FAILURE MAP
← Case archive

FA-59681 / Subscription proration billing / Open access

Immediate upgrades and scheduled downgrades: upgrade charge basis · case 01

Upgrades charge the full new price for remaining days with no credit for the old plan.

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

ROOT CAUSE

The prorated charge uses the new price instead of the price difference.

VERIFIED REPAIR

Restore the contract rule at the upgrade charge basis step: use `(x['new'] - x['cur']) * x['left']`.

Unsuccessful approach: The attempt charges the difference for elapsed days rather than remaining days.

Case contract

Input {cur, new, left, period, pending scheduled price|None}. Upgrade (new > cur): charge (new-cur)*left/period half-up now, switch immediately and clear any scheduled change. Downgrade: no charge, keep cur, schedule new (replacing any pending). Same price: no charge and clear the schedule. Return [charge, active_price, scheduled].

Why this case matters

Asymmetric upgrade/downgrade policies must not let a stale scheduled downgrade override an upgrade.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    if x['new'] > x['cur']:
        ch = (x['new'] * x['left'] * 2 + x['period']) // (2 * x['period'])
        return [ch, x['new'], None]
    if x['new'] < x['cur']:
        return [0, x['cur'], x['new']]
    return [0, x['cur'], None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'cur': 5000, 'new': 7500, 'left': 20, 'period': 30, 'pending': None}, [1667, 7500, None]), ('regression', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 28, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 21, 'period': 31, 'pending': None}, [0, 1000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 220, 'period': 365, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 26, 'period': 31, 'pending': 1000}, [0, 5000, None])], [('regression', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('regression', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 13, 'period': 30, 'pending': None}, [0, 1000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 1, 'period': 31, 'pending': 1000}, [0, 2000, 1000])], [('regression', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('regression', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('normal control', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 15, 'period': 31, 'pending': 1000}, [0, 2000, 1000]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000])], [('regression', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('regression', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 1, 'period': 30, 'pending': 2000}, [33, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 5000, 'left': 29, 'period': 30, 'pending': 1000}, [3867, 5000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 20, 'period': 31, 'pending': 2000}, [0, 1000, None]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 24, 'period': 31, 'pending': None}, [0, 5000, None]), ('normal control', {'cur': 5000, 'new': 2000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 2000])], [('regression', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('regression', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 139, 'period': 365, 'pending': None}, [1142, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 39, 'period': 365, 'pending': 2000}, [107, 2000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 21, 'period': 30, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 24, 'period': 30, 'pending': 1000}, [0, 2000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 328, 'period': 365, 'pending': 1000}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 201, 'period': 365, 'pending': None}, [0, 1000, None])]]
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[5000, 7500, None][1667, 7500, None]Failed
regression 1[1250, 7500, None][417, 7500, None]Failed
partial-repair probe 2[3500, 5000, None][2100, 5000, None]Failed
partial-repair probe 3[4000, 5000, None][2400, 5000, None]Failed
normal control 4[0, 5000, 1000][0, 5000, 1000]Passed
normal control 5[0, 1000, None][0, 1000, None]Passed
normal control 6[0, 2000, None][0, 2000, None]Passed
normal control 7[0, 5000, None][0, 5000, None]Passed

SHA-256 / ba14ef455fbb253c313dca9edceda98123d708b32c54d5791995db06feba48a3

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    if x['new'] > x['cur']:
        ch = ((x['new'] - x['cur']) * (x['period'] - x['left']) * 2 + x['period']) // (2 * x['period'])
        return [ch, x['new'], None]
    if x['new'] < x['cur']:
        return [0, x['cur'], x['new']]
    return [0, x['cur'], None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'cur': 5000, 'new': 7500, 'left': 20, 'period': 30, 'pending': None}, [1667, 7500, None]), ('regression', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 28, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 21, 'period': 31, 'pending': None}, [0, 1000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 220, 'period': 365, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 26, 'period': 31, 'pending': 1000}, [0, 5000, None])], [('regression', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('regression', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 13, 'period': 30, 'pending': None}, [0, 1000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 1, 'period': 31, 'pending': 1000}, [0, 2000, 1000])], [('regression', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('regression', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('normal control', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 15, 'period': 31, 'pending': 1000}, [0, 2000, 1000]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000])], [('regression', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('regression', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 1, 'period': 30, 'pending': 2000}, [33, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 5000, 'left': 29, 'period': 30, 'pending': 1000}, [3867, 5000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 20, 'period': 31, 'pending': 2000}, [0, 1000, None]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 24, 'period': 31, 'pending': None}, [0, 5000, None]), ('normal control', {'cur': 5000, 'new': 2000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 2000])], [('regression', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('regression', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 139, 'period': 365, 'pending': None}, [1142, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 39, 'period': 365, 'pending': 2000}, [107, 2000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 21, 'period': 30, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 24, 'period': 30, 'pending': 1000}, [0, 2000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 328, 'period': 365, 'pending': 1000}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 201, 'period': 365, 'pending': None}, [0, 1000, None])]]
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[833, 7500, None][1667, 7500, None]Failed
regression 1[2083, 7500, None][417, 7500, None]Failed
partial-repair probe 2[900, 5000, None][2100, 5000, None]Failed
partial-repair probe 3[600, 5000, None][2400, 5000, None]Failed
normal control 4[0, 5000, 1000][0, 5000, 1000]Passed
normal control 5[0, 1000, None][0, 1000, None]Passed
normal control 6[0, 2000, None][0, 2000, None]Passed
normal control 7[0, 5000, None][0, 5000, None]Passed

SHA-256 / 7a7b38af8f9f6b507f8bef8f6e53439709cb31689a652ef14e97848449ce1832

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    if x['new'] > x['cur']:
        ch = ((x['new'] - x['cur']) * x['left'] * 2 + x['period']) // (2 * x['period'])
        return [ch, x['new'], None]
    if x['new'] < x['cur']:
        return [0, x['cur'], x['new']]
    return [0, x['cur'], None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'cur': 5000, 'new': 7500, 'left': 20, 'period': 30, 'pending': None}, [1667, 7500, None]), ('regression', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 28, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 21, 'period': 31, 'pending': None}, [0, 1000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 220, 'period': 365, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 26, 'period': 31, 'pending': 1000}, [0, 5000, None])], [('regression', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('regression', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 13, 'period': 30, 'pending': None}, [0, 1000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 1, 'period': 31, 'pending': 1000}, [0, 2000, 1000])], [('regression', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('regression', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('normal control', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 15, 'period': 31, 'pending': 1000}, [0, 2000, 1000]), ('normal control', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000])], [('regression', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('regression', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 1, 'period': 30, 'pending': 2000}, [33, 2000, None]), ('partial-repair probe', {'cur': 1000, 'new': 5000, 'left': 29, 'period': 30, 'pending': 1000}, [3867, 5000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 20, 'period': 31, 'pending': 2000}, [0, 1000, None]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 24, 'period': 31, 'pending': None}, [0, 5000, None]), ('normal control', {'cur': 5000, 'new': 2000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 2000])], [('regression', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('regression', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('partial-repair probe', {'cur': 2000, 'new': 5000, 'left': 139, 'period': 365, 'pending': None}, [1142, 5000, None]), ('partial-repair probe', {'cur': 1000, 'new': 2000, 'left': 39, 'period': 365, 'pending': 2000}, [107, 2000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 21, 'period': 30, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 24, 'period': 30, 'pending': 1000}, [0, 2000, None]), ('normal control', {'cur': 5000, 'new': 1000, 'left': 328, 'period': 365, 'pending': 1000}, [0, 5000, 1000]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 201, 'period': 365, 'pending': None}, [0, 1000, None])]]
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[1667, 7500, None][1667, 7500, None]Passed
regression 1[417, 7500, None][417, 7500, None]Passed
partial-repair probe 2[2100, 5000, None][2100, 5000, None]Passed
partial-repair probe 3[2400, 5000, None][2400, 5000, None]Passed
normal control 4[0, 5000, 1000][0, 5000, 1000]Passed
normal control 5[0, 1000, None][0, 1000, None]Passed
normal control 6[0, 2000, None][0, 2000, None]Passed
normal control 7[0, 5000, None][0, 5000, None]Passed

SHA-256 / 2288d8e2d54f5ac1c2b52c84b1e584c8cb8f7d3d07ff92a53e06b541c28d2b57

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 2026-09-29T14:46:38.519892+00:00.

Case digest / bea36e5c3ceef192652f684593c7ab2d6c29d5c76df7a518236ff38bc4636733