FA-58971 / Payroll withholding rules / Open access
Tip credit and minimum-wage makeup: tip shortfall makeup · case 01
Slow weeks with few tips leave the employee below minimum wage with no makeup pay.
ROOT CAUSE
The full tip credit is claimed regardless of the tips actually received.
VERIFIED REPAIR
Restore the contract rule at the tip shortfall makeup step: use `credit_total = min(x['tips'], h * credit_rate)`.
Unsuccessful approach: The attempt caps by tips but only counts straight-time hours when computing the credit claimed.
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 = 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': 58, 'cash_rate': 213, 'tips': 42999, 'min_wage': 1200, 'tip_credit_max': 800}, [37401, 3401]), ('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('partial-repair probe', {'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]), ('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': 57, 'cash_rate': 213, 'tips': 42636, 'min_wage': 1000, 'tip_credit_max': 800}, [22864, 2223]), ('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('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': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0]), ('normal control', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 43, 'cash_rate': 1600, 'tips': 32479, 'min_wage': 1600, 'tip_credit_max': 300}, [71200, 0])], [('regression', {'hours': 52, 'cash_rate': 300, 'tips': 22429, 'min_wage': 1200, 'tip_credit_max': 512}, [47171, 4195]), ('regression', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('partial-repair probe', {'hours': 57, 'cash_rate': 213, 'tips': 25819, 'min_wage': 1600, 'tip_credit_max': 300}, [87700, 0]), ('partial-repair probe', {'hours': 58, 'cash_rate': 213, 'tips': 42999, 'min_wage': 1200, 'tip_credit_max': 800}, [37401, 3401]), ('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]), ('normal control', {'hours': 8, 'cash_rate': 726, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 300}, [5808, 0])], [('regression', {'hours': 59, 'cash_rate': 500, 'tips': 29022, 'min_wage': 1200, 'tip_credit_max': 512}, [53178, 1186]), ('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 44, 'cash_rate': 300, 'tips': 32620, 'min_wage': 1600, 'tip_credit_max': 512}, [51072, 0]), ('partial-repair probe', {'hours': 57, 'cash_rate': 300, 'tips': 42507, 'min_wage': 726, 'tip_credit_max': 512}, [23271, 0]), ('normal control', {'hours': 23, 'cash_rate': 1200, 'tips': 3995, 'min_wage': 1200, 'tip_credit_max': 800}, [27600, 0]), ('normal control', {'hours': 20, 'cash_rate': 300, 'tips': 26661, 'min_wage': 726, 'tip_credit_max': 800}, [6000, 0]), ('normal control', {'hours': 23, 'cash_rate': 300, 'tips': 12650, 'min_wage': 1000, 'tip_credit_max': 300}, [16100, 0]), ('normal control', {'hours': 37, 'cash_rate': 1050, 'tips': 1780, 'min_wage': 1000, 'tip_credit_max': 300}, [37000, 0])], [('regression', {'hours': 56, 'cash_rate': 500, 'tips': 22410, 'min_wage': 1200, 'tip_credit_max': 512}, [54390, 6262]), ('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 47, 'cash_rate': 213, 'tips': 39095, 'min_wage': 1000, 'tip_credit_max': 800}, [13511, 0]), ('partial-repair probe', {'hours': 57, 'cash_rate': 213, 'tips': 42636, 'min_wage': 1000, 'tip_credit_max': 800}, [22864, 2223]), ('normal control', {'hours': 19, 'cash_rate': 1000, 'tips': 592, 'min_wage': 1000, 'tip_credit_max': 512}, [19000, 0]), ('normal control', {'hours': 59, 'cash_rate': 1600, 'tips': 1070, 'min_wage': 1600, 'tip_credit_max': 300}, [109600, 0]), ('normal control', {'hours': 51, 'cash_rate': 726, 'tips': 596, 'min_wage': 726, 'tip_credit_max': 800}, [41019, 0]), ('normal control', {'hours': 36, 'cash_rate': 1200, 'tips': 4642, 'min_wage': 1200, 'tip_credit_max': 300}, [43200, 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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [34000, 0] | [37401, 3401] | Failed |
| regression 1 | [35200, 0] | [68800, 33600] | Failed |
| partial-repair probe 2 | [63000, 0] | [63000, 0] | Passed |
| partial-repair probe 3 | [54000, 0] | [54000, 0] | 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 / c66fa2ffc301e1bfd2f4a671b31005d1e7d5c00e0f635bd922442d8860090e39
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'], reg * 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': 58, 'cash_rate': 213, 'tips': 42999, 'min_wage': 1200, 'tip_credit_max': 800}, [37401, 3401]), ('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('partial-repair probe', {'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]), ('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': 57, 'cash_rate': 213, 'tips': 42636, 'min_wage': 1000, 'tip_credit_max': 800}, [22864, 2223]), ('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('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': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0]), ('normal control', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 43, 'cash_rate': 1600, 'tips': 32479, 'min_wage': 1600, 'tip_credit_max': 300}, [71200, 0])], [('regression', {'hours': 52, 'cash_rate': 300, 'tips': 22429, 'min_wage': 1200, 'tip_credit_max': 512}, [47171, 4195]), ('regression', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('partial-repair probe', {'hours': 57, 'cash_rate': 213, 'tips': 25819, 'min_wage': 1600, 'tip_credit_max': 300}, [87700, 0]), ('partial-repair probe', {'hours': 58, 'cash_rate': 213, 'tips': 42999, 'min_wage': 1200, 'tip_credit_max': 800}, [37401, 3401]), ('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]), ('normal control', {'hours': 8, 'cash_rate': 726, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 300}, [5808, 0])], [('regression', {'hours': 59, 'cash_rate': 500, 'tips': 29022, 'min_wage': 1200, 'tip_credit_max': 512}, [53178, 1186]), ('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 44, 'cash_rate': 300, 'tips': 32620, 'min_wage': 1600, 'tip_credit_max': 512}, [51072, 0]), ('partial-repair probe', {'hours': 57, 'cash_rate': 300, 'tips': 42507, 'min_wage': 726, 'tip_credit_max': 512}, [23271, 0]), ('normal control', {'hours': 23, 'cash_rate': 1200, 'tips': 3995, 'min_wage': 1200, 'tip_credit_max': 800}, [27600, 0]), ('normal control', {'hours': 20, 'cash_rate': 300, 'tips': 26661, 'min_wage': 726, 'tip_credit_max': 800}, [6000, 0]), ('normal control', {'hours': 23, 'cash_rate': 300, 'tips': 12650, 'min_wage': 1000, 'tip_credit_max': 300}, [16100, 0]), ('normal control', {'hours': 37, 'cash_rate': 1050, 'tips': 1780, 'min_wage': 1000, 'tip_credit_max': 300}, [37000, 0])], [('regression', {'hours': 56, 'cash_rate': 500, 'tips': 22410, 'min_wage': 1200, 'tip_credit_max': 512}, [54390, 6262]), ('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 47, 'cash_rate': 213, 'tips': 39095, 'min_wage': 1000, 'tip_credit_max': 800}, [13511, 0]), ('partial-repair probe', {'hours': 57, 'cash_rate': 213, 'tips': 42636, 'min_wage': 1000, 'tip_credit_max': 800}, [22864, 2223]), ('normal control', {'hours': 19, 'cash_rate': 1000, 'tips': 592, 'min_wage': 1000, 'tip_credit_max': 512}, [19000, 0]), ('normal control', {'hours': 59, 'cash_rate': 1600, 'tips': 1070, 'min_wage': 1600, 'tip_credit_max': 300}, [109600, 0]), ('normal control', {'hours': 51, 'cash_rate': 726, 'tips': 596, 'min_wage': 726, 'tip_credit_max': 800}, [41019, 0]), ('normal control', {'hours': 36, 'cash_rate': 1200, 'tips': 4642, 'min_wage': 1200, 'tip_credit_max': 300}, [43200, 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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [48400, 14400] | [37401, 3401] | Failed |
| regression 1 | [68800, 33600] | [68800, 33600] | Passed |
| partial-repair probe 2 | [68400, 5400] | [63000, 0] | Failed |
| partial-repair probe 3 | [57600, 3600] | [54000, 0] | 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 / 469cb65d55ea89205def90d968def0fd48ba0508976f0d71a577556ca094262e
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': 58, 'cash_rate': 213, 'tips': 42999, 'min_wage': 1200, 'tip_credit_max': 800}, [37401, 3401]), ('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('partial-repair probe', {'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]), ('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': 57, 'cash_rate': 213, 'tips': 42636, 'min_wage': 1000, 'tip_credit_max': 800}, [22864, 2223]), ('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('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': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0]), ('normal control', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 43, 'cash_rate': 1600, 'tips': 32479, 'min_wage': 1600, 'tip_credit_max': 300}, [71200, 0])], [('regression', {'hours': 52, 'cash_rate': 300, 'tips': 22429, 'min_wage': 1200, 'tip_credit_max': 512}, [47171, 4195]), ('regression', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('partial-repair probe', {'hours': 57, 'cash_rate': 213, 'tips': 25819, 'min_wage': 1600, 'tip_credit_max': 300}, [87700, 0]), ('partial-repair probe', {'hours': 58, 'cash_rate': 213, 'tips': 42999, 'min_wage': 1200, 'tip_credit_max': 800}, [37401, 3401]), ('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]), ('normal control', {'hours': 8, 'cash_rate': 726, 'tips': 0, 'min_wage': 726, 'tip_credit_max': 300}, [5808, 0])], [('regression', {'hours': 59, 'cash_rate': 500, 'tips': 29022, 'min_wage': 1200, 'tip_credit_max': 512}, [53178, 1186]), ('regression', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('partial-repair probe', {'hours': 44, 'cash_rate': 300, 'tips': 32620, 'min_wage': 1600, 'tip_credit_max': 512}, [51072, 0]), ('partial-repair probe', {'hours': 57, 'cash_rate': 300, 'tips': 42507, 'min_wage': 726, 'tip_credit_max': 512}, [23271, 0]), ('normal control', {'hours': 23, 'cash_rate': 1200, 'tips': 3995, 'min_wage': 1200, 'tip_credit_max': 800}, [27600, 0]), ('normal control', {'hours': 20, 'cash_rate': 300, 'tips': 26661, 'min_wage': 726, 'tip_credit_max': 800}, [6000, 0]), ('normal control', {'hours': 23, 'cash_rate': 300, 'tips': 12650, 'min_wage': 1000, 'tip_credit_max': 300}, [16100, 0]), ('normal control', {'hours': 37, 'cash_rate': 1050, 'tips': 1780, 'min_wage': 1000, 'tip_credit_max': 300}, [37000, 0])], [('regression', {'hours': 56, 'cash_rate': 500, 'tips': 22410, 'min_wage': 1200, 'tip_credit_max': 512}, [54390, 6262]), ('regression', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('partial-repair probe', {'hours': 47, 'cash_rate': 213, 'tips': 39095, 'min_wage': 1000, 'tip_credit_max': 800}, [13511, 0]), ('partial-repair probe', {'hours': 57, 'cash_rate': 213, 'tips': 42636, 'min_wage': 1000, 'tip_credit_max': 800}, [22864, 2223]), ('normal control', {'hours': 19, 'cash_rate': 1000, 'tips': 592, 'min_wage': 1000, 'tip_credit_max': 512}, [19000, 0]), ('normal control', {'hours': 59, 'cash_rate': 1600, 'tips': 1070, 'min_wage': 1600, 'tip_credit_max': 300}, [109600, 0]), ('normal control', {'hours': 51, 'cash_rate': 726, 'tips': 596, 'min_wage': 726, 'tip_credit_max': 800}, [41019, 0]), ('normal control', {'hours': 36, 'cash_rate': 1200, 'tips': 4642, 'min_wage': 1200, 'tip_credit_max': 300}, [43200, 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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [37401, 3401] | [37401, 3401] | Passed |
| regression 1 | [68800, 33600] | [68800, 33600] | Passed |
| partial-repair probe 2 | [63000, 0] | [63000, 0] | Passed |
| partial-repair probe 3 | [54000, 0] | [54000, 0] | 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 / 6eafaf471c65ba1b37a743e326f33542a8ce69b3fa205c4aafff250c99362c6f
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 2026-09-29T14:46:31.810021+00:00.
Case digest / 8472c28a5206ca74c2bf313917cd632a02cb4206cd24a921e6f299a047e78bee