FA-59406 / Subscription proration billing / Open access

Seat additions billed above the high-water mark: no mid-cycle removal credit · case 01

Removing seats mid-cycle reduces the invoice although the contract grants credit only at renewal.

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

ROOT CAUSE

Seat decreases generate prorated credits.

VERIFIED REPAIR

Restore the contract rule at the no mid-cycle removal credit step: use `current = count`.

Unsuccessful approach: The attempt credits a full period price for seats dropped below the base count.

Case contract

Input {price per seat per period, period_days, base_seats, changes: [[day, seat_count]]}. Changes apply in day order (same-day changes keep input order). Seats above the period high-water mark are charged price*added*(period_days - day)/period_days half-up; decreases earn no mid-cycle credit. Return [prorated charges, high-water mark, renewal seat count (the last count)].

Why this case matters

Seat-based billing charges each seat at most once per period, even when seats are removed and re-added.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(x):
    ch = sorted(x['changes'], key=lambda c: c[0])
    hwm = current = x['base_seats']
    charge = 0
    for day, count in ch:
        if count > hwm:
            added = count - hwm
            left = x['period_days'] - day
            charge += (x['price'] * added * left * 2 + x['period_days']) // (2 * x['period_days'])
            hwm = count
        if count < current:
            charge -= (x['price'] * (current - count) * (x['period_days'] - day) * 2 + x['period_days']) // (2 * x['period_days'])
        current = count
    final = ch[-1][1] if ch else x['base_seats']
    return [charge, hwm, final]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('normal control', {'price': 800, 'period_days': 31, 'base_seats': 8, 'changes': []}, [0, 8, 8]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 5360, 'period_days': 28, 'base_seats': 12, 'changes': []}, [0, 12, 12]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25])], [('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('regression', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('normal control', {'price': 1500, 'period_days': 31, 'base_seats': 6, 'changes': []}, [0, 6, 6]), ('normal control', {'price': 800, 'period_days': 365, 'base_seats': 15, 'changes': []}, [0, 15, 15]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 7, 'changes': []}, [0, 7, 7]), ('normal control', {'price': 1200, 'period_days': 30, 'base_seats': 13, 'changes': []}, [0, 13, 13])], [('regression', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('regression', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('partial-repair probe', {'price': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 1, 'changes': []}, [0, 1, 1]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 4, 'changes': [[333, 15], [352, 23], [4, 5]]}, [2581, 23, 23]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 1, 'changes': [[19, 14], [24, 18]]}, [7123, 18, 18])], [('regression', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 7507, 'period_days': 31, 'base_seats': 15, 'changes': [[15, 8]]}, [0, 15, 8]), ('partial-repair probe', {'price': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 4, 'changes': [[21, 22], [29, 24]]}, [4373, 24, 24]), ('normal control', {'price': 6240, 'period_days': 28, 'base_seats': 1, 'changes': []}, [0, 1, 1]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 2, 'changes': []}, [0, 2, 2]), ('normal control', {'price': 1200, 'period_days': 28, 'base_seats': 9, 'changes': []}, [0, 9, 9])], [('regression', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 800, 'period_days': 31, 'base_seats': 7, 'changes': [[14, 11], [13, 12], [19, 0], [30, 25], [23, 8]]}, [2658, 25, 25]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 2, 'changes': [[26, 6], [4, 0], [26, 2]]}, [429, 6, 2]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 2, 'changes': [[3, 10]]}, [8671, 10, 10]), ('normal control', {'price': 8587, 'period_days': 30, 'base_seats': 12, 'changes': []}, [0, 12, 12]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 8445, 'period_days': 365, 'base_seats': 1, 'changes': [[278, 2], [278, 7]]}, [12078, 7, 7])]]
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 (boundary) 0	`[-600, 7, 7]`	`[1200, 7, 7]`	Failed
regression 1	`[-4800, 13, 1]`	`[0, 13, 1]`	Failed
partial-repair probe 2	`[-800, 20, 3]`	`[7100, 20, 3]`	Failed
partial-repair probe 3	`[-9360, 20, 1]`	`[8640, 20, 1]`	Failed
normal control 4	`[0, 8, 8]`	`[0, 8, 8]`	Passed
normal control 5	`[0, 11, 11]`	`[0, 11, 11]`	Passed
normal control 6	`[0, 12, 12]`	`[0, 12, 12]`	Passed
normal control 7	`[9143, 25, 25]`	`[9143, 25, 25]`	Passed

SHA-256 / 2e8d5753b4a2b5ba3ba0b97ec660ce00aa0f3b04a24c2b1e148fa0caa6829ed7

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    ch = sorted(x['changes'], key=lambda c: c[0])
    hwm = current = x['base_seats']
    charge = 0
    for day, count in ch:
        if count > hwm:
            added = count - hwm
            left = x['period_days'] - day
            charge += (x['price'] * added * left * 2 + x['period_days']) // (2 * x['period_days'])
            hwm = count
        if count < x['base_seats']:
            charge -= x['price'] * (x['base_seats'] - count)
        current = count
    final = ch[-1][1] if ch else x['base_seats']
    return [charge, hwm, final]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('normal control', {'price': 800, 'period_days': 31, 'base_seats': 8, 'changes': []}, [0, 8, 8]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 5360, 'period_days': 28, 'base_seats': 12, 'changes': []}, [0, 12, 12]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25])], [('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('regression', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('normal control', {'price': 1500, 'period_days': 31, 'base_seats': 6, 'changes': []}, [0, 6, 6]), ('normal control', {'price': 800, 'period_days': 365, 'base_seats': 15, 'changes': []}, [0, 15, 15]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 7, 'changes': []}, [0, 7, 7]), ('normal control', {'price': 1200, 'period_days': 30, 'base_seats': 13, 'changes': []}, [0, 13, 13])], [('regression', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('regression', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('partial-repair probe', {'price': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 1, 'changes': []}, [0, 1, 1]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 4, 'changes': [[333, 15], [352, 23], [4, 5]]}, [2581, 23, 23]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 1, 'changes': [[19, 14], [24, 18]]}, [7123, 18, 18])], [('regression', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 7507, 'period_days': 31, 'base_seats': 15, 'changes': [[15, 8]]}, [0, 15, 8]), ('partial-repair probe', {'price': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 4, 'changes': [[21, 22], [29, 24]]}, [4373, 24, 24]), ('normal control', {'price': 6240, 'period_days': 28, 'base_seats': 1, 'changes': []}, [0, 1, 1]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 2, 'changes': []}, [0, 2, 2]), ('normal control', {'price': 1200, 'period_days': 28, 'base_seats': 9, 'changes': []}, [0, 9, 9])], [('regression', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 800, 'period_days': 31, 'base_seats': 7, 'changes': [[14, 11], [13, 12], [19, 0], [30, 25], [23, 8]]}, [2658, 25, 25]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 2, 'changes': [[26, 6], [4, 0], [26, 2]]}, [429, 6, 2]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 2, 'changes': [[3, 10]]}, [8671, 10, 10]), ('normal control', {'price': 8587, 'period_days': 30, 'base_seats': 12, 'changes': []}, [0, 12, 12]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 8445, 'period_days': 365, 'base_seats': 1, 'changes': [[278, 2], [278, 7]]}, [12078, 7, 7])]]
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 (boundary) 0	`[-800, 7, 7]`	`[1200, 7, 7]`	Failed
regression 1	`[-9600, 13, 1]`	`[0, 13, 1]`	Failed
partial-repair probe 2	`[-10900, 20, 3]`	`[7100, 20, 3]`	Failed
partial-repair probe 3	`[-14160, 20, 1]`	`[8640, 20, 1]`	Failed
normal control 4	`[0, 8, 8]`	`[0, 8, 8]`	Passed
normal control 5	`[0, 11, 11]`	`[0, 11, 11]`	Passed
normal control 6	`[0, 12, 12]`	`[0, 12, 12]`	Passed
normal control 7	`[9143, 25, 25]`	`[9143, 25, 25]`	Passed

SHA-256 / 2468f1efeb18b85d4df3d57ac13dfe157e02d6c68b913dfeb7fd86410f4acd52

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(x):
    ch = sorted(x['changes'], key=lambda c: c[0])
    hwm = current = x['base_seats']
    charge = 0
    for day, count in ch:
        if count > hwm:
            added = count - hwm
            left = x['period_days'] - day
            charge += (x['price'] * added * left * 2 + x['period_days']) // (2 * x['period_days'])
            hwm = count
        current = count
    final = ch[-1][1] if ch else x['base_seats']
    return [charge, hwm, final]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('normal control', {'price': 800, 'period_days': 31, 'base_seats': 8, 'changes': []}, [0, 8, 8]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 5360, 'period_days': 28, 'base_seats': 12, 'changes': []}, [0, 12, 12]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25])], [('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('regression', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('normal control', {'price': 1500, 'period_days': 31, 'base_seats': 6, 'changes': []}, [0, 6, 6]), ('normal control', {'price': 800, 'period_days': 365, 'base_seats': 15, 'changes': []}, [0, 15, 15]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 7, 'changes': []}, [0, 7, 7]), ('normal control', {'price': 1200, 'period_days': 30, 'base_seats': 13, 'changes': []}, [0, 13, 13])], [('regression', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('regression', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('partial-repair probe', {'price': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 1, 'changes': []}, [0, 1, 1]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 4, 'changes': [[333, 15], [352, 23], [4, 5]]}, [2581, 23, 23]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 1, 'changes': [[19, 14], [24, 18]]}, [7123, 18, 18])], [('regression', {'price': 1200, 'period_days': 30, 'base_seats': 11, 'changes': [[6, 20], [12, 8], [15, 1], [7, 5], [0, 11]]}, [8640, 20, 1]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 7507, 'period_days': 31, 'base_seats': 15, 'changes': [[15, 8]]}, [0, 15, 8]), ('partial-repair probe', {'price': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 4, 'changes': [[21, 22], [29, 24]]}, [4373, 24, 24]), ('normal control', {'price': 6240, 'period_days': 28, 'base_seats': 1, 'changes': []}, [0, 1, 1]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 2, 'changes': []}, [0, 2, 2]), ('normal control', {'price': 1200, 'period_days': 28, 'base_seats': 9, 'changes': []}, [0, 9, 9])], [('regression', {'price': 1500, 'period_days': 28, 'base_seats': 12, 'changes': [[0, 4], [0, 9], [6, 24], [23, 1], [9, 21]]}, [14143, 24, 1]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 800, 'period_days': 31, 'base_seats': 7, 'changes': [[14, 11], [13, 12], [19, 0], [30, 25], [23, 8]]}, [2658, 25, 25]), ('partial-repair probe', {'price': 1500, 'period_days': 28, 'base_seats': 2, 'changes': [[26, 6], [4, 0], [26, 2]]}, [429, 6, 2]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 2, 'changes': [[3, 10]]}, [8671, 10, 10]), ('normal control', {'price': 8587, 'period_days': 30, 'base_seats': 12, 'changes': []}, [0, 12, 12]), ('normal control', {'price': 1200, 'period_days': 365, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 8445, 'period_days': 365, 'base_seats': 1, 'changes': [[278, 2], [278, 7]]}, [12078, 7, 7])]]
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 (boundary) 0	`[1200, 7, 7]`	`[1200, 7, 7]`	Passed
regression 1	`[0, 13, 1]`	`[0, 13, 1]`	Passed
partial-repair probe 2	`[7100, 20, 3]`	`[7100, 20, 3]`	Passed
partial-repair probe 3	`[8640, 20, 1]`	`[8640, 20, 1]`	Passed
normal control 4	`[0, 8, 8]`	`[0, 8, 8]`	Passed
normal control 5	`[0, 11, 11]`	`[0, 11, 11]`	Passed
normal control 6	`[0, 12, 12]`	`[0, 12, 12]`	Passed
normal control 7	`[9143, 25, 25]`	`[9143, 25, 25]`	Passed

SHA-256 / a91a3f4ceea0609c370e5c1cd79c7a2d03c54331ae8a617f721e3a659022d57c

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 2026-09-29T14:46:36.062171+00:00.

Case digest / c2e03d0e852c75ef5ed8babdc9bcb994447324c09c7d86e8a5277948e3850e77