FA-59331 / Payroll withholding rules / Open access
Whole-dollar income tax withholding option: extra before rounding · case 01
Employees with additional withholding end up with odd-cent income tax despite electing whole dollars.
ROOT CAUSE
Extra withholding is added after rounding instead of being included in the rounded amount.
VERIFIED REPAIR
Restore the contract rule at the extra before rounding step: use `fit = x['fit'] + x['extra'] if x['whole_dollar']: fit = (fit + 50) // 100 * 100`.
Unsuccessful approach: The attempt skips rounding whenever extra withholding exists.
Case contract
Input {fit, extra, whole_dollar, fica}. Income tax withholding = fit + extra; when whole_dollar is elected, that sum is rounded to the nearest dollar (half-up). FICA is never rounded. Return [income_tax, income_tax + fica].
Why this case matters
Whole-dollar rounding is optional for income tax only and must be applied after all income tax components.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
fit = x['fit']
if x['whole_dollar']:
fit = (fit + 50) // 100 * 100
fit += x['extra']
return [fit, fit + x['fica']]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'fit': 9156, 'extra': 2251, 'whole_dollar': True, 'fica': 25269}, [11400, 36669]), ('regression', {'fit': 8185, 'extra': 1110, 'whole_dollar': True, 'fica': 28304}, [9300, 37604]), ('partial-repair probe', {'fit': 90104, 'extra': 2500, 'whole_dollar': True, 'fica': 8194}, [92600, 100794]), ('partial-repair probe', {'fit': 132638, 'extra': 2500, 'whole_dollar': True, 'fica': 46802}, [135100, 181902]), ('normal control', {'fit': 69591, 'extra': 0, 'whole_dollar': False, 'fica': 14775}, [69591, 84366]), ('normal control', {'fit': 154831, 'extra': 3634, 'whole_dollar': False, 'fica': 44615}, [158465, 203080]), ('normal control', {'fit': 77351, 'extra': 0, 'whole_dollar': False, 'fica': 12610}, [77351, 89961]), ('normal control', {'fit': 88332, 'extra': 0, 'whole_dollar': True, 'fica': 8166}, [88300, 96466])], [('regression', {'fit': 8185, 'extra': 1110, 'whole_dollar': True, 'fica': 28304}, [9300, 37604]), ('regression', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('partial-repair probe', {'fit': 132638, 'extra': 2500, 'whole_dollar': True, 'fica': 46802}, [135100, 181902]), ('partial-repair probe', {'fit': 39742, 'extra': 2500, 'whole_dollar': True, 'fica': 2719}, [42200, 44919]), ('normal control', {'fit': 87258, 'extra': 0, 'whole_dollar': False, 'fica': 46798}, [87258, 134056]), ('normal control', {'fit': 118865, 'extra': 0, 'whole_dollar': False, 'fica': 28601}, [118865, 147466]), ('normal control', {'fit': 16677, 'extra': 0, 'whole_dollar': False, 'fica': 47905}, [16677, 64582]), ('normal control', {'fit': 72176, 'extra': 8810, 'whole_dollar': False, 'fica': 17211}, [80986, 98197])], [('regression', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('regression', {'fit': 71043, 'extra': 1719, 'whole_dollar': True, 'fica': 19074}, [72800, 91874]), ('partial-repair probe', {'fit': 39742, 'extra': 2500, 'whole_dollar': True, 'fica': 2719}, [42200, 44919]), ('partial-repair probe', {'fit': 46034, 'extra': 2500, 'whole_dollar': True, 'fica': 26997}, [48500, 75497]), ('normal control', {'fit': 53286, 'extra': 0, 'whole_dollar': False, 'fica': 40551}, [53286, 93837]), ('normal control', {'fit': 76409, 'extra': 0, 'whole_dollar': False, 'fica': 47429}, [76409, 123838]), ('normal control', {'fit': 158877, 'extra': 0, 'whole_dollar': False, 'fica': 16786}, [158877, 175663]), ('normal control', {'fit': 83141, 'extra': 0, 'whole_dollar': True, 'fica': 11931}, [83100, 95031])], [('regression', {'fit': 71043, 'extra': 1719, 'whole_dollar': True, 'fica': 19074}, [72800, 91874]), ('regression', {'fit': 187550, 'extra': 8476, 'whole_dollar': True, 'fica': 45605}, [196000, 241605]), ('partial-repair probe', {'fit': 160110, 'extra': 2500, 'whole_dollar': True, 'fica': 47818}, [162600, 210418]), ('partial-repair probe', {'fit': 117866, 'extra': 2500, 'whole_dollar': True, 'fica': 44243}, [120400, 164643]), ('normal control', {'fit': 77435, 'extra': 0, 'whole_dollar': True, 'fica': 39916}, [77400, 117316]), ('normal control', {'fit': 16947, 'extra': 0, 'whole_dollar': True, 'fica': 9583}, [16900, 26483]), ('normal control', {'fit': 45654, 'extra': 0, 'whole_dollar': True, 'fica': 24238}, [45700, 69938]), ('normal control', {'fit': 69043, 'extra': 0, 'whole_dollar': True, 'fica': 38706}, [69000, 107706])], [('regression', {'fit': 187550, 'extra': 8476, 'whole_dollar': True, 'fica': 45605}, [196000, 241605]), ('regression', {'fit': 144230, 'extra': 9285, 'whole_dollar': True, 'fica': 21550}, [153500, 175050]), ('partial-repair probe', {'fit': 151775, 'extra': 2500, 'whole_dollar': True, 'fica': 42505}, [154300, 196805]), ('partial-repair probe', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('normal control', {'fit': 12373, 'extra': 1118, 'whole_dollar': False, 'fica': 16260}, [13491, 29751]), ('normal control', {'fit': 31187, 'extra': 0, 'whole_dollar': False, 'fica': 13316}, [31187, 44503]), ('normal control', {'fit': 15705, 'extra': 0, 'whole_dollar': True, 'fica': 2352}, [15700, 18052]), ('normal control', {'fit': 91543, 'extra': 0, 'whole_dollar': True, 'fica': 21661}, [91500, 113161])]]
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 | [11451, 36720] | [11400, 36669] | Failed |
| regression 1 | [9310, 37614] | [9300, 37604] | Failed |
| partial-repair probe 2 | [92600, 100794] | [92600, 100794] | Passed |
| partial-repair probe 3 | [135100, 181902] | [135100, 181902] | Passed |
| normal control 4 | [69591, 84366] | [69591, 84366] | Passed |
| normal control 5 | [158465, 203080] | [158465, 203080] | Passed |
| normal control 6 | [77351, 89961] | [77351, 89961] | Passed |
| normal control 7 | [88300, 96466] | [88300, 96466] | Passed |
SHA-256 / ad42a7855da54dc02f872b25589941021dc59d1c9bd3826c4326a3a213f993ea
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
fit = x['fit'] + x['extra']
if x['whole_dollar'] and x['extra'] == 0:
fit = (fit + 50) // 100 * 100
return [fit, fit + x['fica']]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'fit': 9156, 'extra': 2251, 'whole_dollar': True, 'fica': 25269}, [11400, 36669]), ('regression', {'fit': 8185, 'extra': 1110, 'whole_dollar': True, 'fica': 28304}, [9300, 37604]), ('partial-repair probe', {'fit': 90104, 'extra': 2500, 'whole_dollar': True, 'fica': 8194}, [92600, 100794]), ('partial-repair probe', {'fit': 132638, 'extra': 2500, 'whole_dollar': True, 'fica': 46802}, [135100, 181902]), ('normal control', {'fit': 69591, 'extra': 0, 'whole_dollar': False, 'fica': 14775}, [69591, 84366]), ('normal control', {'fit': 154831, 'extra': 3634, 'whole_dollar': False, 'fica': 44615}, [158465, 203080]), ('normal control', {'fit': 77351, 'extra': 0, 'whole_dollar': False, 'fica': 12610}, [77351, 89961]), ('normal control', {'fit': 88332, 'extra': 0, 'whole_dollar': True, 'fica': 8166}, [88300, 96466])], [('regression', {'fit': 8185, 'extra': 1110, 'whole_dollar': True, 'fica': 28304}, [9300, 37604]), ('regression', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('partial-repair probe', {'fit': 132638, 'extra': 2500, 'whole_dollar': True, 'fica': 46802}, [135100, 181902]), ('partial-repair probe', {'fit': 39742, 'extra': 2500, 'whole_dollar': True, 'fica': 2719}, [42200, 44919]), ('normal control', {'fit': 87258, 'extra': 0, 'whole_dollar': False, 'fica': 46798}, [87258, 134056]), ('normal control', {'fit': 118865, 'extra': 0, 'whole_dollar': False, 'fica': 28601}, [118865, 147466]), ('normal control', {'fit': 16677, 'extra': 0, 'whole_dollar': False, 'fica': 47905}, [16677, 64582]), ('normal control', {'fit': 72176, 'extra': 8810, 'whole_dollar': False, 'fica': 17211}, [80986, 98197])], [('regression', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('regression', {'fit': 71043, 'extra': 1719, 'whole_dollar': True, 'fica': 19074}, [72800, 91874]), ('partial-repair probe', {'fit': 39742, 'extra': 2500, 'whole_dollar': True, 'fica': 2719}, [42200, 44919]), ('partial-repair probe', {'fit': 46034, 'extra': 2500, 'whole_dollar': True, 'fica': 26997}, [48500, 75497]), ('normal control', {'fit': 53286, 'extra': 0, 'whole_dollar': False, 'fica': 40551}, [53286, 93837]), ('normal control', {'fit': 76409, 'extra': 0, 'whole_dollar': False, 'fica': 47429}, [76409, 123838]), ('normal control', {'fit': 158877, 'extra': 0, 'whole_dollar': False, 'fica': 16786}, [158877, 175663]), ('normal control', {'fit': 83141, 'extra': 0, 'whole_dollar': True, 'fica': 11931}, [83100, 95031])], [('regression', {'fit': 71043, 'extra': 1719, 'whole_dollar': True, 'fica': 19074}, [72800, 91874]), ('regression', {'fit': 187550, 'extra': 8476, 'whole_dollar': True, 'fica': 45605}, [196000, 241605]), ('partial-repair probe', {'fit': 160110, 'extra': 2500, 'whole_dollar': True, 'fica': 47818}, [162600, 210418]), ('partial-repair probe', {'fit': 117866, 'extra': 2500, 'whole_dollar': True, 'fica': 44243}, [120400, 164643]), ('normal control', {'fit': 77435, 'extra': 0, 'whole_dollar': True, 'fica': 39916}, [77400, 117316]), ('normal control', {'fit': 16947, 'extra': 0, 'whole_dollar': True, 'fica': 9583}, [16900, 26483]), ('normal control', {'fit': 45654, 'extra': 0, 'whole_dollar': True, 'fica': 24238}, [45700, 69938]), ('normal control', {'fit': 69043, 'extra': 0, 'whole_dollar': True, 'fica': 38706}, [69000, 107706])], [('regression', {'fit': 187550, 'extra': 8476, 'whole_dollar': True, 'fica': 45605}, [196000, 241605]), ('regression', {'fit': 144230, 'extra': 9285, 'whole_dollar': True, 'fica': 21550}, [153500, 175050]), ('partial-repair probe', {'fit': 151775, 'extra': 2500, 'whole_dollar': True, 'fica': 42505}, [154300, 196805]), ('partial-repair probe', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('normal control', {'fit': 12373, 'extra': 1118, 'whole_dollar': False, 'fica': 16260}, [13491, 29751]), ('normal control', {'fit': 31187, 'extra': 0, 'whole_dollar': False, 'fica': 13316}, [31187, 44503]), ('normal control', {'fit': 15705, 'extra': 0, 'whole_dollar': True, 'fica': 2352}, [15700, 18052]), ('normal control', {'fit': 91543, 'extra': 0, 'whole_dollar': True, 'fica': 21661}, [91500, 113161])]]
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 | [11407, 36676] | [11400, 36669] | Failed |
| regression 1 | [9295, 37599] | [9300, 37604] | Failed |
| partial-repair probe 2 | [92604, 100798] | [92600, 100794] | Failed |
| partial-repair probe 3 | [135138, 181940] | [135100, 181902] | Failed |
| normal control 4 | [69591, 84366] | [69591, 84366] | Passed |
| normal control 5 | [158465, 203080] | [158465, 203080] | Passed |
| normal control 6 | [77351, 89961] | [77351, 89961] | Passed |
| normal control 7 | [88300, 96466] | [88300, 96466] | Passed |
SHA-256 / 802eb1f272859cd6675d2c6107f8823dd4be00a395ce47da708b9663811e1595
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
fit = x['fit'] + x['extra']
if x['whole_dollar']:
fit = (fit + 50) // 100 * 100
return [fit, fit + x['fica']]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'fit': 9156, 'extra': 2251, 'whole_dollar': True, 'fica': 25269}, [11400, 36669]), ('regression', {'fit': 8185, 'extra': 1110, 'whole_dollar': True, 'fica': 28304}, [9300, 37604]), ('partial-repair probe', {'fit': 90104, 'extra': 2500, 'whole_dollar': True, 'fica': 8194}, [92600, 100794]), ('partial-repair probe', {'fit': 132638, 'extra': 2500, 'whole_dollar': True, 'fica': 46802}, [135100, 181902]), ('normal control', {'fit': 69591, 'extra': 0, 'whole_dollar': False, 'fica': 14775}, [69591, 84366]), ('normal control', {'fit': 154831, 'extra': 3634, 'whole_dollar': False, 'fica': 44615}, [158465, 203080]), ('normal control', {'fit': 77351, 'extra': 0, 'whole_dollar': False, 'fica': 12610}, [77351, 89961]), ('normal control', {'fit': 88332, 'extra': 0, 'whole_dollar': True, 'fica': 8166}, [88300, 96466])], [('regression', {'fit': 8185, 'extra': 1110, 'whole_dollar': True, 'fica': 28304}, [9300, 37604]), ('regression', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('partial-repair probe', {'fit': 132638, 'extra': 2500, 'whole_dollar': True, 'fica': 46802}, [135100, 181902]), ('partial-repair probe', {'fit': 39742, 'extra': 2500, 'whole_dollar': True, 'fica': 2719}, [42200, 44919]), ('normal control', {'fit': 87258, 'extra': 0, 'whole_dollar': False, 'fica': 46798}, [87258, 134056]), ('normal control', {'fit': 118865, 'extra': 0, 'whole_dollar': False, 'fica': 28601}, [118865, 147466]), ('normal control', {'fit': 16677, 'extra': 0, 'whole_dollar': False, 'fica': 47905}, [16677, 64582]), ('normal control', {'fit': 72176, 'extra': 8810, 'whole_dollar': False, 'fica': 17211}, [80986, 98197])], [('regression', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('regression', {'fit': 71043, 'extra': 1719, 'whole_dollar': True, 'fica': 19074}, [72800, 91874]), ('partial-repair probe', {'fit': 39742, 'extra': 2500, 'whole_dollar': True, 'fica': 2719}, [42200, 44919]), ('partial-repair probe', {'fit': 46034, 'extra': 2500, 'whole_dollar': True, 'fica': 26997}, [48500, 75497]), ('normal control', {'fit': 53286, 'extra': 0, 'whole_dollar': False, 'fica': 40551}, [53286, 93837]), ('normal control', {'fit': 76409, 'extra': 0, 'whole_dollar': False, 'fica': 47429}, [76409, 123838]), ('normal control', {'fit': 158877, 'extra': 0, 'whole_dollar': False, 'fica': 16786}, [158877, 175663]), ('normal control', {'fit': 83141, 'extra': 0, 'whole_dollar': True, 'fica': 11931}, [83100, 95031])], [('regression', {'fit': 71043, 'extra': 1719, 'whole_dollar': True, 'fica': 19074}, [72800, 91874]), ('regression', {'fit': 187550, 'extra': 8476, 'whole_dollar': True, 'fica': 45605}, [196000, 241605]), ('partial-repair probe', {'fit': 160110, 'extra': 2500, 'whole_dollar': True, 'fica': 47818}, [162600, 210418]), ('partial-repair probe', {'fit': 117866, 'extra': 2500, 'whole_dollar': True, 'fica': 44243}, [120400, 164643]), ('normal control', {'fit': 77435, 'extra': 0, 'whole_dollar': True, 'fica': 39916}, [77400, 117316]), ('normal control', {'fit': 16947, 'extra': 0, 'whole_dollar': True, 'fica': 9583}, [16900, 26483]), ('normal control', {'fit': 45654, 'extra': 0, 'whole_dollar': True, 'fica': 24238}, [45700, 69938]), ('normal control', {'fit': 69043, 'extra': 0, 'whole_dollar': True, 'fica': 38706}, [69000, 107706])], [('regression', {'fit': 187550, 'extra': 8476, 'whole_dollar': True, 'fica': 45605}, [196000, 241605]), ('regression', {'fit': 144230, 'extra': 9285, 'whole_dollar': True, 'fica': 21550}, [153500, 175050]), ('partial-repair probe', {'fit': 151775, 'extra': 2500, 'whole_dollar': True, 'fica': 42505}, [154300, 196805]), ('partial-repair probe', {'fit': 15510, 'extra': 5992, 'whole_dollar': True, 'fica': 2377}, [21500, 23877]), ('normal control', {'fit': 12373, 'extra': 1118, 'whole_dollar': False, 'fica': 16260}, [13491, 29751]), ('normal control', {'fit': 31187, 'extra': 0, 'whole_dollar': False, 'fica': 13316}, [31187, 44503]), ('normal control', {'fit': 15705, 'extra': 0, 'whole_dollar': True, 'fica': 2352}, [15700, 18052]), ('normal control', {'fit': 91543, 'extra': 0, 'whole_dollar': True, 'fica': 21661}, [91500, 113161])]]
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 | [11400, 36669] | [11400, 36669] | Passed |
| regression 1 | [9300, 37604] | [9300, 37604] | Passed |
| partial-repair probe 2 | [92600, 100794] | [92600, 100794] | Passed |
| partial-repair probe 3 | [135100, 181902] | [135100, 181902] | Passed |
| normal control 4 | [69591, 84366] | [69591, 84366] | Passed |
| normal control 5 | [158465, 203080] | [158465, 203080] | Passed |
| normal control 6 | [77351, 89961] | [77351, 89961] | Passed |
| normal control 7 | [88300, 96466] | [88300, 96466] | Passed |
SHA-256 / dca66c581d80a18a558e2f8dba1ed973aae49cc7e41baf5838e37db37be315fa
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 2026-09-29T14:46:35.234706+00:00.
Case digest / 957421ef0482db6e19431e7d5f4efabef3e61beb73d066435cf0387e747d2c19