FA-59001 / Payroll withholding rules / Open access
Overtime regular rate with bonuses and differentials: shift differential in regular rate · case 01
Night-shift workers with overtime are underpaid on their premium.
ROOT CAUSE
Shift-differential pay is left out of the compensation used for the regular rate.
VERIFIED REPAIR
Restore the contract rule at the shift differential in regular rate step: use `included = base +`.
Unsuccessful approach: The attempt applies the differential to every hour instead of only the differential hours.
Case contract
Input weekly {hours, rate, bonus, discretionary, diff_hours, diff}. Straight pay = hours*rate + diff_hours*diff. Regular rate = (straight pay + bonus unless discretionary) / hours. Overtime premium = regular rate * max(0, hours-40) / 2, rounded half-up to the cent. Total = straight pay + bonus + premium. Zero hours returns [0, bonus]. Return [premium, total].
Why this case matters
Nondiscretionary bonuses and shift differentials raise the regular rate, and only a half-time premium is owed on top of straight-time pay.
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):
h = x['hours']
base = h * x['rate'] + x['diff_hours'] * x['diff']
included = h * x['rate'] + (0 if x['discretionary'] else x['bonus'])
if h == 0:
return [0, x['bonus']]
ot = max(0, h - 40)
rr = Fraction(included, h)
premium = rr * ot / 2
prem_c = math.floor(premium + Fraction(1, 2))
total = base + x['bonus'] + prem_c
return [prem_c, total]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'hours': 56, 'rate': 2894, 'bonus': 0, 'discretionary': True, 'diff_hours': 20, 'diff': 189}, [23692, 189536]), ('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('partial-repair probe', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('normal control', {'hours': 29, 'rate': 3185, 'bonus': 18960, 'discretionary': True, 'diff_hours': 0, 'diff': 235}, [0, 111325]), ('normal control', {'hours': 60, 'rate': 3726, 'bonus': 328, 'discretionary': False, 'diff_hours': 13, 'diff': 0}, [37315, 261203]), ('normal control', {'hours': 15, 'rate': 1913, 'bonus': 0, 'discretionary': False, 'diff_hours': 3, 'diff': 100}, [0, 28995]), ('normal control', {'hours': 36, 'rate': 1564, 'bonus': 12440, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 68744])], [('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('normal control', {'hours': 36, 'rate': 3124, 'bonus': 20788, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 133252]), ('normal control', {'hours': 38, 'rate': 3803, 'bonus': 0, 'discretionary': False, 'diff_hours': 5, 'diff': 175}, [0, 145389]), ('normal control', {'hours': 40, 'rate': 2368, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 94720]), ('normal control', {'hours': 15, 'rate': 1275, 'bonus': 16297, 'discretionary': False, 'diff_hours': 0, 'diff': 100}, [0, 35422])], [('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('partial-repair probe', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('normal control', {'hours': 0, 'rate': 1235, 'bonus': 452, 'discretionary': False, 'diff_hours': 15, 'diff': 0}, [0, 452]), ('normal control', {'hours': 25, 'rate': 2784, 'bonus': 431, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 70031]), ('normal control', {'hours': 49, 'rate': 2740, 'bonus': 1615, 'discretionary': True, 'diff_hours': 20, 'diff': 0}, [12330, 148205]), ('normal control', {'hours': 4, 'rate': 2630, 'bonus': 24821, 'discretionary': False, 'diff_hours': 10, 'diff': 100}, [0, 36341])], [('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('partial-repair probe', {'hours': 41, 'rate': 1051, 'bonus': 26, 'discretionary': False, 'diff_hours': 0, 'diff': 259}, [526, 43643]), ('partial-repair probe', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('normal control', {'hours': 36, 'rate': 3824, 'bonus': 0, 'discretionary': False, 'diff_hours': 12, 'diff': 100}, [0, 138864]), ('normal control', {'hours': 36, 'rate': 3363, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 0}, [0, 121068]), ('normal control', {'hours': 58, 'rate': 1280, 'bonus': 145, 'discretionary': False, 'diff_hours': 5, 'diff': 0}, [11543, 85928]), ('normal control', {'hours': 25, 'rate': 1555, 'bonus': 17670, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 56545])], [('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('regression', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('partial-repair probe', {'hours': 52, 'rate': 2128, 'bonus': 978, 'discretionary': True, 'diff_hours': 9, 'diff': 100}, [12872, 125406]), ('partial-repair probe', {'hours': 51, 'rate': 3958, 'bonus': 443, 'discretionary': True, 'diff_hours': 0, 'diff': 100}, [21769, 224070]), ('normal control', {'hours': 13, 'rate': 3420, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 44460]), ('normal control', {'hours': 17, 'rate': 3366, 'bonus': 396, 'discretionary': True, 'diff_hours': 8, 'diff': 0}, [0, 57618]), ('normal control', {'hours': 1, 'rate': 1458, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 1458]), ('normal control', {'hours': 40, 'rate': 2719, 'bonus': 0, 'discretionary': False, 'diff_hours': 13, 'diff': 100}, [0, 110060])]]
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 | [23152, 188996] | [23692, 189536] | Failed |
| regression 1 | [9444, 125022] | [9632, 125210] | Failed |
| partial-repair probe 2 | [2675, 57718] | [2692, 57735] | Failed |
| partial-repair probe 3 | [7865, 84982] | [8254, 85371] | Failed |
| normal control 4 | [0, 111325] | [0, 111325] | Passed |
| normal control 5 | [37315, 261203] | [37315, 261203] | Passed |
| normal control 6 | [0, 28995] | [0, 28995] | Passed |
| normal control 7 | [0, 68744] | [0, 68744] | Passed |
SHA-256 / 4ce12715a4f961d3d7645d4a95c84bf69721d143cad0efa36e6d556de6305183
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):
h = x['hours']
base = h * x['rate'] + x['diff_hours'] * x['diff']
included = h * (x['rate'] + x['diff']) + (0 if x['discretionary'] else x['bonus'])
if h == 0:
return [0, x['bonus']]
ot = max(0, h - 40)
rr = Fraction(included, h)
premium = rr * ot / 2
prem_c = math.floor(premium + Fraction(1, 2))
total = base + x['bonus'] + prem_c
return [prem_c, total]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'hours': 56, 'rate': 2894, 'bonus': 0, 'discretionary': True, 'diff_hours': 20, 'diff': 189}, [23692, 189536]), ('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('partial-repair probe', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('normal control', {'hours': 29, 'rate': 3185, 'bonus': 18960, 'discretionary': True, 'diff_hours': 0, 'diff': 235}, [0, 111325]), ('normal control', {'hours': 60, 'rate': 3726, 'bonus': 328, 'discretionary': False, 'diff_hours': 13, 'diff': 0}, [37315, 261203]), ('normal control', {'hours': 15, 'rate': 1913, 'bonus': 0, 'discretionary': False, 'diff_hours': 3, 'diff': 100}, [0, 28995]), ('normal control', {'hours': 36, 'rate': 1564, 'bonus': 12440, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 68744])], [('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('normal control', {'hours': 36, 'rate': 3124, 'bonus': 20788, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 133252]), ('normal control', {'hours': 38, 'rate': 3803, 'bonus': 0, 'discretionary': False, 'diff_hours': 5, 'diff': 175}, [0, 145389]), ('normal control', {'hours': 40, 'rate': 2368, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 94720]), ('normal control', {'hours': 15, 'rate': 1275, 'bonus': 16297, 'discretionary': False, 'diff_hours': 0, 'diff': 100}, [0, 35422])], [('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('partial-repair probe', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('normal control', {'hours': 0, 'rate': 1235, 'bonus': 452, 'discretionary': False, 'diff_hours': 15, 'diff': 0}, [0, 452]), ('normal control', {'hours': 25, 'rate': 2784, 'bonus': 431, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 70031]), ('normal control', {'hours': 49, 'rate': 2740, 'bonus': 1615, 'discretionary': True, 'diff_hours': 20, 'diff': 0}, [12330, 148205]), ('normal control', {'hours': 4, 'rate': 2630, 'bonus': 24821, 'discretionary': False, 'diff_hours': 10, 'diff': 100}, [0, 36341])], [('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('partial-repair probe', {'hours': 41, 'rate': 1051, 'bonus': 26, 'discretionary': False, 'diff_hours': 0, 'diff': 259}, [526, 43643]), ('partial-repair probe', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('normal control', {'hours': 36, 'rate': 3824, 'bonus': 0, 'discretionary': False, 'diff_hours': 12, 'diff': 100}, [0, 138864]), ('normal control', {'hours': 36, 'rate': 3363, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 0}, [0, 121068]), ('normal control', {'hours': 58, 'rate': 1280, 'bonus': 145, 'discretionary': False, 'diff_hours': 5, 'diff': 0}, [11543, 85928]), ('normal control', {'hours': 25, 'rate': 1555, 'bonus': 17670, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 56545])], [('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('regression', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('partial-repair probe', {'hours': 52, 'rate': 2128, 'bonus': 978, 'discretionary': True, 'diff_hours': 9, 'diff': 100}, [12872, 125406]), ('partial-repair probe', {'hours': 51, 'rate': 3958, 'bonus': 443, 'discretionary': True, 'diff_hours': 0, 'diff': 100}, [21769, 224070]), ('normal control', {'hours': 13, 'rate': 3420, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 44460]), ('normal control', {'hours': 17, 'rate': 3366, 'bonus': 396, 'discretionary': True, 'diff_hours': 8, 'diff': 0}, [0, 57618]), ('normal control', {'hours': 1, 'rate': 1458, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 1458]), ('normal control', {'hours': 40, 'rate': 2719, 'bonus': 0, 'discretionary': False, 'diff_hours': 13, 'diff': 100}, [0, 110060])]]
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 | [24664, 190508] | [23692, 189536] | Failed |
| regression 1 | [10044, 125622] | [9632, 125210] | Failed |
| partial-repair probe 2 | [2925, 57968] | [2692, 57735] | Failed |
| partial-repair probe 3 | [9031, 86148] | [8254, 85371] | Failed |
| normal control 4 | [0, 111325] | [0, 111325] | Passed |
| normal control 5 | [37315, 261203] | [37315, 261203] | Passed |
| normal control 6 | [0, 28995] | [0, 28995] | Passed |
| normal control 7 | [0, 68744] | [0, 68744] | Passed |
SHA-256 / b4775046a748631ec3fdf930d7c40b5d7e8de434c08f649bb840bafed920e408
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):
h = x['hours']
base = h * x['rate'] + x['diff_hours'] * x['diff']
included = base + (0 if x['discretionary'] else x['bonus'])
if h == 0:
return [0, x['bonus']]
ot = max(0, h - 40)
rr = Fraction(included, h)
premium = rr * ot / 2
prem_c = math.floor(premium + Fraction(1, 2))
total = base + x['bonus'] + prem_c
return [prem_c, total]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'hours': 56, 'rate': 2894, 'bonus': 0, 'discretionary': True, 'diff_hours': 20, 'diff': 189}, [23692, 189536]), ('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('partial-repair probe', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('normal control', {'hours': 29, 'rate': 3185, 'bonus': 18960, 'discretionary': True, 'diff_hours': 0, 'diff': 235}, [0, 111325]), ('normal control', {'hours': 60, 'rate': 3726, 'bonus': 328, 'discretionary': False, 'diff_hours': 13, 'diff': 0}, [37315, 261203]), ('normal control', {'hours': 15, 'rate': 1913, 'bonus': 0, 'discretionary': False, 'diff_hours': 3, 'diff': 100}, [0, 28995]), ('normal control', {'hours': 36, 'rate': 1564, 'bonus': 12440, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 68744])], [('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('normal control', {'hours': 36, 'rate': 3124, 'bonus': 20788, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 133252]), ('normal control', {'hours': 38, 'rate': 3803, 'bonus': 0, 'discretionary': False, 'diff_hours': 5, 'diff': 175}, [0, 145389]), ('normal control', {'hours': 40, 'rate': 2368, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 94720]), ('normal control', {'hours': 15, 'rate': 1275, 'bonus': 16297, 'discretionary': False, 'diff_hours': 0, 'diff': 100}, [0, 35422])], [('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('partial-repair probe', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('normal control', {'hours': 0, 'rate': 1235, 'bonus': 452, 'discretionary': False, 'diff_hours': 15, 'diff': 0}, [0, 452]), ('normal control', {'hours': 25, 'rate': 2784, 'bonus': 431, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 70031]), ('normal control', {'hours': 49, 'rate': 2740, 'bonus': 1615, 'discretionary': True, 'diff_hours': 20, 'diff': 0}, [12330, 148205]), ('normal control', {'hours': 4, 'rate': 2630, 'bonus': 24821, 'discretionary': False, 'diff_hours': 10, 'diff': 100}, [0, 36341])], [('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('partial-repair probe', {'hours': 41, 'rate': 1051, 'bonus': 26, 'discretionary': False, 'diff_hours': 0, 'diff': 259}, [526, 43643]), ('partial-repair probe', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('normal control', {'hours': 36, 'rate': 3824, 'bonus': 0, 'discretionary': False, 'diff_hours': 12, 'diff': 100}, [0, 138864]), ('normal control', {'hours': 36, 'rate': 3363, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 0}, [0, 121068]), ('normal control', {'hours': 58, 'rate': 1280, 'bonus': 145, 'discretionary': False, 'diff_hours': 5, 'diff': 0}, [11543, 85928]), ('normal control', {'hours': 25, 'rate': 1555, 'bonus': 17670, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 56545])], [('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('regression', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('partial-repair probe', {'hours': 52, 'rate': 2128, 'bonus': 978, 'discretionary': True, 'diff_hours': 9, 'diff': 100}, [12872, 125406]), ('partial-repair probe', {'hours': 51, 'rate': 3958, 'bonus': 443, 'discretionary': True, 'diff_hours': 0, 'diff': 100}, [21769, 224070]), ('normal control', {'hours': 13, 'rate': 3420, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 44460]), ('normal control', {'hours': 17, 'rate': 3366, 'bonus': 396, 'discretionary': True, 'diff_hours': 8, 'diff': 0}, [0, 57618]), ('normal control', {'hours': 1, 'rate': 1458, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 1458]), ('normal control', {'hours': 40, 'rate': 2719, 'bonus': 0, 'discretionary': False, 'diff_hours': 13, 'diff': 100}, [0, 110060])]]
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 | [23692, 189536] | [23692, 189536] | Passed |
| regression 1 | [9632, 125210] | [9632, 125210] | Passed |
| partial-repair probe 2 | [2692, 57735] | [2692, 57735] | Passed |
| partial-repair probe 3 | [8254, 85371] | [8254, 85371] | Passed |
| normal control 4 | [0, 111325] | [0, 111325] | Passed |
| normal control 5 | [37315, 261203] | [37315, 261203] | Passed |
| normal control 6 | [0, 28995] | [0, 28995] | Passed |
| normal control 7 | [0, 68744] | [0, 68744] | Passed |
SHA-256 / 64cfecd3fcc8f8f5eecf3778e545cfa2fbd109b20262d6718d9e723ad594f944
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 2026-09-29T14:46:32.085476+00:00.
Case digest / dbf59ba4d6a3394b1727ac950ce5128a237da0a8f5ef6d2d6e941109da5c7c1c