FA-59471 / Subscription proration billing / Open access
Cancellation refund for unused days: setup fee exclusion · case 01
Customers who cancel get part of the one-time setup fee back.
ROOT CAUSE
The setup fee is included in the prorated refund base.
VERIFIED REPAIR
Restore the contract rule at the setup fee exclusion step: use `refund = x['price'] * refundable_days // x['period_days']`.
Unsuccessful approach: The attempt excludes the fee from the base but then deducts it from the refund as a penalty.
Case contract
Input {price, setup, period_days, used_days, min_days, mode immediate|period_end}. Unused = max(0, period_days - used_days). period_end: no refund, access continues for the unused days. immediate: charged days = max(used_days, min_days); refund = price*max(0, period_days - charged)/period_days floored (never over-refund); the setup fee is never refunded; access ends. Return [refund, access_days].
Why this case matters
Refund math must respect minimum-charge terms and exclude non-refundable fees.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
unused = max(0, x['period_days'] - x['used_days'])
if x['mode'] == 'period_end':
return [0, unused]
charged_days = max(x['used_days'], x['min_days'])
refundable_days = max(0, x['period_days'] - charged_days)
refund = (x['price'] + x['setup']) * refundable_days // x['period_days']
return [refund, 0]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'price': 4900, 'setup': 2500, 'period_days': 90, 'used_days': 1, 'min_days': 7, 'mode': 'immediate'}, [4518, 0]), ('regression', {'price': 999, 'setup': 2500, 'period_days': 30, 'used_days': 18, 'min_days': 14, 'mode': 'immediate'}, [399, 0]), ('partial-repair probe', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('partial-repair probe', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('normal control', {'price': 4900, 'setup': 9483, 'period_days': 31, 'used_days': 13, 'min_days': 0, 'mode': 'period_end'}, [0, 18]), ('normal control', {'price': 32440, 'setup': 0, 'period_days': 90, 'used_days': 25, 'min_days': 14, 'mode': 'period_end'}, [0, 65]), ('normal control', {'price': 28584, 'setup': 2500, 'period_days': 90, 'used_days': 93, 'min_days': 14, 'mode': 'immediate'}, [0, 0]), ('normal control', {'price': 4970, 'setup': 0, 'period_days': 90, 'used_days': 0, 'min_days': 7, 'mode': 'immediate'}, [4583, 0])], [('regression', {'price': 999, 'setup': 2500, 'period_days': 30, 'used_days': 18, 'min_days': 14, 'mode': 'immediate'}, [399, 0]), ('regression', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('partial-repair probe', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('partial-repair probe', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 30, 'used_days': 22, 'min_days': 14, 'mode': 'immediate'}, [31973, 0]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 66, 'min_days': 0, 'mode': 'period_end'}, [0, 24]), ('normal control', {'price': 86963, 'setup': 0, 'period_days': 90, 'used_days': 71, 'min_days': 14, 'mode': 'immediate'}, [18358, 0]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 365, 'used_days': 45, 'min_days': 30, 'mode': 'period_end'}, [0, 320])], [('regression', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('regression', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('partial-repair probe', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('partial-repair probe', {'price': 4900, 'setup': 2500, 'period_days': 365, 'used_days': 232, 'min_days': 0, 'mode': 'immediate'}, [1785, 0]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 31, 'used_days': 10, 'min_days': 30, 'mode': 'period_end'}, [0, 21]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 30, 'used_days': 19, 'min_days': 0, 'mode': 'immediate'}, [366, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 365, 'used_days': 32, 'min_days': 0, 'mode': 'immediate'}, [109388, 0]), ('normal control', {'price': 93530, 'setup': 0, 'period_days': 30, 'used_days': 29, 'min_days': 14, 'mode': 'immediate'}, [3117, 0])], [('regression', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('regression', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('partial-repair probe', {'price': 119900, 'setup': 3409, 'period_days': 31, 'used_days': 27, 'min_days': 7, 'mode': 'immediate'}, [15470, 0]), ('partial-repair probe', {'price': 119900, 'setup': 2500, 'period_days': 30, 'used_days': 6, 'min_days': 14, 'mode': 'immediate'}, [63946, 0]), ('normal control', {'price': 119900, 'setup': 2500, 'period_days': 31, 'used_days': 17, 'min_days': 14, 'mode': 'period_end'}, [0, 14]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 53, 'min_days': 30, 'mode': 'immediate'}, [2014, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 90, 'used_days': 22, 'min_days': 7, 'mode': 'period_end'}, [0, 68]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 55, 'min_days': 14, 'mode': 'period_end'}, [0, 35])], [('regression', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('regression', {'price': 4900, 'setup': 2500, 'period_days': 365, 'used_days': 232, 'min_days': 0, 'mode': 'immediate'}, [1785, 0]), ('partial-repair probe', {'price': 4900, 'setup': 7, 'period_days': 90, 'used_days': 46, 'min_days': 0, 'mode': 'immediate'}, [2395, 0]), ('partial-repair probe', {'price': 24966, 'setup': 5091, 'period_days': 31, 'used_days': 26, 'min_days': 0, 'mode': 'immediate'}, [4026, 0]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 90, 'used_days': 3, 'min_days': 14, 'mode': 'period_end'}, [0, 87]), ('normal control', {'price': 119900, 'setup': 5059, 'period_days': 90, 'used_days': 7, 'min_days': 0, 'mode': 'period_end'}, [0, 83]), ('normal control', {'price': 119900, 'setup': 2500, 'period_days': 365, 'used_days': 323, 'min_days': 7, 'mode': 'period_end'}, [0, 42]), ('normal control', {'price': 67850, 'setup': 0, 'period_days': 31, 'used_days': 10, 'min_days': 30, 'mode': 'immediate'}, [2188, 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 | [6824, 0] | [4518, 0] | Failed |
| regression 1 | [1399, 0] | [399, 0] | Failed |
| partial-repair probe 2 | [1693, 0] | [483, 0] | Failed |
| partial-repair probe 3 | [4802, 0] | [3852, 0] | Failed |
| normal control 4 | [0, 18] | [0, 18] | Passed |
| normal control 5 | [0, 65] | [0, 65] | Passed |
| normal control 6 | [0, 0] | [0, 0] | Passed |
| normal control 7 | [4583, 0] | [4583, 0] | Passed |
SHA-256 / c1515fa6259cef49dc7bc6f450bd526ad1936f1bfe5ffbefbb07f8e9357d4cd9
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
unused = max(0, x['period_days'] - x['used_days'])
if x['mode'] == 'period_end':
return [0, unused]
charged_days = max(x['used_days'], x['min_days'])
refundable_days = max(0, x['period_days'] - charged_days)
refund = max(0, x['price'] * refundable_days // x['period_days'] - x['setup'])
return [refund, 0]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'price': 4900, 'setup': 2500, 'period_days': 90, 'used_days': 1, 'min_days': 7, 'mode': 'immediate'}, [4518, 0]), ('regression', {'price': 999, 'setup': 2500, 'period_days': 30, 'used_days': 18, 'min_days': 14, 'mode': 'immediate'}, [399, 0]), ('partial-repair probe', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('partial-repair probe', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('normal control', {'price': 4900, 'setup': 9483, 'period_days': 31, 'used_days': 13, 'min_days': 0, 'mode': 'period_end'}, [0, 18]), ('normal control', {'price': 32440, 'setup': 0, 'period_days': 90, 'used_days': 25, 'min_days': 14, 'mode': 'period_end'}, [0, 65]), ('normal control', {'price': 28584, 'setup': 2500, 'period_days': 90, 'used_days': 93, 'min_days': 14, 'mode': 'immediate'}, [0, 0]), ('normal control', {'price': 4970, 'setup': 0, 'period_days': 90, 'used_days': 0, 'min_days': 7, 'mode': 'immediate'}, [4583, 0])], [('regression', {'price': 999, 'setup': 2500, 'period_days': 30, 'used_days': 18, 'min_days': 14, 'mode': 'immediate'}, [399, 0]), ('regression', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('partial-repair probe', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('partial-repair probe', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 30, 'used_days': 22, 'min_days': 14, 'mode': 'immediate'}, [31973, 0]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 66, 'min_days': 0, 'mode': 'period_end'}, [0, 24]), ('normal control', {'price': 86963, 'setup': 0, 'period_days': 90, 'used_days': 71, 'min_days': 14, 'mode': 'immediate'}, [18358, 0]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 365, 'used_days': 45, 'min_days': 30, 'mode': 'period_end'}, [0, 320])], [('regression', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('regression', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('partial-repair probe', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('partial-repair probe', {'price': 4900, 'setup': 2500, 'period_days': 365, 'used_days': 232, 'min_days': 0, 'mode': 'immediate'}, [1785, 0]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 31, 'used_days': 10, 'min_days': 30, 'mode': 'period_end'}, [0, 21]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 30, 'used_days': 19, 'min_days': 0, 'mode': 'immediate'}, [366, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 365, 'used_days': 32, 'min_days': 0, 'mode': 'immediate'}, [109388, 0]), ('normal control', {'price': 93530, 'setup': 0, 'period_days': 30, 'used_days': 29, 'min_days': 14, 'mode': 'immediate'}, [3117, 0])], [('regression', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('regression', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('partial-repair probe', {'price': 119900, 'setup': 3409, 'period_days': 31, 'used_days': 27, 'min_days': 7, 'mode': 'immediate'}, [15470, 0]), ('partial-repair probe', {'price': 119900, 'setup': 2500, 'period_days': 30, 'used_days': 6, 'min_days': 14, 'mode': 'immediate'}, [63946, 0]), ('normal control', {'price': 119900, 'setup': 2500, 'period_days': 31, 'used_days': 17, 'min_days': 14, 'mode': 'period_end'}, [0, 14]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 53, 'min_days': 30, 'mode': 'immediate'}, [2014, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 90, 'used_days': 22, 'min_days': 7, 'mode': 'period_end'}, [0, 68]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 55, 'min_days': 14, 'mode': 'period_end'}, [0, 35])], [('regression', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('regression', {'price': 4900, 'setup': 2500, 'period_days': 365, 'used_days': 232, 'min_days': 0, 'mode': 'immediate'}, [1785, 0]), ('partial-repair probe', {'price': 4900, 'setup': 7, 'period_days': 90, 'used_days': 46, 'min_days': 0, 'mode': 'immediate'}, [2395, 0]), ('partial-repair probe', {'price': 24966, 'setup': 5091, 'period_days': 31, 'used_days': 26, 'min_days': 0, 'mode': 'immediate'}, [4026, 0]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 90, 'used_days': 3, 'min_days': 14, 'mode': 'period_end'}, [0, 87]), ('normal control', {'price': 119900, 'setup': 5059, 'period_days': 90, 'used_days': 7, 'min_days': 0, 'mode': 'period_end'}, [0, 83]), ('normal control', {'price': 119900, 'setup': 2500, 'period_days': 365, 'used_days': 323, 'min_days': 7, 'mode': 'period_end'}, [0, 42]), ('normal control', {'price': 67850, 'setup': 0, 'period_days': 31, 'used_days': 10, 'min_days': 30, 'mode': 'immediate'}, [2188, 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 | [2018, 0] | [4518, 0] | Failed |
| regression 1 | [0, 0] | [399, 0] | Failed |
| partial-repair probe 2 | [0, 0] | [483, 0] | Failed |
| partial-repair probe 3 | [2644, 0] | [3852, 0] | Failed |
| normal control 4 | [0, 18] | [0, 18] | Passed |
| normal control 5 | [0, 65] | [0, 65] | Passed |
| normal control 6 | [0, 0] | [0, 0] | Passed |
| normal control 7 | [4583, 0] | [4583, 0] | Passed |
SHA-256 / 60837400229a282aed1ad1b54b8015150c14fa282f0c6af0896dd5e246ebfdfc
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
unused = max(0, x['period_days'] - x['used_days'])
if x['mode'] == 'period_end':
return [0, unused]
charged_days = max(x['used_days'], x['min_days'])
refundable_days = max(0, x['period_days'] - charged_days)
refund = x['price'] * refundable_days // x['period_days']
return [refund, 0]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'price': 4900, 'setup': 2500, 'period_days': 90, 'used_days': 1, 'min_days': 7, 'mode': 'immediate'}, [4518, 0]), ('regression', {'price': 999, 'setup': 2500, 'period_days': 30, 'used_days': 18, 'min_days': 14, 'mode': 'immediate'}, [399, 0]), ('partial-repair probe', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('partial-repair probe', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('normal control', {'price': 4900, 'setup': 9483, 'period_days': 31, 'used_days': 13, 'min_days': 0, 'mode': 'period_end'}, [0, 18]), ('normal control', {'price': 32440, 'setup': 0, 'period_days': 90, 'used_days': 25, 'min_days': 14, 'mode': 'period_end'}, [0, 65]), ('normal control', {'price': 28584, 'setup': 2500, 'period_days': 90, 'used_days': 93, 'min_days': 14, 'mode': 'immediate'}, [0, 0]), ('normal control', {'price': 4970, 'setup': 0, 'period_days': 90, 'used_days': 0, 'min_days': 7, 'mode': 'immediate'}, [4583, 0])], [('regression', {'price': 999, 'setup': 2500, 'period_days': 30, 'used_days': 18, 'min_days': 14, 'mode': 'immediate'}, [399, 0]), ('regression', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('partial-repair probe', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('partial-repair probe', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 30, 'used_days': 22, 'min_days': 14, 'mode': 'immediate'}, [31973, 0]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 66, 'min_days': 0, 'mode': 'period_end'}, [0, 24]), ('normal control', {'price': 86963, 'setup': 0, 'period_days': 90, 'used_days': 71, 'min_days': 14, 'mode': 'immediate'}, [18358, 0]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 365, 'used_days': 45, 'min_days': 30, 'mode': 'period_end'}, [0, 320])], [('regression', {'price': 999, 'setup': 2500, 'period_days': 31, 'used_days': 16, 'min_days': 0, 'mode': 'immediate'}, [483, 0]), ('regression', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('partial-repair probe', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('partial-repair probe', {'price': 4900, 'setup': 2500, 'period_days': 365, 'used_days': 232, 'min_days': 0, 'mode': 'immediate'}, [1785, 0]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 31, 'used_days': 10, 'min_days': 30, 'mode': 'period_end'}, [0, 21]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 30, 'used_days': 19, 'min_days': 0, 'mode': 'immediate'}, [366, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 365, 'used_days': 32, 'min_days': 0, 'mode': 'immediate'}, [109388, 0]), ('normal control', {'price': 93530, 'setup': 0, 'period_days': 30, 'used_days': 29, 'min_days': 14, 'mode': 'immediate'}, [3117, 0])], [('regression', {'price': 4900, 'setup': 1208, 'period_days': 365, 'used_days': 78, 'min_days': 14, 'mode': 'immediate'}, [3852, 0]), ('regression', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('partial-repair probe', {'price': 119900, 'setup': 3409, 'period_days': 31, 'used_days': 27, 'min_days': 7, 'mode': 'immediate'}, [15470, 0]), ('partial-repair probe', {'price': 119900, 'setup': 2500, 'period_days': 30, 'used_days': 6, 'min_days': 14, 'mode': 'immediate'}, [63946, 0]), ('normal control', {'price': 119900, 'setup': 2500, 'period_days': 31, 'used_days': 17, 'min_days': 14, 'mode': 'period_end'}, [0, 14]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 53, 'min_days': 30, 'mode': 'immediate'}, [2014, 0]), ('normal control', {'price': 119900, 'setup': 0, 'period_days': 90, 'used_days': 22, 'min_days': 7, 'mode': 'period_end'}, [0, 68]), ('normal control', {'price': 4900, 'setup': 0, 'period_days': 90, 'used_days': 55, 'min_days': 14, 'mode': 'period_end'}, [0, 35])], [('regression', {'price': 119900, 'setup': 1279, 'period_days': 30, 'used_days': 14, 'min_days': 7, 'mode': 'immediate'}, [63946, 0]), ('regression', {'price': 4900, 'setup': 2500, 'period_days': 365, 'used_days': 232, 'min_days': 0, 'mode': 'immediate'}, [1785, 0]), ('partial-repair probe', {'price': 4900, 'setup': 7, 'period_days': 90, 'used_days': 46, 'min_days': 0, 'mode': 'immediate'}, [2395, 0]), ('partial-repair probe', {'price': 24966, 'setup': 5091, 'period_days': 31, 'used_days': 26, 'min_days': 0, 'mode': 'immediate'}, [4026, 0]), ('normal control', {'price': 999, 'setup': 0, 'period_days': 90, 'used_days': 3, 'min_days': 14, 'mode': 'period_end'}, [0, 87]), ('normal control', {'price': 119900, 'setup': 5059, 'period_days': 90, 'used_days': 7, 'min_days': 0, 'mode': 'period_end'}, [0, 83]), ('normal control', {'price': 119900, 'setup': 2500, 'period_days': 365, 'used_days': 323, 'min_days': 7, 'mode': 'period_end'}, [0, 42]), ('normal control', {'price': 67850, 'setup': 0, 'period_days': 31, 'used_days': 10, 'min_days': 30, 'mode': 'immediate'}, [2188, 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 | [4518, 0] | [4518, 0] | Passed |
| regression 1 | [399, 0] | [399, 0] | Passed |
| partial-repair probe 2 | [483, 0] | [483, 0] | Passed |
| partial-repair probe 3 | [3852, 0] | [3852, 0] | Passed |
| normal control 4 | [0, 18] | [0, 18] | Passed |
| normal control 5 | [0, 65] | [0, 65] | Passed |
| normal control 6 | [0, 0] | [0, 0] | Passed |
| normal control 7 | [4583, 0] | [4583, 0] | Passed |
SHA-256 / a80033354d10f5e108490ede826f499b807eeaf440ed17b2c1283b3c2df344c2
Verification & scope
A deterministic teaching model of a stipulated billing rule. It makes no claim to reproduce any billing provider's exact behaviour and is not billing 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 2026-09-29T14:46:36.571664+00:00.
Case digest / 6d57e5aaf7afcf3cadee2f1af0679d6ea04341cf606056c9c282781cc6a51ce4