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FA-59416 / Subscription proration billing / Open access

Seat additions billed above the high-water mark: renewal seat count · case 01

Renewal invoices bill the peak seat count even after seats were removed.

Verified by executionVariant 1 · 9 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The renewal quantity uses the high-water mark instead of the final seat count.

THE FAILURE

The renewal quantity uses the high-water mark instead of the final seat count.

Unsuccessful approach: The attempt uses the last change in log order rather than chronological order.

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
        current = count
    final = hwm
    return [charge, hwm, final]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('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': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('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': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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': 1500, 'period_days': 31, 'base_seats': 6, 'changes': []}, [0, 6, 6])], [('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': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25]), ('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]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 1, 'changes': []}, [0, 1, 1])], [('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': 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': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('partial-repair probe', {'price': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('normal control', {'price': 1500, 'period_days': 365, 'base_seats': 10, 'changes': [[41, 3], [46, 0], [12, 3], [315, 24], [41, 24]]}, [18641, 24, 24]), ('normal control', {'price': 1500, 'period_days': 30, 'base_seats': 1, 'changes': [[18, 0], [27, 5]]}, [600, 5, 5]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 1, 'changes': [[19, 14], [24, 18]]}, [7123, 18, 18])], [('regression', {'price': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 800, 'period_days': 30, 'base_seats': 8, 'changes': [[19, 24], [23, 8], [10, 24], [12, 12]]}, [8533, 24, 8]), ('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]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('regression', {'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': 1500, 'period_days': 28, 'base_seats': 7, 'changes': [[17, 25], [9, 11], [4, 16], [3, 0], [0, 15]]}, [18590, 25, 25]), ('partial-repair probe', {'price': 800, 'period_days': 30, 'base_seats': 14, 'changes': [[5, 14], [28, 7], [18, 5], [7, 10], [5, 4]]}, [0, 14, 7]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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 fixtureActualExpectedOutcome
regression 0[7100, 20, 20][7100, 20, 3]Failed
regression 1[0, 13, 13][0, 13, 1]Failed
partial-repair probe 2[8640, 20, 20][8640, 20, 1]Failed
partial-repair probe 3[9143, 25, 25][9143, 25, 25]Passed
boundary control 4[1200, 7, 7][1200, 7, 7]Passed
normal control 5[0, 8, 8][0, 8, 8]Passed
normal control 6[0, 11, 11][0, 11, 11]Passed
normal control 7[0, 12, 12][0, 12, 12]Passed
normal control 8[0, 6, 6][0, 6, 6]Passed

SHA-256 / 582bdaef8401369e00b15c3d789407cd312fe0d515081c7fb1367d91ba872471

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
        current = count
    final = x['changes'][-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', {'price': 1500, 'period_days': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('regression', {'price': 800, 'period_days': 30, 'base_seats': 13, 'changes': [[15, 1]]}, [0, 13, 1]), ('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': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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': 1500, 'period_days': 31, 'base_seats': 6, 'changes': []}, [0, 6, 6])], [('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': 30, 'base_seats': 9, 'changes': [[19, 10], [22, 3], [12, 12], [24, 3], [19, 20]]}, [7100, 20, 3]), ('partial-repair probe', {'price': 800, 'period_days': 28, 'base_seats': 6, 'changes': [[27, 25], [1, 17], [12, 18]]}, [9143, 25, 25]), ('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]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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]), ('normal control', {'price': 800, 'period_days': 30, 'base_seats': 1, 'changes': []}, [0, 1, 1])], [('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': 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': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('partial-repair probe', {'price': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('normal control', {'price': 1500, 'period_days': 365, 'base_seats': 10, 'changes': [[41, 3], [46, 0], [12, 3], [315, 24], [41, 24]]}, [18641, 24, 24]), ('normal control', {'price': 1500, 'period_days': 30, 'base_seats': 1, 'changes': [[18, 0], [27, 5]]}, [600, 5, 5]), ('normal control', {'price': 800, 'period_days': 28, 'base_seats': 11, 'changes': []}, [0, 11, 11]), ('normal control', {'price': 1200, 'period_days': 31, 'base_seats': 1, 'changes': [[19, 14], [24, 18]]}, [7123, 18, 18])], [('regression', {'price': 5820, 'period_days': 365, 'base_seats': 15, 'changes': [[14, 25], [190, 23], [339, 10], [22, 7], [14, 1]]}, [55968, 25, 10]), ('regression', {'price': 1500, 'period_days': 28, 'base_seats': 10, 'changes': [[1, 16], [20, 13]]}, [8679, 16, 13]), ('partial-repair probe', {'price': 800, 'period_days': 30, 'base_seats': 8, 'changes': [[19, 24], [23, 8], [10, 24], [12, 12]]}, [8533, 24, 8]), ('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]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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': 2726, 'period_days': 365, 'base_seats': 15, 'changes': [[171, 14], [284, 12], [141, 18], [171, 5]]}, [5019, 18, 12]), ('regression', {'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': 1500, 'period_days': 28, 'base_seats': 7, 'changes': [[17, 25], [9, 11], [4, 16], [3, 0], [0, 15]]}, [18590, 25, 25]), ('partial-repair probe', {'price': 800, 'period_days': 30, 'base_seats': 14, 'changes': [[5, 14], [28, 7], [18, 5], [7, 10], [5, 4]]}, [0, 14, 7]), ('boundary control', {'price': 1000, 'period_days': 30, 'base_seats': 5, 'changes': [[3, 3], [10, 5], [12, 7]]}, [1200, 7, 7]), ('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 fixtureActualExpectedOutcome
regression 0[7100, 20, 20][7100, 20, 3]Failed
regression 1[0, 13, 1][0, 13, 1]Passed
partial-repair probe 2[8640, 20, 11][8640, 20, 1]Failed
partial-repair probe 3[9143, 25, 18][9143, 25, 25]Failed
boundary control 4[1200, 7, 7][1200, 7, 7]Passed
normal control 5[0, 8, 8][0, 8, 8]Passed
normal control 6[0, 11, 11][0, 11, 11]Passed
normal control 7[0, 12, 12][0, 12, 12]Passed
normal control 8[0, 6, 6][0, 6, 6]Passed

SHA-256 / e85b0a69f5d3206c5fd3c07ae66665c3eaf5032bf55847ec35151be1e081fd32

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 9 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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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 2026-09-29T14:46:36.179676+00:00.

Case digest / 9935db694aa24876bdb499747c748c0ab160c57cea6717b37f857a53e954cd10