FA-59501 / Subscription proration billing / Open access

Usage tiers after a prorated included allowance: covered usage short-circuit · case 01

Customers whose usage is fully covered by the allowance are billed a tier flat fee.

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

ROOT CAUSE

The zero-billable shortcut never fires, so the volume branch charges the first tier's flat fee for zero units.

VERIFIED REPAIR

Restore the contract rule at the covered usage short-circuit step: use `if units == 0: return [allow, 0]`.

Unsuccessful approach: The attempt short-circuits only on zero raw usage, still charging a flat fee when the allowance covers usage.

Case contract

Input {usage, tiers: [[up_to|None, unit_price, flat_fee]], mode volume|graduated, included, active_days, period_days}. Allowance = included*active_days//period_days. Billable = max(0, usage - allowance); zero billable costs 0. Volume: all billable units at the first tier with billable <= up_to, plus that tier's flat fee. Graduated: each tier bills the units falling in its range plus its flat fee if any unit falls in it. Return [allowance, cost].

Why this case matters

A partial first period shrinks the included allowance, and volume versus graduated tiers price the remainder differently.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(x):
    allow = x['included'] * x['active_days'] // x['period_days']
    units = max(0, x['usage'] - allow)
    if units < 0:
        return [allow, 0]
    if x['mode'] == 'volume':
        for up, price, flat in x['tiers']:
            if up is None or units <= up:
                return [allow, units * price + flat]
    cost = 0
    prev = 0
    for up, price, flat in x['tiers']:
        top = units if up is None else min(units, up)
        if top > prev:
            cost += (top - prev) * price + flat
        if up is None or units <= up:
            break
        prev = up
    return [allow, cost]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'usage': 129, 'tiers': [[129, 9, 500], [845, 8, 300], [None, 18, 0]], 'mode': 'volume', 'included': 250, 'active_days': 29, 'period_days': 30}, [241, 0]), ('regression', {'usage': 59, 'tiers': [[302, 10, 500], [914, 15, 0], [None, 7, 0]], 'mode': 'volume', 'included': 250, 'active_days': 27, 'period_days': 30}, [225, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 215, 'tiers': [[215, 32, 0], [1030, 38, 300], [None, 21, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('normal control', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('normal control', {'usage': 195, 'tiers': [[263, 41, 0], [444, 16, 300], [None, 18, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 31}, [0, 7995]), ('normal control', {'usage': 149, 'tiers': [[96, 6, 0], [296, 34, 0], [None, 9, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 27, 'period_days': 30}, [0, 2378])], [('regression', {'usage': 59, 'tiers': [[302, 10, 500], [914, 15, 0], [None, 7, 0]], 'mode': 'volume', 'included': 250, 'active_days': 27, 'period_days': 30}, [225, 0]), ('regression', {'usage': 0, 'tiers': [[261, 18, 500], [782, 5, 300], [None, 25, 0]], 'mode': 'volume', 'included': 100, 'active_days': 14, 'period_days': 30}, [46, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 132, 'tiers': [[102, 43, 500], [1090, 39, 300], [None, 1, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 31, 'period_days': 31}, [100, 1876]), ('normal control', {'usage': 1908, 'tiers': [[438, 11, 500], [1328, 8, 0], [None, 10, 0]], 'mode': 'volume', 'included': 0, 'active_days': 3, 'period_days': 31}, [0, 19080]), ('normal control', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0])], [('regression', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('regression', {'usage': 0, 'tiers': [[261, 18, 500], [782, 5, 300], [None, 25, 0]], 'mode': 'volume', 'included': 100, 'active_days': 14, 'period_days': 30}, [46, 0]), ('partial-repair probe', {'usage': 71, 'tiers': [[446, 25, 500], [966, 37, 300], [None, 16, 0]], 'mode': 'volume', 'included': 100, 'active_days': 26, 'period_days': 31}, [83, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[164, 9, 500], [362, 20, 0], [None, 12, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('normal control', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('normal control', {'usage': 47, 'tiers': [[264, 39, 0], [1255, 6, 0], [None, 12, 0]], 'mode': 'volume', 'included': 250, 'active_days': 24, 'period_days': 30}, [200, 0]), ('normal control', {'usage': 31, 'tiers': [[57, 14, 0], [997, 11, 300], [None, 7, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 3, 'period_days': 30}, [100, 0]), ('normal control', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160])], [('regression', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('regression', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 121, 'tiers': [[92, 15, 500], [339, 6, 300], [None, 14, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 26, 'period_days': 31}, [838, 0]), ('partial-repair probe', {'usage': 60, 'tiers': [[268, 25, 500], [1138, 32, 0], [None, 4, 0]], 'mode': 'volume', 'included': 250, 'active_days': 10, 'period_days': 30}, [83, 0]), ('normal control', {'usage': 1862, 'tiers': [[90, 6, 0], [1073, 39, 0], [None, 20, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 29, 'period_days': 31}, [93, 52797]), ('normal control', {'usage': 2285, 'tiers': [[395, 26, 500], [1142, 40, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 13, 'period_days': 30}, [0, 68082]), ('normal control', {'usage': 205, 'tiers': [[205, 36, 0], [1123, 17, 300], [None, 4, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 16, 'period_days': 31}, [516, 0]), ('normal control', {'usage': 286, 'tiers': [[286, 31, 500], [1169, 13, 0], [None, 22, 0]], 'mode': 'volume', 'included': 250, 'active_days': 30, 'period_days': 31}, [241, 1895])], [('regression', {'usage': 71, 'tiers': [[446, 25, 500], [966, 37, 300], [None, 16, 0]], 'mode': 'volume', 'included': 100, 'active_days': 26, 'period_days': 31}, [83, 0]), ('regression', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 480, 'tiers': [[297, 7, 500], [709, 18, 0], [None, 22, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 18, 'period_days': 30}, [600, 0]), ('partial-repair probe', {'usage': 74, 'tiers': [[471, 38, 500], [754, 8, 300], [None, 3, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 16, 'period_days': 30}, [533, 0]), ('normal control', {'usage': 22, 'tiers': [[271, 40, 0], [731, 39, 0], [None, 19, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 23, 'period_days': 30}, [191, 0]), ('normal control', {'usage': 1383, 'tiers': [[285, 7, 0], [550, 30, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 24, 'period_days': 31}, [193, 25305]), ('normal control', {'usage': 77, 'tiers': [[278, 41, 0], [1269, 3, 0], [None, 6, 0]], 'mode': 'volume', 'included': 100, 'active_days': 13, 'period_days': 30}, [43, 1394]), ('normal control', {'usage': 340, 'tiers': [[481, 48, 0], [1132, 35, 300], [None, 6, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 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	`[241, 500]`	`[241, 0]`	Failed
regression 1	`[225, 500]`	`[225, 0]`	Failed
partial-repair probe 2	`[800, 500]`	`[800, 0]`	Failed
partial-repair probe 3	`[733, 500]`	`[733, 0]`	Failed
normal control 4	`[900, 0]`	`[900, 0]`	Passed
normal control 5	`[8, 64368]`	`[8, 64368]`	Passed
normal control 6	`[0, 7995]`	`[0, 7995]`	Passed
normal control 7	`[0, 2378]`	`[0, 2378]`	Passed

SHA-256 / f8b08d4e9b2a1202e26328551118c1f9a7ce3fe095be7784873bde2ffbe98bff

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    allow = x['included'] * x['active_days'] // x['period_days']
    units = max(0, x['usage'] - allow)
    if x['usage'] == 0:
        return [allow, 0]
    if x['mode'] == 'volume':
        for up, price, flat in x['tiers']:
            if up is None or units <= up:
                return [allow, units * price + flat]
    cost = 0
    prev = 0
    for up, price, flat in x['tiers']:
        top = units if up is None else min(units, up)
        if top > prev:
            cost += (top - prev) * price + flat
        if up is None or units <= up:
            break
        prev = up
    return [allow, cost]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'usage': 129, 'tiers': [[129, 9, 500], [845, 8, 300], [None, 18, 0]], 'mode': 'volume', 'included': 250, 'active_days': 29, 'period_days': 30}, [241, 0]), ('regression', {'usage': 59, 'tiers': [[302, 10, 500], [914, 15, 0], [None, 7, 0]], 'mode': 'volume', 'included': 250, 'active_days': 27, 'period_days': 30}, [225, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 215, 'tiers': [[215, 32, 0], [1030, 38, 300], [None, 21, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('normal control', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('normal control', {'usage': 195, 'tiers': [[263, 41, 0], [444, 16, 300], [None, 18, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 31}, [0, 7995]), ('normal control', {'usage': 149, 'tiers': [[96, 6, 0], [296, 34, 0], [None, 9, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 27, 'period_days': 30}, [0, 2378])], [('regression', {'usage': 59, 'tiers': [[302, 10, 500], [914, 15, 0], [None, 7, 0]], 'mode': 'volume', 'included': 250, 'active_days': 27, 'period_days': 30}, [225, 0]), ('regression', {'usage': 0, 'tiers': [[261, 18, 500], [782, 5, 300], [None, 25, 0]], 'mode': 'volume', 'included': 100, 'active_days': 14, 'period_days': 30}, [46, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 132, 'tiers': [[102, 43, 500], [1090, 39, 300], [None, 1, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 31, 'period_days': 31}, [100, 1876]), ('normal control', {'usage': 1908, 'tiers': [[438, 11, 500], [1328, 8, 0], [None, 10, 0]], 'mode': 'volume', 'included': 0, 'active_days': 3, 'period_days': 31}, [0, 19080]), ('normal control', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0])], [('regression', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('regression', {'usage': 0, 'tiers': [[261, 18, 500], [782, 5, 300], [None, 25, 0]], 'mode': 'volume', 'included': 100, 'active_days': 14, 'period_days': 30}, [46, 0]), ('partial-repair probe', {'usage': 71, 'tiers': [[446, 25, 500], [966, 37, 300], [None, 16, 0]], 'mode': 'volume', 'included': 100, 'active_days': 26, 'period_days': 31}, [83, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[164, 9, 500], [362, 20, 0], [None, 12, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('normal control', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('normal control', {'usage': 47, 'tiers': [[264, 39, 0], [1255, 6, 0], [None, 12, 0]], 'mode': 'volume', 'included': 250, 'active_days': 24, 'period_days': 30}, [200, 0]), ('normal control', {'usage': 31, 'tiers': [[57, 14, 0], [997, 11, 300], [None, 7, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 3, 'period_days': 30}, [100, 0]), ('normal control', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160])], [('regression', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('regression', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 121, 'tiers': [[92, 15, 500], [339, 6, 300], [None, 14, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 26, 'period_days': 31}, [838, 0]), ('partial-repair probe', {'usage': 60, 'tiers': [[268, 25, 500], [1138, 32, 0], [None, 4, 0]], 'mode': 'volume', 'included': 250, 'active_days': 10, 'period_days': 30}, [83, 0]), ('normal control', {'usage': 1862, 'tiers': [[90, 6, 0], [1073, 39, 0], [None, 20, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 29, 'period_days': 31}, [93, 52797]), ('normal control', {'usage': 2285, 'tiers': [[395, 26, 500], [1142, 40, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 13, 'period_days': 30}, [0, 68082]), ('normal control', {'usage': 205, 'tiers': [[205, 36, 0], [1123, 17, 300], [None, 4, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 16, 'period_days': 31}, [516, 0]), ('normal control', {'usage': 286, 'tiers': [[286, 31, 500], [1169, 13, 0], [None, 22, 0]], 'mode': 'volume', 'included': 250, 'active_days': 30, 'period_days': 31}, [241, 1895])], [('regression', {'usage': 71, 'tiers': [[446, 25, 500], [966, 37, 300], [None, 16, 0]], 'mode': 'volume', 'included': 100, 'active_days': 26, 'period_days': 31}, [83, 0]), ('regression', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 480, 'tiers': [[297, 7, 500], [709, 18, 0], [None, 22, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 18, 'period_days': 30}, [600, 0]), ('partial-repair probe', {'usage': 74, 'tiers': [[471, 38, 500], [754, 8, 300], [None, 3, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 16, 'period_days': 30}, [533, 0]), ('normal control', {'usage': 22, 'tiers': [[271, 40, 0], [731, 39, 0], [None, 19, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 23, 'period_days': 30}, [191, 0]), ('normal control', {'usage': 1383, 'tiers': [[285, 7, 0], [550, 30, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 24, 'period_days': 31}, [193, 25305]), ('normal control', {'usage': 77, 'tiers': [[278, 41, 0], [1269, 3, 0], [None, 6, 0]], 'mode': 'volume', 'included': 100, 'active_days': 13, 'period_days': 30}, [43, 1394]), ('normal control', {'usage': 340, 'tiers': [[481, 48, 0], [1132, 35, 300], [None, 6, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 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	`[241, 500]`	`[241, 0]`	Failed
regression 1	`[225, 500]`	`[225, 0]`	Failed
partial-repair probe 2	`[800, 500]`	`[800, 0]`	Failed
partial-repair probe 3	`[733, 500]`	`[733, 0]`	Failed
normal control 4	`[900, 0]`	`[900, 0]`	Passed
normal control 5	`[8, 64368]`	`[8, 64368]`	Passed
normal control 6	`[0, 7995]`	`[0, 7995]`	Passed
normal control 7	`[0, 2378]`	`[0, 2378]`	Passed

SHA-256 / 306ff03ec32223a13bbbd46c6bcf558f331eaedccddfcfa92ae2458d894956d3

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(x):
    allow = x['included'] * x['active_days'] // x['period_days']
    units = max(0, x['usage'] - allow)
    if units == 0:
        return [allow, 0]
    if x['mode'] == 'volume':
        for up, price, flat in x['tiers']:
            if up is None or units <= up:
                return [allow, units * price + flat]
    cost = 0
    prev = 0
    for up, price, flat in x['tiers']:
        top = units if up is None else min(units, up)
        if top > prev:
            cost += (top - prev) * price + flat
        if up is None or units <= up:
            break
        prev = up
    return [allow, cost]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'usage': 129, 'tiers': [[129, 9, 500], [845, 8, 300], [None, 18, 0]], 'mode': 'volume', 'included': 250, 'active_days': 29, 'period_days': 30}, [241, 0]), ('regression', {'usage': 59, 'tiers': [[302, 10, 500], [914, 15, 0], [None, 7, 0]], 'mode': 'volume', 'included': 250, 'active_days': 27, 'period_days': 30}, [225, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 215, 'tiers': [[215, 32, 0], [1030, 38, 300], [None, 21, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('normal control', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('normal control', {'usage': 195, 'tiers': [[263, 41, 0], [444, 16, 300], [None, 18, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 31}, [0, 7995]), ('normal control', {'usage': 149, 'tiers': [[96, 6, 0], [296, 34, 0], [None, 9, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 27, 'period_days': 30}, [0, 2378])], [('regression', {'usage': 59, 'tiers': [[302, 10, 500], [914, 15, 0], [None, 7, 0]], 'mode': 'volume', 'included': 250, 'active_days': 27, 'period_days': 30}, [225, 0]), ('regression', {'usage': 0, 'tiers': [[261, 18, 500], [782, 5, 300], [None, 25, 0]], 'mode': 'volume', 'included': 100, 'active_days': 14, 'period_days': 30}, [46, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('normal control', {'usage': 132, 'tiers': [[102, 43, 500], [1090, 39, 300], [None, 1, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 31, 'period_days': 31}, [100, 1876]), ('normal control', {'usage': 1908, 'tiers': [[438, 11, 500], [1328, 8, 0], [None, 10, 0]], 'mode': 'volume', 'included': 0, 'active_days': 3, 'period_days': 31}, [0, 19080]), ('normal control', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0])], [('regression', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('regression', {'usage': 0, 'tiers': [[261, 18, 500], [782, 5, 300], [None, 25, 0]], 'mode': 'volume', 'included': 100, 'active_days': 14, 'period_days': 30}, [46, 0]), ('partial-repair probe', {'usage': 71, 'tiers': [[446, 25, 500], [966, 37, 300], [None, 16, 0]], 'mode': 'volume', 'included': 100, 'active_days': 26, 'period_days': 31}, [83, 0]), ('partial-repair probe', {'usage': 175, 'tiers': [[164, 9, 500], [362, 20, 0], [None, 12, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('normal control', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('normal control', {'usage': 47, 'tiers': [[264, 39, 0], [1255, 6, 0], [None, 12, 0]], 'mode': 'volume', 'included': 250, 'active_days': 24, 'period_days': 30}, [200, 0]), ('normal control', {'usage': 31, 'tiers': [[57, 14, 0], [997, 11, 300], [None, 7, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 3, 'period_days': 30}, [100, 0]), ('normal control', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160])], [('regression', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('regression', {'usage': 175, 'tiers': [[281, 41, 500], [419, 21, 0], [None, 9, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 24, 'period_days': 30}, [800, 0]), ('partial-repair probe', {'usage': 121, 'tiers': [[92, 15, 500], [339, 6, 300], [None, 14, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 26, 'period_days': 31}, [838, 0]), ('partial-repair probe', {'usage': 60, 'tiers': [[268, 25, 500], [1138, 32, 0], [None, 4, 0]], 'mode': 'volume', 'included': 250, 'active_days': 10, 'period_days': 30}, [83, 0]), ('normal control', {'usage': 1862, 'tiers': [[90, 6, 0], [1073, 39, 0], [None, 20, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 29, 'period_days': 31}, [93, 52797]), ('normal control', {'usage': 2285, 'tiers': [[395, 26, 500], [1142, 40, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 13, 'period_days': 30}, [0, 68082]), ('normal control', {'usage': 205, 'tiers': [[205, 36, 0], [1123, 17, 300], [None, 4, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 16, 'period_days': 31}, [516, 0]), ('normal control', {'usage': 286, 'tiers': [[286, 31, 500], [1169, 13, 0], [None, 22, 0]], 'mode': 'volume', 'included': 250, 'active_days': 30, 'period_days': 31}, [241, 1895])], [('regression', {'usage': 71, 'tiers': [[446, 25, 500], [966, 37, 300], [None, 16, 0]], 'mode': 'volume', 'included': 100, 'active_days': 26, 'period_days': 31}, [83, 0]), ('regression', {'usage': 116, 'tiers': [[56, 12, 500], [335, 14, 300], [None, 2, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 480, 'tiers': [[297, 7, 500], [709, 18, 0], [None, 22, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 18, 'period_days': 30}, [600, 0]), ('partial-repair probe', {'usage': 74, 'tiers': [[471, 38, 500], [754, 8, 300], [None, 3, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 16, 'period_days': 30}, [533, 0]), ('normal control', {'usage': 22, 'tiers': [[271, 40, 0], [731, 39, 0], [None, 19, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 23, 'period_days': 30}, [191, 0]), ('normal control', {'usage': 1383, 'tiers': [[285, 7, 0], [550, 30, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 24, 'period_days': 31}, [193, 25305]), ('normal control', {'usage': 77, 'tiers': [[278, 41, 0], [1269, 3, 0], [None, 6, 0]], 'mode': 'volume', 'included': 100, 'active_days': 13, 'period_days': 30}, [43, 1394]), ('normal control', {'usage': 340, 'tiers': [[481, 48, 0], [1132, 35, 300], [None, 6, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 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	`[241, 0]`	`[241, 0]`	Passed
regression 1	`[225, 0]`	`[225, 0]`	Passed
partial-repair probe 2	`[800, 0]`	`[800, 0]`	Passed
partial-repair probe 3	`[733, 0]`	`[733, 0]`	Passed
normal control 4	`[900, 0]`	`[900, 0]`	Passed
normal control 5	`[8, 64368]`	`[8, 64368]`	Passed
normal control 6	`[0, 7995]`	`[0, 7995]`	Passed
normal control 7	`[0, 2378]`	`[0, 2378]`	Passed

SHA-256 / 8827bd1fda2c42450a06ca04596703332de8458032645a820bac22a711a8d107

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 2026-09-29T14:46:36.890488+00:00.

Case digest / 3518da14b847664e47070a5cda2091044b83758e13946d2d81b6713fa8d97db6