FA-59686 / Subscription proration billing / Open access

Volume discount tiers on quantity changes: tier for each quantity · case 01

Customers adding seats across a tier boundary keep the old tier's discount.

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

ROOT CAUSE

The discount tier is chosen from the old quantity for both lines.

VERIFIED REPAIR

Restore the contract rule at the tier for each quantity step: use `return q * x['unit'] * (100 - tier_pct(q))`.

Unsuccessful approach: The attempt uses the larger quantity's tier, so downgrades keep the higher discount.

Case contract

Input {unit, old, new quantities, tiers: ascending [[min_qty, pct_off]], left, period}. A quantity's discount is the pct of the last tier with qty >= min_qty and applies to all units: total(q) = q*unit*(100-pct) in cent-percent. Proration of q = total(q)*left/(100*period) half-up. Return [-proration(old), proration(new)].

Why this case matters

Crossing a volume tier on a mid-cycle quantity change re-prices every unit for the rest of the period.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(x):
    def tier_pct(q):
        pct = 0
        for mn, off in x['tiers']:
            if q >= mn:
                pct = off
        return pct
    def total(q):
        return q * x['unit'] * (100 - tier_pct(x['old']))
    def pr(q):
        return (total(q) * x['left'] * 2 + 100 * x['period']) // (200 * x['period'])
    return [-pr(x['old']), pr(x['new'])]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'unit': 999, 'old': 18, 'new': 1, 'tiers': [[1, 0], [8, 10], [28, 15]], 'left': 25, 'period': 31}, [-13051, 806]), ('regression', {'unit': 1396, 'old': 18, 'new': 39, 'tiers': [[1, 0], [5, 10], [31, 25]], 'left': 21, 'period': 31}, [-15320, 27661]), ('partial-repair probe', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('partial-repair probe', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('normal control', {'unit': 1500, 'old': 22, 'new': 32, 'tiers': [[1, 0], [5, 5], [35, 25]], 'left': 9, 'period': 31}, [-9102, 13239]), ('normal control', {'unit': 1500, 'old': 26, 'new': 40, 'tiers': [[1, 0], [7, 10], [26, 20]], 'left': 20, 'period': 31}, [-20129, 30968]), ('normal control', {'unit': 1000, 'old': 5, 'new': 17, 'tiers': [[1, 0], [5, 5], [37, 25]], 'left': 10, 'period': 31}, [-1532, 5210]), ('normal control', {'unit': 1000, 'old': 27, 'new': 46, 'tiers': [[1, 0], [9, 5], [20, 15]], 'left': 4, 'period': 31}, [-2961, 5045])], [('regression', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('regression', {'unit': 999, 'old': 18, 'new': 1, 'tiers': [[1, 0], [8, 10], [28, 15]], 'left': 25, 'period': 31}, [-13051, 806]), ('partial-repair probe', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('partial-repair probe', {'unit': 1500, 'old': 38, 'new': 12, 'tiers': [[1, 0], [8, 10], [31, 25]], 'left': 15, 'period': 31}, [-20685, 7839]), ('normal control', {'unit': 1000, 'old': 18, 'new': 17, 'tiers': [[1, 0], [10, 10], [32, 25]], 'left': 24, 'period': 31}, [-12542, 11845]), ('normal control', {'unit': 1000, 'old': 41, 'new': 30, 'tiers': [[1, 0], [5, 5], [20, 15]], 'left': 23, 'period': 31}, [-25856, 18919]), ('normal control', {'unit': 1000, 'old': 1, 'new': 2, 'tiers': [[1, 0], [10, 5], [21, 20]], 'left': 30, 'period': 31}, [-968, 1935]), ('normal control', {'unit': 1500, 'old': 42, 'new': 42, 'tiers': [[1, 0], [9, 10], [33, 25]], 'left': 30, 'period': 31}, [-45726, 45726])], [('regression', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('regression', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('partial-repair probe', {'unit': 4525, 'old': 44, 'new': 17, 'tiers': [[1, 0], [5, 5], [26, 15]], 'left': 7, 'period': 31}, [-38214, 16502]), ('partial-repair probe', {'unit': 1000, 'old': 48, 'new': 14, 'tiers': [[1, 0], [9, 5], [31, 20]], 'left': 21, 'period': 31}, [-26013, 9010]), ('normal control', {'unit': 1500, 'old': 45, 'new': 35, 'tiers': [[1, 0], [9, 10], [25, 25]], 'left': 26, 'period': 31}, [-42460, 33024]), ('normal control', {'unit': 1000, 'old': 27, 'new': 21, 'tiers': [[1, 0], [8, 10], [36, 15]], 'left': 24, 'period': 31}, [-18813, 14632]), ('normal control', {'unit': 999, 'old': 24, 'new': 6, 'tiers': [[1, 0], [5, 10], [35, 15]], 'left': 27, 'period': 31}, [-18794, 4699]), ('normal control', {'unit': 999, 'old': 37, 'new': 45, 'tiers': [[1, 0], [9, 10], [25, 20]], 'left': 31, 'period': 31}, [-29570, 35964])], [('regression', {'unit': 1500, 'old': 38, 'new': 12, 'tiers': [[1, 0], [8, 10], [31, 25]], 'left': 15, 'period': 31}, [-20685, 7839]), ('regression', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('partial-repair probe', {'unit': 999, 'old': 14, 'new': 7, 'tiers': [[1, 0], [8, 10], [27, 25]], 'left': 24, 'period': 31}, [-9745, 5414]), ('partial-repair probe', {'unit': 999, 'old': 39, 'new': 26, 'tiers': [[1, 0], [7, 10], [32, 15]], 'left': 27, 'period': 31}, [-28844, 20360]), ('normal control', {'unit': 3876, 'old': 16, 'new': 37, 'tiers': [[1, 0], [6, 10], [40, 20]], 'left': 14, 'period': 31}, [-25207, 58290]), ('normal control', {'unit': 1500, 'old': 32, 'new': 32, 'tiers': [[1, 0], [5, 5], [35, 20]], 'left': 25, 'period': 31}, [-36774, 36774]), ('normal control', {'unit': 1000, 'old': 22, 'new': 25, 'tiers': [[1, 0], [9, 10], [38, 15]], 'left': 13, 'period': 31}, [-8303, 9435]), ('normal control', {'unit': 999, 'old': 32, 'new': 41, 'tiers': [[1, 0], [5, 10], [23, 15]], 'left': 13, 'period': 31}, [-11395, 14600])], [('regression', {'unit': 4525, 'old': 44, 'new': 17, 'tiers': [[1, 0], [5, 5], [26, 15]], 'left': 7, 'period': 31}, [-38214, 16502]), ('regression', {'unit': 1500, 'old': 21, 'new': 45, 'tiers': [[1, 0], [7, 10], [29, 20]], 'left': 20, 'period': 31}, [-18290, 34839]), ('partial-repair probe', {'unit': 999, 'old': 10, 'new': 1, 'tiers': [[1, 0], [5, 10], [22, 15]], 'left': 29, 'period': 31}, [-8411, 935]), ('partial-repair probe', {'unit': 999, 'old': 21, 'new': 1, 'tiers': [[1, 0], [10, 5], [31, 20]], 'left': 9, 'period': 31}, [-5786, 290]), ('normal control', {'unit': 3669, 'old': 42, 'new': 45, 'tiers': [[1, 0], [8, 10], [31, 15]], 'left': 27, 'period': 31}, [-114082, 122231]), ('normal control', {'unit': 3542, 'old': 20, 'new': 15, 'tiers': [[1, 0], [10, 10], [31, 25]], 'left': 9, 'period': 31}, [-18510, 13882]), ('normal control', {'unit': 1500, 'old': 5, 'new': 29, 'tiers': [[1, 0], [5, 5], [38, 15]], 'left': 16, 'period': 31}, [-3677, 21329]), ('normal control', {'unit': 1500, 'old': 18, 'new': 18, 'tiers': [[1, 0], [9, 10], [31, 20]], 'left': 1, 'period': 31}, [-784, 784])]]
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	`[-13051, 725]`	`[-13051, 806]`	Failed
regression 1	`[-15320, 33193]`	`[-15320, 27661]`	Failed
partial-repair probe 2	`[-19783, 10159]`	`[-19783, 11354]`	Failed
partial-repair probe 3	`[-38646, 22440]`	`[-38646, 28424]`	Failed
normal control 4	`[-9102, 13239]`	`[-9102, 13239]`	Passed
normal control 5	`[-20129, 30968]`	`[-20129, 30968]`	Passed
normal control 6	`[-1532, 5210]`	`[-1532, 5210]`	Passed
normal control 7	`[-2961, 5045]`	`[-2961, 5045]`	Passed

SHA-256 / bcb79c89f06d1f02793dfbd7810e3275da01e3e8987d9dc0d64f06d48debebfc

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    def tier_pct(q):
        pct = 0
        for mn, off in x['tiers']:
            if q >= mn:
                pct = off
        return pct
    def total(q):
        return q * x['unit'] * (100 - tier_pct(max(q, x['old'])))
    def pr(q):
        return (total(q) * x['left'] * 2 + 100 * x['period']) // (200 * x['period'])
    return [-pr(x['old']), pr(x['new'])]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'unit': 999, 'old': 18, 'new': 1, 'tiers': [[1, 0], [8, 10], [28, 15]], 'left': 25, 'period': 31}, [-13051, 806]), ('regression', {'unit': 1396, 'old': 18, 'new': 39, 'tiers': [[1, 0], [5, 10], [31, 25]], 'left': 21, 'period': 31}, [-15320, 27661]), ('partial-repair probe', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('partial-repair probe', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('normal control', {'unit': 1500, 'old': 22, 'new': 32, 'tiers': [[1, 0], [5, 5], [35, 25]], 'left': 9, 'period': 31}, [-9102, 13239]), ('normal control', {'unit': 1500, 'old': 26, 'new': 40, 'tiers': [[1, 0], [7, 10], [26, 20]], 'left': 20, 'period': 31}, [-20129, 30968]), ('normal control', {'unit': 1000, 'old': 5, 'new': 17, 'tiers': [[1, 0], [5, 5], [37, 25]], 'left': 10, 'period': 31}, [-1532, 5210]), ('normal control', {'unit': 1000, 'old': 27, 'new': 46, 'tiers': [[1, 0], [9, 5], [20, 15]], 'left': 4, 'period': 31}, [-2961, 5045])], [('regression', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('regression', {'unit': 999, 'old': 18, 'new': 1, 'tiers': [[1, 0], [8, 10], [28, 15]], 'left': 25, 'period': 31}, [-13051, 806]), ('partial-repair probe', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('partial-repair probe', {'unit': 1500, 'old': 38, 'new': 12, 'tiers': [[1, 0], [8, 10], [31, 25]], 'left': 15, 'period': 31}, [-20685, 7839]), ('normal control', {'unit': 1000, 'old': 18, 'new': 17, 'tiers': [[1, 0], [10, 10], [32, 25]], 'left': 24, 'period': 31}, [-12542, 11845]), ('normal control', {'unit': 1000, 'old': 41, 'new': 30, 'tiers': [[1, 0], [5, 5], [20, 15]], 'left': 23, 'period': 31}, [-25856, 18919]), ('normal control', {'unit': 1000, 'old': 1, 'new': 2, 'tiers': [[1, 0], [10, 5], [21, 20]], 'left': 30, 'period': 31}, [-968, 1935]), ('normal control', {'unit': 1500, 'old': 42, 'new': 42, 'tiers': [[1, 0], [9, 10], [33, 25]], 'left': 30, 'period': 31}, [-45726, 45726])], [('regression', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('regression', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('partial-repair probe', {'unit': 4525, 'old': 44, 'new': 17, 'tiers': [[1, 0], [5, 5], [26, 15]], 'left': 7, 'period': 31}, [-38214, 16502]), ('partial-repair probe', {'unit': 1000, 'old': 48, 'new': 14, 'tiers': [[1, 0], [9, 5], [31, 20]], 'left': 21, 'period': 31}, [-26013, 9010]), ('normal control', {'unit': 1500, 'old': 45, 'new': 35, 'tiers': [[1, 0], [9, 10], [25, 25]], 'left': 26, 'period': 31}, [-42460, 33024]), ('normal control', {'unit': 1000, 'old': 27, 'new': 21, 'tiers': [[1, 0], [8, 10], [36, 15]], 'left': 24, 'period': 31}, [-18813, 14632]), ('normal control', {'unit': 999, 'old': 24, 'new': 6, 'tiers': [[1, 0], [5, 10], [35, 15]], 'left': 27, 'period': 31}, [-18794, 4699]), ('normal control', {'unit': 999, 'old': 37, 'new': 45, 'tiers': [[1, 0], [9, 10], [25, 20]], 'left': 31, 'period': 31}, [-29570, 35964])], [('regression', {'unit': 1500, 'old': 38, 'new': 12, 'tiers': [[1, 0], [8, 10], [31, 25]], 'left': 15, 'period': 31}, [-20685, 7839]), ('regression', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('partial-repair probe', {'unit': 999, 'old': 14, 'new': 7, 'tiers': [[1, 0], [8, 10], [27, 25]], 'left': 24, 'period': 31}, [-9745, 5414]), ('partial-repair probe', {'unit': 999, 'old': 39, 'new': 26, 'tiers': [[1, 0], [7, 10], [32, 15]], 'left': 27, 'period': 31}, [-28844, 20360]), ('normal control', {'unit': 3876, 'old': 16, 'new': 37, 'tiers': [[1, 0], [6, 10], [40, 20]], 'left': 14, 'period': 31}, [-25207, 58290]), ('normal control', {'unit': 1500, 'old': 32, 'new': 32, 'tiers': [[1, 0], [5, 5], [35, 20]], 'left': 25, 'period': 31}, [-36774, 36774]), ('normal control', {'unit': 1000, 'old': 22, 'new': 25, 'tiers': [[1, 0], [9, 10], [38, 15]], 'left': 13, 'period': 31}, [-8303, 9435]), ('normal control', {'unit': 999, 'old': 32, 'new': 41, 'tiers': [[1, 0], [5, 10], [23, 15]], 'left': 13, 'period': 31}, [-11395, 14600])], [('regression', {'unit': 4525, 'old': 44, 'new': 17, 'tiers': [[1, 0], [5, 5], [26, 15]], 'left': 7, 'period': 31}, [-38214, 16502]), ('regression', {'unit': 1500, 'old': 21, 'new': 45, 'tiers': [[1, 0], [7, 10], [29, 20]], 'left': 20, 'period': 31}, [-18290, 34839]), ('partial-repair probe', {'unit': 999, 'old': 10, 'new': 1, 'tiers': [[1, 0], [5, 10], [22, 15]], 'left': 29, 'period': 31}, [-8411, 935]), ('partial-repair probe', {'unit': 999, 'old': 21, 'new': 1, 'tiers': [[1, 0], [10, 5], [31, 20]], 'left': 9, 'period': 31}, [-5786, 290]), ('normal control', {'unit': 3669, 'old': 42, 'new': 45, 'tiers': [[1, 0], [8, 10], [31, 15]], 'left': 27, 'period': 31}, [-114082, 122231]), ('normal control', {'unit': 3542, 'old': 20, 'new': 15, 'tiers': [[1, 0], [10, 10], [31, 25]], 'left': 9, 'period': 31}, [-18510, 13882]), ('normal control', {'unit': 1500, 'old': 5, 'new': 29, 'tiers': [[1, 0], [5, 5], [38, 15]], 'left': 16, 'period': 31}, [-3677, 21329]), ('normal control', {'unit': 1500, 'old': 18, 'new': 18, 'tiers': [[1, 0], [9, 10], [31, 20]], 'left': 1, 'period': 31}, [-784, 784])]]
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	`[-13051, 725]`	`[-13051, 806]`	Failed
regression 1	`[-15320, 27661]`	`[-15320, 27661]`	Passed
partial-repair probe 2	`[-19783, 10159]`	`[-19783, 11354]`	Failed
partial-repair probe 3	`[-38646, 22440]`	`[-38646, 28424]`	Failed
normal control 4	`[-9102, 13239]`	`[-9102, 13239]`	Passed
normal control 5	`[-20129, 30968]`	`[-20129, 30968]`	Passed
normal control 6	`[-1532, 5210]`	`[-1532, 5210]`	Passed
normal control 7	`[-2961, 5045]`	`[-2961, 5045]`	Passed

SHA-256 / 4c787021b13618e5545ebd8b309e1addae5cb99716548be2bf3631d0b8de64e5

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(x):
    def tier_pct(q):
        pct = 0
        for mn, off in x['tiers']:
            if q >= mn:
                pct = off
        return pct
    def total(q):
        return q * x['unit'] * (100 - tier_pct(q))
    def pr(q):
        return (total(q) * x['left'] * 2 + 100 * x['period']) // (200 * x['period'])
    return [-pr(x['old']), pr(x['new'])]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'unit': 999, 'old': 18, 'new': 1, 'tiers': [[1, 0], [8, 10], [28, 15]], 'left': 25, 'period': 31}, [-13051, 806]), ('regression', {'unit': 1396, 'old': 18, 'new': 39, 'tiers': [[1, 0], [5, 10], [31, 25]], 'left': 21, 'period': 31}, [-15320, 27661]), ('partial-repair probe', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('partial-repair probe', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('normal control', {'unit': 1500, 'old': 22, 'new': 32, 'tiers': [[1, 0], [5, 5], [35, 25]], 'left': 9, 'period': 31}, [-9102, 13239]), ('normal control', {'unit': 1500, 'old': 26, 'new': 40, 'tiers': [[1, 0], [7, 10], [26, 20]], 'left': 20, 'period': 31}, [-20129, 30968]), ('normal control', {'unit': 1000, 'old': 5, 'new': 17, 'tiers': [[1, 0], [5, 5], [37, 25]], 'left': 10, 'period': 31}, [-1532, 5210]), ('normal control', {'unit': 1000, 'old': 27, 'new': 46, 'tiers': [[1, 0], [9, 5], [20, 15]], 'left': 4, 'period': 31}, [-2961, 5045])], [('regression', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('regression', {'unit': 999, 'old': 18, 'new': 1, 'tiers': [[1, 0], [8, 10], [28, 15]], 'left': 25, 'period': 31}, [-13051, 806]), ('partial-repair probe', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('partial-repair probe', {'unit': 1500, 'old': 38, 'new': 12, 'tiers': [[1, 0], [8, 10], [31, 25]], 'left': 15, 'period': 31}, [-20685, 7839]), ('normal control', {'unit': 1000, 'old': 18, 'new': 17, 'tiers': [[1, 0], [10, 10], [32, 25]], 'left': 24, 'period': 31}, [-12542, 11845]), ('normal control', {'unit': 1000, 'old': 41, 'new': 30, 'tiers': [[1, 0], [5, 5], [20, 15]], 'left': 23, 'period': 31}, [-25856, 18919]), ('normal control', {'unit': 1000, 'old': 1, 'new': 2, 'tiers': [[1, 0], [10, 5], [21, 20]], 'left': 30, 'period': 31}, [-968, 1935]), ('normal control', {'unit': 1500, 'old': 42, 'new': 42, 'tiers': [[1, 0], [9, 10], [33, 25]], 'left': 30, 'period': 31}, [-45726, 45726])], [('regression', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('regression', {'unit': 1500, 'old': 37, 'new': 19, 'tiers': [[1, 0], [5, 5], [28, 15]], 'left': 13, 'period': 31}, [-19783, 11354]), ('partial-repair probe', {'unit': 4525, 'old': 44, 'new': 17, 'tiers': [[1, 0], [5, 5], [26, 15]], 'left': 7, 'period': 31}, [-38214, 16502]), ('partial-repair probe', {'unit': 1000, 'old': 48, 'new': 14, 'tiers': [[1, 0], [9, 5], [31, 20]], 'left': 21, 'period': 31}, [-26013, 9010]), ('normal control', {'unit': 1500, 'old': 45, 'new': 35, 'tiers': [[1, 0], [9, 10], [25, 25]], 'left': 26, 'period': 31}, [-42460, 33024]), ('normal control', {'unit': 1000, 'old': 27, 'new': 21, 'tiers': [[1, 0], [8, 10], [36, 15]], 'left': 24, 'period': 31}, [-18813, 14632]), ('normal control', {'unit': 999, 'old': 24, 'new': 6, 'tiers': [[1, 0], [5, 10], [35, 15]], 'left': 27, 'period': 31}, [-18794, 4699]), ('normal control', {'unit': 999, 'old': 37, 'new': 45, 'tiers': [[1, 0], [9, 10], [25, 20]], 'left': 31, 'period': 31}, [-29570, 35964])], [('regression', {'unit': 1500, 'old': 38, 'new': 12, 'tiers': [[1, 0], [8, 10], [31, 25]], 'left': 15, 'period': 31}, [-20685, 7839]), ('regression', {'unit': 4294, 'old': 31, 'new': 18, 'tiers': [[1, 0], [10, 5], [21, 25]], 'left': 12, 'period': 31}, [-38646, 28424]), ('partial-repair probe', {'unit': 999, 'old': 14, 'new': 7, 'tiers': [[1, 0], [8, 10], [27, 25]], 'left': 24, 'period': 31}, [-9745, 5414]), ('partial-repair probe', {'unit': 999, 'old': 39, 'new': 26, 'tiers': [[1, 0], [7, 10], [32, 15]], 'left': 27, 'period': 31}, [-28844, 20360]), ('normal control', {'unit': 3876, 'old': 16, 'new': 37, 'tiers': [[1, 0], [6, 10], [40, 20]], 'left': 14, 'period': 31}, [-25207, 58290]), ('normal control', {'unit': 1500, 'old': 32, 'new': 32, 'tiers': [[1, 0], [5, 5], [35, 20]], 'left': 25, 'period': 31}, [-36774, 36774]), ('normal control', {'unit': 1000, 'old': 22, 'new': 25, 'tiers': [[1, 0], [9, 10], [38, 15]], 'left': 13, 'period': 31}, [-8303, 9435]), ('normal control', {'unit': 999, 'old': 32, 'new': 41, 'tiers': [[1, 0], [5, 10], [23, 15]], 'left': 13, 'period': 31}, [-11395, 14600])], [('regression', {'unit': 4525, 'old': 44, 'new': 17, 'tiers': [[1, 0], [5, 5], [26, 15]], 'left': 7, 'period': 31}, [-38214, 16502]), ('regression', {'unit': 1500, 'old': 21, 'new': 45, 'tiers': [[1, 0], [7, 10], [29, 20]], 'left': 20, 'period': 31}, [-18290, 34839]), ('partial-repair probe', {'unit': 999, 'old': 10, 'new': 1, 'tiers': [[1, 0], [5, 10], [22, 15]], 'left': 29, 'period': 31}, [-8411, 935]), ('partial-repair probe', {'unit': 999, 'old': 21, 'new': 1, 'tiers': [[1, 0], [10, 5], [31, 20]], 'left': 9, 'period': 31}, [-5786, 290]), ('normal control', {'unit': 3669, 'old': 42, 'new': 45, 'tiers': [[1, 0], [8, 10], [31, 15]], 'left': 27, 'period': 31}, [-114082, 122231]), ('normal control', {'unit': 3542, 'old': 20, 'new': 15, 'tiers': [[1, 0], [10, 10], [31, 25]], 'left': 9, 'period': 31}, [-18510, 13882]), ('normal control', {'unit': 1500, 'old': 5, 'new': 29, 'tiers': [[1, 0], [5, 5], [38, 15]], 'left': 16, 'period': 31}, [-3677, 21329]), ('normal control', {'unit': 1500, 'old': 18, 'new': 18, 'tiers': [[1, 0], [9, 10], [31, 20]], 'left': 1, 'period': 31}, [-784, 784])]]
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	`[-13051, 806]`	`[-13051, 806]`	Passed
regression 1	`[-15320, 27661]`	`[-15320, 27661]`	Passed
partial-repair probe 2	`[-19783, 11354]`	`[-19783, 11354]`	Passed
partial-repair probe 3	`[-38646, 28424]`	`[-38646, 28424]`	Passed
normal control 4	`[-9102, 13239]`	`[-9102, 13239]`	Passed
normal control 5	`[-20129, 30968]`	`[-20129, 30968]`	Passed
normal control 6	`[-1532, 5210]`	`[-1532, 5210]`	Passed
normal control 7	`[-2961, 5045]`	`[-2961, 5045]`	Passed

SHA-256 / 0b1bb5100388265c5da09d0d7f47dd868683a0a008050ea8f296f8353a32acdc

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 2026-09-29T14:46:38.652977+00:00.

Case digest / d781bfdd0de36f5979f80b3d68e1f4138a46b2ddf8ea67d20ded0d3b63f2a919