FA-59306 / Payroll withholding rules / Open access

Tip shortfall allocation by hours: hours share base · case 01

The wrong employees are selected for allocation because shares are too small.

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

ROOT CAUSE

Shares are computed from the shortfall rather than the full 8% target.

VERIFIED REPAIR

Restore the contract rule at the hours share base step: use `share = target * hrs / hours_total`.

Unsuccessful approach: The attempt splits the target equally, ignoring hours worked.

Case contract

Input {receipts, staff: [[name, hours, reported_tips]]} with positive total hours. Target = 8% of receipts. Shortfall = target - total reported; none -> {}. Each employee's share = target*hours/total_hours; employees reporting less than their share get deficit = share - reported. Shortfall is allocated in proportion to deficits, each rounded half-up. Return {name: cents}.

Why this case matters

Tip allocation assigns reported income on W-2s, and only employees under-reporting their share receive allocations.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(x):
    target = Fraction(x['receipts'] * 8, 100)
    reported = sum(e[2] for e in x['staff'])
    short = target - reported
    if short <= 0:
        return {}
    hours_total = sum(e[1] for e in x['staff'])
    deficits = {}
    for name, hrs, rep in x['staff']:
        share = short * hrs / hours_total
        if rep < share:
            deficits[name] = share - rep
    dsum = sum(deficits.values())
    out = {}
    for name, d in deficits.items():
        out[name] = math.floor(short * d / dsum + Fraction(1, 2))
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'receipts': 1926229, 'staff': [['dee', 20, 0], ['eli', 23, 0], ['ben', 15, 23516], ['ana', 9, 0]]}, {'dee': 45999, 'eli': 52899, 'ben': 10984, 'ana': 20700}), ('regression', {'receipts': 1565828, 'staff': [['eli', 40, 65745], ['cy', 29, 58626]]}, {'eli': 895}), ('partial-repair probe', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('partial-repair probe', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('normal control', {'receipts': 296732, 'staff': [['cy', 31, 25635], ['ben', 23, 0], ['ana', 23, 0]]}, {}), ('normal control', {'receipts': 550244, 'staff': [['cy', 19, 57024]]}, {}), ('normal control', {'receipts': 831709, 'staff': [['eli', 25, 0], ['dee', 27, 33872], ['cy', 8, 38931], ['ben', 2, 0]]}, {}), ('normal control', {'receipts': 1484874, 'staff': [['cy', 38, 0], ['dee', 4, 11025], ['eli', 23, 61900], ['ana', 1, 55590]]}, {})], [('regression', {'receipts': 1565828, 'staff': [['eli', 40, 65745], ['cy', 29, 58626]]}, {'eli': 895}), ('regression', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('partial-repair probe', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('partial-repair probe', {'receipts': 213918, 'staff': [['ben', 40, 0], ['dee', 37, 0]]}, {'ben': 8890, 'dee': 8223}), ('normal control', {'receipts': 414394, 'staff': [['ben', 30, 72971], ['dee', 11, 50779], ['cy', 12, 57744], ['ana', 33, 0], ['eli', 37, 0]]}, {}), ('normal control', {'receipts': 15877, 'staff': [['dee', 2, 0], ['ben', 29, 36864], ['cy', 36, 367]]}, {}), ('normal control', {'receipts': 1019554, 'staff': [['dee', 11, 0]]}, {'dee': 81564}), ('normal control', {'receipts': 325470, 'staff': [['ana', 24, 0], ['dee', 18, 39572]]}, {})], [('regression', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('regression', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('partial-repair probe', {'receipts': 213918, 'staff': [['ben', 40, 0], ['dee', 37, 0]]}, {'ben': 8890, 'dee': 8223}), ('partial-repair probe', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('normal control', {'receipts': 1844273, 'staff': [['ben', 6, 9908], ['cy', 2, 35253], ['dee', 0, 47647], ['eli', 24, 54448], ['ana', 19, 60962]]}, {}), ('normal control', {'receipts': 985969, 'staff': [['dee', 16, 0]]}, {'dee': 78878}), ('normal control', {'receipts': 2260421, 'staff': [['cy', 25, 0]]}, {'cy': 180834}), ('normal control', {'receipts': 305915, 'staff': [['ben', 17, 0], ['cy', 35, 49832], ['dee', 14, 0]]}, {})], [('regression', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('regression', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('partial-repair probe', {'receipts': 377533, 'staff': [['ben', 14, 0], ['eli', 20, 18063], ['dee', 33, 0]]}, {'ben': 3616, 'dee': 8524}), ('partial-repair probe', {'receipts': 789350, 'staff': [['dee', 24, 0], ['ben', 32, 0], ['cy', 33, 0], ['ana', 25, 0]]}, {'dee': 13294, 'ben': 17726, 'cy': 18280, 'ana': 13848}), ('normal control', {'receipts': 97470, 'staff': [['ana', 8, 0], ['eli', 19, 14113], ['dee', 6, 49117], ['ben', 0, 0], ['cy', 33, 9103]]}, {}), ('normal control', {'receipts': 1562647, 'staff': [['dee', 25, 0], ['ana', 2, 45159], ['ben', 28, 79991], ['eli', 7, 0], ['cy', 29, 0]]}, {}), ('normal control', {'receipts': 498706, 'staff': [['dee', 17, 44639], ['ana', 25, 0], ['cy', 9, 0]]}, {}), ('normal control', {'receipts': 937562, 'staff': [['eli', 3, 44205], ['ana', 5, 63945], ['dee', 2, 72206], ['cy', 27, 6437]]}, {})], [('regression', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('regression', {'receipts': 2458851, 'staff': [['cy', 31, 15683], ['ben', 27, 76376]]}, {'cy': 89454, 'ben': 15195}), ('partial-repair probe', {'receipts': 2589131, 'staff': [['cy', 0, 9383], ['ana', 9, 74166], ['dee', 1, 54844]]}, {'ana': 68737}), ('partial-repair probe', {'receipts': 2640630, 'staff': [['eli', 21, 16955], ['dee', 0, 76011]]}, {'eli': 118284}), ('normal control', {'receipts': 2072929, 'staff': [['dee', 16, 65338]]}, {'dee': 100496}), ('normal control', {'receipts': 2549094, 'staff': [['ben', 25, 0]]}, {'ben': 203928}), ('normal control', {'receipts': 1079780, 'staff': [['eli', 13, 55620], ['dee', 20, 0]]}, {'dee': 30762}), ('normal control', {'receipts': 207036, 'staff': [['ben', 34, 0], ['ana', 23, 0], ['dee', 36, 46201], ['cy', 28, 10127], ['eli', 18, 74356]]}, {})]]
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	`{'ana': 21394, 'ben': 6975, 'dee': 47541, 'eli': 54672}`	`{'ana': 20700, 'ben': 10984, 'dee': 45999, 'eli': 52899}`	Failed
regression 1	`{}`	`{'eli': 895}`	Failed
partial-repair probe 2	`{'ana': 19303, 'ben': 8948}`	`{'ana': 11268, 'ben': 16984}`	Failed
partial-repair probe 3	`{'ana': 19130, 'ben': 159457}`	`{'ana': 25510, 'ben': 153078}`	Failed
normal control 4	`{}`	`{}`	Passed
normal control 5	`{}`	`{}`	Passed
normal control 6	`{}`	`{}`	Passed
normal control 7	`{}`	`{}`	Passed

SHA-256 / c7415461bb893da3adb7bb288721597c856b609c8b8544c956f7c74c574439f3

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(x):
    target = Fraction(x['receipts'] * 8, 100)
    reported = sum(e[2] for e in x['staff'])
    short = target - reported
    if short <= 0:
        return {}
    hours_total = sum(e[1] for e in x['staff'])
    deficits = {}
    for name, hrs, rep in x['staff']:
        share = target / len(x['staff'])
        if rep < share:
            deficits[name] = share - rep
    dsum = sum(deficits.values())
    out = {}
    for name, d in deficits.items():
        out[name] = math.floor(short * d / dsum + Fraction(1, 2))
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'receipts': 1926229, 'staff': [['dee', 20, 0], ['eli', 23, 0], ['ben', 15, 23516], ['ana', 9, 0]]}, {'dee': 45999, 'eli': 52899, 'ben': 10984, 'ana': 20700}), ('regression', {'receipts': 1565828, 'staff': [['eli', 40, 65745], ['cy', 29, 58626]]}, {'eli': 895}), ('partial-repair probe', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('partial-repair probe', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('normal control', {'receipts': 296732, 'staff': [['cy', 31, 25635], ['ben', 23, 0], ['ana', 23, 0]]}, {}), ('normal control', {'receipts': 550244, 'staff': [['cy', 19, 57024]]}, {}), ('normal control', {'receipts': 831709, 'staff': [['eli', 25, 0], ['dee', 27, 33872], ['cy', 8, 38931], ['ben', 2, 0]]}, {}), ('normal control', {'receipts': 1484874, 'staff': [['cy', 38, 0], ['dee', 4, 11025], ['eli', 23, 61900], ['ana', 1, 55590]]}, {})], [('regression', {'receipts': 1565828, 'staff': [['eli', 40, 65745], ['cy', 29, 58626]]}, {'eli': 895}), ('regression', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('partial-repair probe', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('partial-repair probe', {'receipts': 213918, 'staff': [['ben', 40, 0], ['dee', 37, 0]]}, {'ben': 8890, 'dee': 8223}), ('normal control', {'receipts': 414394, 'staff': [['ben', 30, 72971], ['dee', 11, 50779], ['cy', 12, 57744], ['ana', 33, 0], ['eli', 37, 0]]}, {}), ('normal control', {'receipts': 15877, 'staff': [['dee', 2, 0], ['ben', 29, 36864], ['cy', 36, 367]]}, {}), ('normal control', {'receipts': 1019554, 'staff': [['dee', 11, 0]]}, {'dee': 81564}), ('normal control', {'receipts': 325470, 'staff': [['ana', 24, 0], ['dee', 18, 39572]]}, {})], [('regression', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('regression', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('partial-repair probe', {'receipts': 213918, 'staff': [['ben', 40, 0], ['dee', 37, 0]]}, {'ben': 8890, 'dee': 8223}), ('partial-repair probe', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('normal control', {'receipts': 1844273, 'staff': [['ben', 6, 9908], ['cy', 2, 35253], ['dee', 0, 47647], ['eli', 24, 54448], ['ana', 19, 60962]]}, {}), ('normal control', {'receipts': 985969, 'staff': [['dee', 16, 0]]}, {'dee': 78878}), ('normal control', {'receipts': 2260421, 'staff': [['cy', 25, 0]]}, {'cy': 180834}), ('normal control', {'receipts': 305915, 'staff': [['ben', 17, 0], ['cy', 35, 49832], ['dee', 14, 0]]}, {})], [('regression', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('regression', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('partial-repair probe', {'receipts': 377533, 'staff': [['ben', 14, 0], ['eli', 20, 18063], ['dee', 33, 0]]}, {'ben': 3616, 'dee': 8524}), ('partial-repair probe', {'receipts': 789350, 'staff': [['dee', 24, 0], ['ben', 32, 0], ['cy', 33, 0], ['ana', 25, 0]]}, {'dee': 13294, 'ben': 17726, 'cy': 18280, 'ana': 13848}), ('normal control', {'receipts': 97470, 'staff': [['ana', 8, 0], ['eli', 19, 14113], ['dee', 6, 49117], ['ben', 0, 0], ['cy', 33, 9103]]}, {}), ('normal control', {'receipts': 1562647, 'staff': [['dee', 25, 0], ['ana', 2, 45159], ['ben', 28, 79991], ['eli', 7, 0], ['cy', 29, 0]]}, {}), ('normal control', {'receipts': 498706, 'staff': [['dee', 17, 44639], ['ana', 25, 0], ['cy', 9, 0]]}, {}), ('normal control', {'receipts': 937562, 'staff': [['eli', 3, 44205], ['ana', 5, 63945], ['dee', 2, 72206], ['cy', 27, 6437]]}, {})], [('regression', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('regression', {'receipts': 2458851, 'staff': [['cy', 31, 15683], ['ben', 27, 76376]]}, {'cy': 89454, 'ben': 15195}), ('partial-repair probe', {'receipts': 2589131, 'staff': [['cy', 0, 9383], ['ana', 9, 74166], ['dee', 1, 54844]]}, {'ana': 68737}), ('partial-repair probe', {'receipts': 2640630, 'staff': [['eli', 21, 16955], ['dee', 0, 76011]]}, {'eli': 118284}), ('normal control', {'receipts': 2072929, 'staff': [['dee', 16, 65338]]}, {'dee': 100496}), ('normal control', {'receipts': 2549094, 'staff': [['ben', 25, 0]]}, {'ben': 203928}), ('normal control', {'receipts': 1079780, 'staff': [['eli', 13, 55620], ['dee', 20, 0]]}, {'dee': 30762}), ('normal control', {'receipts': 207036, 'staff': [['ben', 34, 0], ['ana', 23, 0], ['dee', 36, 46201], ['cy', 28, 10127], ['eli', 18, 74356]]}, {})]]
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	`{'ana': 38525, 'ben': 15009, 'dee': 38525, 'eli': 38525}`	`{'ana': 20700, 'ben': 10984, 'dee': 45999, 'eli': 52899}`	Failed
regression 1	`{'cy': 895}`	`{'eli': 895}`	Failed
partial-repair probe 2	`{'ana': 23240, 'ben': 5012}`	`{'ana': 11268, 'ben': 16984}`	Failed
partial-repair probe 3	`{'ana': 71433, 'ben': 107154}`	`{'ana': 25510, 'ben': 153078}`	Failed
normal control 4	`{}`	`{}`	Passed
normal control 5	`{}`	`{}`	Passed
normal control 6	`{}`	`{}`	Passed
normal control 7	`{}`	`{}`	Passed

SHA-256 / a147db9fca1bd6c13db6b9bba6ac0654b452636477cbef90bb7a96697cabc1f5

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(x):
    target = Fraction(x['receipts'] * 8, 100)
    reported = sum(e[2] for e in x['staff'])
    short = target - reported
    if short <= 0:
        return {}
    hours_total = sum(e[1] for e in x['staff'])
    deficits = {}
    for name, hrs, rep in x['staff']:
        share = target * hrs / hours_total
        if rep < share:
            deficits[name] = share - rep
    dsum = sum(deficits.values())
    out = {}
    for name, d in deficits.items():
        out[name] = math.floor(short * d / dsum + Fraction(1, 2))
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'receipts': 1926229, 'staff': [['dee', 20, 0], ['eli', 23, 0], ['ben', 15, 23516], ['ana', 9, 0]]}, {'dee': 45999, 'eli': 52899, 'ben': 10984, 'ana': 20700}), ('regression', {'receipts': 1565828, 'staff': [['eli', 40, 65745], ['cy', 29, 58626]]}, {'eli': 895}), ('partial-repair probe', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('partial-repair probe', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('normal control', {'receipts': 296732, 'staff': [['cy', 31, 25635], ['ben', 23, 0], ['ana', 23, 0]]}, {}), ('normal control', {'receipts': 550244, 'staff': [['cy', 19, 57024]]}, {}), ('normal control', {'receipts': 831709, 'staff': [['eli', 25, 0], ['dee', 27, 33872], ['cy', 8, 38931], ['ben', 2, 0]]}, {}), ('normal control', {'receipts': 1484874, 'staff': [['cy', 38, 0], ['dee', 4, 11025], ['eli', 23, 61900], ['ana', 1, 55590]]}, {})], [('regression', {'receipts': 1565828, 'staff': [['eli', 40, 65745], ['cy', 29, 58626]]}, {'eli': 895}), ('regression', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('partial-repair probe', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('partial-repair probe', {'receipts': 213918, 'staff': [['ben', 40, 0], ['dee', 37, 0]]}, {'ben': 8890, 'dee': 8223}), ('normal control', {'receipts': 414394, 'staff': [['ben', 30, 72971], ['dee', 11, 50779], ['cy', 12, 57744], ['ana', 33, 0], ['eli', 37, 0]]}, {}), ('normal control', {'receipts': 15877, 'staff': [['dee', 2, 0], ['ben', 29, 36864], ['cy', 36, 367]]}, {}), ('normal control', {'receipts': 1019554, 'staff': [['dee', 11, 0]]}, {'dee': 81564}), ('normal control', {'receipts': 325470, 'staff': [['ana', 24, 0], ['dee', 18, 39572]]}, {})], [('regression', {'receipts': 580998, 'staff': [['ana', 8, 0], ['ben', 25, 18228]]}, {'ana': 11268, 'ben': 16984}), ('regression', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('partial-repair probe', {'receipts': 213918, 'staff': [['ben', 40, 0], ['dee', 37, 0]]}, {'ben': 8890, 'dee': 8223}), ('partial-repair probe', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('normal control', {'receipts': 1844273, 'staff': [['ben', 6, 9908], ['cy', 2, 35253], ['dee', 0, 47647], ['eli', 24, 54448], ['ana', 19, 60962]]}, {}), ('normal control', {'receipts': 985969, 'staff': [['dee', 16, 0]]}, {'dee': 78878}), ('normal control', {'receipts': 2260421, 'staff': [['cy', 25, 0]]}, {'cy': 180834}), ('normal control', {'receipts': 305915, 'staff': [['ben', 17, 0], ['cy', 35, 49832], ['dee', 14, 0]]}, {})], [('regression', {'receipts': 2678857, 'staff': [['ana', 8, 35721], ['ben', 20, 0]]}, {'ana': 25510, 'ben': 153078}), ('regression', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('partial-repair probe', {'receipts': 377533, 'staff': [['ben', 14, 0], ['eli', 20, 18063], ['dee', 33, 0]]}, {'ben': 3616, 'dee': 8524}), ('partial-repair probe', {'receipts': 789350, 'staff': [['dee', 24, 0], ['ben', 32, 0], ['cy', 33, 0], ['ana', 25, 0]]}, {'dee': 13294, 'ben': 17726, 'cy': 18280, 'ana': 13848}), ('normal control', {'receipts': 97470, 'staff': [['ana', 8, 0], ['eli', 19, 14113], ['dee', 6, 49117], ['ben', 0, 0], ['cy', 33, 9103]]}, {}), ('normal control', {'receipts': 1562647, 'staff': [['dee', 25, 0], ['ana', 2, 45159], ['ben', 28, 79991], ['eli', 7, 0], ['cy', 29, 0]]}, {}), ('normal control', {'receipts': 498706, 'staff': [['dee', 17, 44639], ['ana', 25, 0], ['cy', 9, 0]]}, {}), ('normal control', {'receipts': 937562, 'staff': [['eli', 3, 44205], ['ana', 5, 63945], ['dee', 2, 72206], ['cy', 27, 6437]]}, {})], [('regression', {'receipts': 2631747, 'staff': [['eli', 6, 73067], ['ana', 26, 49452], ['cy', 10, 52106]]}, {'ana': 35915}), ('regression', {'receipts': 2458851, 'staff': [['cy', 31, 15683], ['ben', 27, 76376]]}, {'cy': 89454, 'ben': 15195}), ('partial-repair probe', {'receipts': 2589131, 'staff': [['cy', 0, 9383], ['ana', 9, 74166], ['dee', 1, 54844]]}, {'ana': 68737}), ('partial-repair probe', {'receipts': 2640630, 'staff': [['eli', 21, 16955], ['dee', 0, 76011]]}, {'eli': 118284}), ('normal control', {'receipts': 2072929, 'staff': [['dee', 16, 65338]]}, {'dee': 100496}), ('normal control', {'receipts': 2549094, 'staff': [['ben', 25, 0]]}, {'ben': 203928}), ('normal control', {'receipts': 1079780, 'staff': [['eli', 13, 55620], ['dee', 20, 0]]}, {'dee': 30762}), ('normal control', {'receipts': 207036, 'staff': [['ben', 34, 0], ['ana', 23, 0], ['dee', 36, 46201], ['cy', 28, 10127], ['eli', 18, 74356]]}, {})]]
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	`{'ana': 20700, 'ben': 10984, 'dee': 45999, 'eli': 52899}`	`{'ana': 20700, 'ben': 10984, 'dee': 45999, 'eli': 52899}`	Passed
regression 1	`{'eli': 895}`	`{'eli': 895}`	Passed
partial-repair probe 2	`{'ana': 11268, 'ben': 16984}`	`{'ana': 11268, 'ben': 16984}`	Passed
partial-repair probe 3	`{'ana': 25510, 'ben': 153078}`	`{'ana': 25510, 'ben': 153078}`	Passed
normal control 4	`{}`	`{}`	Passed
normal control 5	`{}`	`{}`	Passed
normal control 6	`{}`	`{}`	Passed
normal control 7	`{}`	`{}`	Passed

SHA-256 / 5e57e3799580f49556ff7953f9f377d9df9f0d1386e7c97a513ee0204168c0fc

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 2026-09-29T14:46:34.985708+00:00.

Case digest / ba9ca03e13ce6abc538a99e30e6f592ce70b8b65b6a98433dde11ac42dc6481c