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FA-59166 / Payroll withholding rules / Open access

Applicable minimum wage and youth rate: most protective rate · case 01

Employees in cities with a minimum wage below the state rate are underpaid.

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

ROOT CAUSE

The most specific jurisdiction wins instead of the highest rate.

VERIFIED REPAIR

Restore the contract rule at the most protective rate step: use `applicable = max(rates)`.

Unsuccessful approach: The attempt takes the maximum but ignores local minimum wages.

Case contract

Input {federal, state|None, local|None, age, days_employed, hours, paid}. Applicable rate is the highest of the defined federal, state and local minimums. A youth rate of 4.25 may replace it when age < 20, days_employed <= 90 and neither a state nor local minimum is defined. Return [rate, max(0, hours*rate - paid)].

Why this case matters

Payroll must pay the most protective minimum wage, and the youth rate exception is narrow.

1 / The failure

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

N = 1
observations = []
def solve(x):
    rates = [x['federal']] + [r for r in (x['state'], x['local']) if r is not None]
    applicable = rates[-1]
    if x['age'] < 20 and x['days_employed'] <= 90 and x['state'] is None and x['local'] is None:
        applicable = min(applicable, 425)
    return [applicable, max(0, x['hours'] * applicable - x['paid'])]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'federal': 725, 'state': 1500, 'local': 900, 'age': 19, 'days_employed': 200, 'hours': 32, 'paid': 60214}, [1500, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 17, 'days_employed': 200, 'hours': 12, 'paid': 62529}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 21, 'days_employed': 10, 'hours': 36, 'paid': 32014}, [900, 386]), ('partial-repair probe', {'federal': 725, 'state': 700, 'local': 900, 'age': 17, 'days_employed': 90, 'hours': 42, 'paid': 43650}, [900, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 17, 'days_employed': 10, 'hours': 44, 'paid': 52557}, [1500, 13443]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 19, 'days_employed': 200, 'hours': 49, 'paid': 66535}, [725, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 91, 'hours': 13, 'paid': 52409}, [1500, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 91, 'hours': 3, 'paid': 73893}, [1500, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 17, 'days_employed': 200, 'hours': 12, 'paid': 62529}, [1500, 0]), ('regression', {'federal': 725, 'state': 700, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 55516}, [725, 0]), ('partial-repair probe', {'federal': 725, 'state': 700, 'local': 1400, 'age': 20, 'days_employed': 119, 'hours': 50, 'paid': 49362}, [1400, 20638]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 1700, 'age': 21, 'days_employed': 200, 'hours': 31, 'paid': 26649}, [1700, 26051]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 35, 'days_employed': 91, 'hours': 1, 'paid': 31574}, [1500, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 89, 'hours': 20, 'paid': 19880}, [1500, 10120]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 100, 'hours': 21, 'paid': 76399}, [1500, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 20, 'days_employed': 91, 'hours': 50, 'paid': 25834}, [725, 10416])], [('regression', {'federal': 725, 'state': 700, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 55516}, [725, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 19, 'days_employed': 90, 'hours': 35, 'paid': 10142}, [1500, 42358]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 35, 'days_employed': 10, 'hours': 44, 'paid': 29457}, [900, 10143]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 1400, 'age': 21, 'days_employed': 89, 'hours': 42, 'paid': 70854}, [1400, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 12908}, [800, 7092]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 90, 'hours': 7, 'paid': 37819}, [1500, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 21, 'days_employed': 200, 'hours': 28, 'paid': 34186}, [800, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 119, 'hours': 21, 'paid': 78346}, [1500, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 19, 'days_employed': 90, 'hours': 35, 'paid': 10142}, [1500, 42358]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 21, 'days_employed': 100, 'hours': 38, 'paid': 59758}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 900, 'age': 35, 'days_employed': 10, 'hours': 27, 'paid': 8188}, [900, 16112]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 17, 'days_employed': 200, 'hours': 2, 'paid': 39319}, [900, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 21, 'days_employed': 90, 'hours': 39, 'paid': 35117}, [800, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 19, 'days_employed': 90, 'hours': 23, 'paid': 23235}, [425, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 100, 'hours': 13, 'paid': 71642}, [1500, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 21, 'days_employed': 200, 'hours': 0, 'paid': 36583}, [725, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 21, 'days_employed': 100, 'hours': 38, 'paid': 59758}, [1500, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 20, 'days_employed': 89, 'hours': 13, 'paid': 20235}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 1700, 'age': 19, 'days_employed': 90, 'hours': 1, 'paid': 33812}, [1700, 0]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 1700, 'age': 17, 'days_employed': 119, 'hours': 36, 'paid': 42057}, [1700, 19143]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 119, 'hours': 16, 'paid': 1656}, [1500, 22344]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 26, 'paid': 4444}, [1500, 34556]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 89, 'hours': 38, 'paid': 44401}, [1500, 12599]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 20, 'days_employed': 200, 'hours': 12, 'paid': 33515}, [800, 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[900, 0][1500, 0]Failed
regression 1[1400, 0][1500, 0]Failed
partial-repair probe 2[900, 386][900, 386]Passed
partial-repair probe 3[900, 0][900, 0]Passed
normal control 4[1500, 13443][1500, 13443]Passed
normal control 5[725, 0][725, 0]Passed
normal control 6[1500, 0][1500, 0]Passed
normal control 7[1500, 0][1500, 0]Passed

SHA-256 / 12bea8512453b48bdc6f759d15323e8988bf1cb7a1f37e464b253cde73fcc79a

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(x):
    rates = [x['federal']] + [r for r in (x['state'], x['local']) if r is not None]
    applicable = max(x['federal'], x['state'] or 0)
    if x['age'] < 20 and x['days_employed'] <= 90 and x['state'] is None and x['local'] is None:
        applicable = min(applicable, 425)
    return [applicable, max(0, x['hours'] * applicable - x['paid'])]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'federal': 725, 'state': 1500, 'local': 900, 'age': 19, 'days_employed': 200, 'hours': 32, 'paid': 60214}, [1500, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 17, 'days_employed': 200, 'hours': 12, 'paid': 62529}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 21, 'days_employed': 10, 'hours': 36, 'paid': 32014}, [900, 386]), ('partial-repair probe', {'federal': 725, 'state': 700, 'local': 900, 'age': 17, 'days_employed': 90, 'hours': 42, 'paid': 43650}, [900, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 17, 'days_employed': 10, 'hours': 44, 'paid': 52557}, [1500, 13443]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 19, 'days_employed': 200, 'hours': 49, 'paid': 66535}, [725, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 91, 'hours': 13, 'paid': 52409}, [1500, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 91, 'hours': 3, 'paid': 73893}, [1500, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 17, 'days_employed': 200, 'hours': 12, 'paid': 62529}, [1500, 0]), ('regression', {'federal': 725, 'state': 700, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 55516}, [725, 0]), ('partial-repair probe', {'federal': 725, 'state': 700, 'local': 1400, 'age': 20, 'days_employed': 119, 'hours': 50, 'paid': 49362}, [1400, 20638]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 1700, 'age': 21, 'days_employed': 200, 'hours': 31, 'paid': 26649}, [1700, 26051]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 35, 'days_employed': 91, 'hours': 1, 'paid': 31574}, [1500, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 89, 'hours': 20, 'paid': 19880}, [1500, 10120]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 100, 'hours': 21, 'paid': 76399}, [1500, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 20, 'days_employed': 91, 'hours': 50, 'paid': 25834}, [725, 10416])], [('regression', {'federal': 725, 'state': 700, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 55516}, [725, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 19, 'days_employed': 90, 'hours': 35, 'paid': 10142}, [1500, 42358]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 35, 'days_employed': 10, 'hours': 44, 'paid': 29457}, [900, 10143]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 1400, 'age': 21, 'days_employed': 89, 'hours': 42, 'paid': 70854}, [1400, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 12908}, [800, 7092]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 90, 'hours': 7, 'paid': 37819}, [1500, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 21, 'days_employed': 200, 'hours': 28, 'paid': 34186}, [800, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 119, 'hours': 21, 'paid': 78346}, [1500, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 19, 'days_employed': 90, 'hours': 35, 'paid': 10142}, [1500, 42358]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 21, 'days_employed': 100, 'hours': 38, 'paid': 59758}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 900, 'age': 35, 'days_employed': 10, 'hours': 27, 'paid': 8188}, [900, 16112]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 17, 'days_employed': 200, 'hours': 2, 'paid': 39319}, [900, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 21, 'days_employed': 90, 'hours': 39, 'paid': 35117}, [800, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 19, 'days_employed': 90, 'hours': 23, 'paid': 23235}, [425, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 100, 'hours': 13, 'paid': 71642}, [1500, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 21, 'days_employed': 200, 'hours': 0, 'paid': 36583}, [725, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 21, 'days_employed': 100, 'hours': 38, 'paid': 59758}, [1500, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 20, 'days_employed': 89, 'hours': 13, 'paid': 20235}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 1700, 'age': 19, 'days_employed': 90, 'hours': 1, 'paid': 33812}, [1700, 0]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 1700, 'age': 17, 'days_employed': 119, 'hours': 36, 'paid': 42057}, [1700, 19143]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 119, 'hours': 16, 'paid': 1656}, [1500, 22344]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 26, 'paid': 4444}, [1500, 34556]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 89, 'hours': 38, 'paid': 44401}, [1500, 12599]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 20, 'days_employed': 200, 'hours': 12, 'paid': 33515}, [800, 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[1500, 0][1500, 0]Passed
regression 1[1500, 0][1500, 0]Passed
partial-repair probe 2[725, 0][900, 386]Failed
partial-repair probe 3[725, 0][900, 0]Failed
normal control 4[1500, 13443][1500, 13443]Passed
normal control 5[725, 0][725, 0]Passed
normal control 6[1500, 0][1500, 0]Passed
normal control 7[1500, 0][1500, 0]Passed

SHA-256 / 7b670d711fbc5d936b1d2e7962478253fcc800a0d0bcc1e5d885b450bb6c3764

3 / The verified repair

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

N = 1
observations = []
def solve(x):
    rates = [x['federal']] + [r for r in (x['state'], x['local']) if r is not None]
    applicable = max(rates)
    if x['age'] < 20 and x['days_employed'] <= 90 and x['state'] is None and x['local'] is None:
        applicable = min(applicable, 425)
    return [applicable, max(0, x['hours'] * applicable - x['paid'])]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'federal': 725, 'state': 1500, 'local': 900, 'age': 19, 'days_employed': 200, 'hours': 32, 'paid': 60214}, [1500, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 17, 'days_employed': 200, 'hours': 12, 'paid': 62529}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 21, 'days_employed': 10, 'hours': 36, 'paid': 32014}, [900, 386]), ('partial-repair probe', {'federal': 725, 'state': 700, 'local': 900, 'age': 17, 'days_employed': 90, 'hours': 42, 'paid': 43650}, [900, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 17, 'days_employed': 10, 'hours': 44, 'paid': 52557}, [1500, 13443]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 19, 'days_employed': 200, 'hours': 49, 'paid': 66535}, [725, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 91, 'hours': 13, 'paid': 52409}, [1500, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 91, 'hours': 3, 'paid': 73893}, [1500, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 17, 'days_employed': 200, 'hours': 12, 'paid': 62529}, [1500, 0]), ('regression', {'federal': 725, 'state': 700, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 55516}, [725, 0]), ('partial-repair probe', {'federal': 725, 'state': 700, 'local': 1400, 'age': 20, 'days_employed': 119, 'hours': 50, 'paid': 49362}, [1400, 20638]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 1700, 'age': 21, 'days_employed': 200, 'hours': 31, 'paid': 26649}, [1700, 26051]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 35, 'days_employed': 91, 'hours': 1, 'paid': 31574}, [1500, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 89, 'hours': 20, 'paid': 19880}, [1500, 10120]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 100, 'hours': 21, 'paid': 76399}, [1500, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 20, 'days_employed': 91, 'hours': 50, 'paid': 25834}, [725, 10416])], [('regression', {'federal': 725, 'state': 700, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 55516}, [725, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 19, 'days_employed': 90, 'hours': 35, 'paid': 10142}, [1500, 42358]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 35, 'days_employed': 10, 'hours': 44, 'paid': 29457}, [900, 10143]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 1400, 'age': 21, 'days_employed': 89, 'hours': 42, 'paid': 70854}, [1400, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 25, 'paid': 12908}, [800, 7092]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 21, 'days_employed': 90, 'hours': 7, 'paid': 37819}, [1500, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 21, 'days_employed': 200, 'hours': 28, 'paid': 34186}, [800, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 119, 'hours': 21, 'paid': 78346}, [1500, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 19, 'days_employed': 90, 'hours': 35, 'paid': 10142}, [1500, 42358]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 21, 'days_employed': 100, 'hours': 38, 'paid': 59758}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 900, 'age': 35, 'days_employed': 10, 'hours': 27, 'paid': 8188}, [900, 16112]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 900, 'age': 17, 'days_employed': 200, 'hours': 2, 'paid': 39319}, [900, 0]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 21, 'days_employed': 90, 'hours': 39, 'paid': 35117}, [800, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 19, 'days_employed': 90, 'hours': 23, 'paid': 23235}, [425, 0]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 19, 'days_employed': 100, 'hours': 13, 'paid': 71642}, [1500, 0]), ('normal control', {'federal': 725, 'state': None, 'local': None, 'age': 21, 'days_employed': 200, 'hours': 0, 'paid': 36583}, [725, 0])], [('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 21, 'days_employed': 100, 'hours': 38, 'paid': 59758}, [1500, 0]), ('regression', {'federal': 725, 'state': 1500, 'local': 1400, 'age': 20, 'days_employed': 89, 'hours': 13, 'paid': 20235}, [1500, 0]), ('partial-repair probe', {'federal': 725, 'state': None, 'local': 1700, 'age': 19, 'days_employed': 90, 'hours': 1, 'paid': 33812}, [1700, 0]), ('partial-repair probe', {'federal': 725, 'state': 800, 'local': 1700, 'age': 17, 'days_employed': 119, 'hours': 36, 'paid': 42057}, [1700, 19143]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 119, 'hours': 16, 'paid': 1656}, [1500, 22344]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 35, 'days_employed': 200, 'hours': 26, 'paid': 4444}, [1500, 34556]), ('normal control', {'federal': 725, 'state': 1500, 'local': None, 'age': 20, 'days_employed': 89, 'hours': 38, 'paid': 44401}, [1500, 12599]), ('normal control', {'federal': 725, 'state': 800, 'local': None, 'age': 20, 'days_employed': 200, 'hours': 12, 'paid': 33515}, [800, 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[1500, 0][1500, 0]Passed
regression 1[1500, 0][1500, 0]Passed
partial-repair probe 2[900, 386][900, 386]Passed
partial-repair probe 3[900, 0][900, 0]Passed
normal control 4[1500, 13443][1500, 13443]Passed
normal control 5[725, 0][725, 0]Passed
normal control 6[1500, 0][1500, 0]Passed
normal control 7[1500, 0][1500, 0]Passed

SHA-256 / 414f76dcfb8955767bfb4b9e0c16069c6f44e44915d55638d34ac7271408a834

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 2026-09-29T14:46:33.622958+00:00.

Case digest / c678b649ad4fe462d3e1f448e5c2f4316ce01b41b6397860a4ae85de59bb8deb