, 'cash_rate': 300, 'tips': 45712, 'min_wage': 1600, 'tip_credit_max': 512}, [68064, 0]), ('normal control', {'hours': 43, 'cash_rate': 1600, 'tips': 32479, 'min_wage': 1600, 'tip_credit_max': 300}, [71200, 0]), ('normal control', {'hours': 36, 'cash_rate': 500, 'tips': 27434, 'min_wage': 1000, 'tip_credit_max': 300}, [25200, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 9, 'cash_rate': 1050, 'tips': 1770, 'min_wage': 1000, 'tip_credit_max': 800}, [9000, 0])], [('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('regression', {'hours': 46, 'cash_rate': 213, 'tips': 3880, 'min_wage': 1200, 'tip_credit_max': 300}, [54920, 9920]), ('partial-repair probe', {'hours': 57, 'cash_rate': 500, 'tips': 4387, 'min_wage': 1000, 'tip_credit_max': 512}, [61113, 24113]), ('partial-repair probe', {'hours': 41, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 512}, [41500, 20992]), ('normal control', {'hours': 8, 'cash_rate': 726, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 300}, [5808, 0]), ('normal control', {'hours': 19, 'cash_rate': 300, 'tips': 13502, 'min_wage': 1200, 'tip_credit_max': 800}, [9298, 1698]), ('normal control', {'hours': 1, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [1600, 800]), ('normal control', {'hours': 23, 'cash_rate': 1200, 'tips': 3995, 'min_wage': 1200, 'tip_credit_max': 800}, [27600, 0])]]
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[68800, 33600][68800, 33600]Passed
regression 1[63000, 0][63000, 0]Passed
partial-repair probe 2[54000, 0][54000, 0]Passed
partial-repair probe 3[63536, 43608][63536, 43608]Passed
normal control 4[60600, 0][60600, 0]Passed
normal control 5[16698, 0][16698, 0]Passed
normal control 6[14448, 0][14448, 0]Passed
normal control 7[19602, 0][19602, 0]Passed

SHA-256 / 46888ce89964ca0da905456785c4a9cb2f47c53ea22b497ac490ed83532d19bb

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 2026-09-29T14:46:31.779479+00:00.

Case digest / bb60587aa4f6de19902f58bf6ff61a2ff666ce8c0441921842eababccbe52e54