FAILURE MAP
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FA-59676 / Subscription proration billing / Open access

Immediate upgrades and scheduled downgrades: downgrade deferral · case 01

Downgrades take effect immediately without credit, cutting service mid-period.

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

ROOT CAUSE

The downgrade is applied now instead of being scheduled for renewal.

VERIFIED REPAIR

Restore the contract rule at the downgrade deferral step: use `return [0, x['cur'], x['new']]`.

Unsuccessful approach: The attempt schedules it but lets an older pending change win.

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['cur']) * x['left'] * 2 + x['period']) // (2 * x['period'])
        return [ch, x['new'], None]
    if x['new'] < x['cur']:
        return [0, x['new'], None]
    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': 1000, 'left': 28, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('normal control', {'cur': 5000, 'new': 7500, 'left': 20, 'period': 30, 'pending': None}, [1667, 7500, None]), ('normal control', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 21, 'period': 31, 'pending': None}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000]), ('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]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 13, 'period': 30, 'pending': None}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 2000]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 1, 'period': 30, 'pending': 2000}, [33, 2000, None]), ('normal control', {'cur': 1000, 'new': 5000, 'left': 29, 'period': 30, 'pending': 1000}, [3867, 5000, None])], [('regression', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000]), ('regression', {'cur': 2000, 'new': 1000, 'left': 1, 'period': 31, 'pending': 1000}, [0, 2000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 7, 'period': 31, 'pending': 500}, [0, 2000, 1000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 139, 'period': 365, 'pending': None}, [1142, 5000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 39, 'period': 365, 'pending': 2000}, [107, 2000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 20, 'period': 31, 'pending': 2000}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 164, 'period': 365, 'pending': 2000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 31, 'period': 31, 'pending': 500}, [0, 2000, 1000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 24, 'period': 31, 'pending': None}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 21, 'period': 30, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 1000, 'new': 5000, 'left': 9, 'period': 365, 'pending': 500}, [99, 5000, None]), ('normal control', {'cur': 2000, 'new': 7500, 'left': 2, 'period': 31, 'pending': None}, [355, 7500, 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[0, 1000, None][0, 5000, 1000]Failed
regression 1[0, 1000, None][0, 5000, 1000]Failed
partial-repair probe 2[0, 1000, None][0, 5000, 1000]Failed
partial-repair probe 3[0, 2000, None][0, 5000, 2000]Failed
normal control 4[1667, 7500, None][1667, 7500, None]Passed
normal control 5[417, 7500, None][417, 7500, None]Passed
normal control 6[2100, 5000, None][2100, 5000, None]Passed
normal control 7[0, 1000, None][0, 1000, None]Passed

SHA-256 / d973b556aa9d1489438a6247776a2019a0f497054ec2dcf21335450ccce1d8f8

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['left'] * 2 + x['period']) // (2 * x['period'])
        return [ch, x['new'], None]
    if x['new'] < x['cur']:
        return [0, x['cur'], x['pending'] or 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': 1000, 'left': 28, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('normal control', {'cur': 5000, 'new': 7500, 'left': 20, 'period': 30, 'pending': None}, [1667, 7500, None]), ('normal control', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 21, 'period': 31, 'pending': None}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000]), ('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]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 13, 'period': 30, 'pending': None}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 2000]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 1, 'period': 30, 'pending': 2000}, [33, 2000, None]), ('normal control', {'cur': 1000, 'new': 5000, 'left': 29, 'period': 30, 'pending': 1000}, [3867, 5000, None])], [('regression', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000]), ('regression', {'cur': 2000, 'new': 1000, 'left': 1, 'period': 31, 'pending': 1000}, [0, 2000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 7, 'period': 31, 'pending': 500}, [0, 2000, 1000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 139, 'period': 365, 'pending': None}, [1142, 5000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 39, 'period': 365, 'pending': 2000}, [107, 2000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 20, 'period': 31, 'pending': 2000}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 164, 'period': 365, 'pending': 2000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 31, 'period': 31, 'pending': 500}, [0, 2000, 1000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 24, 'period': 31, 'pending': None}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 21, 'period': 30, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 1000, 'new': 5000, 'left': 9, 'period': 365, 'pending': 500}, [99, 5000, None]), ('normal control', {'cur': 2000, 'new': 7500, 'left': 2, 'period': 31, 'pending': None}, [355, 7500, 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[0, 5000, 500][0, 5000, 1000]Failed
regression 1[0, 5000, 1000][0, 5000, 1000]Passed
partial-repair probe 2[0, 5000, 500][0, 5000, 1000]Failed
partial-repair probe 3[0, 5000, 1000][0, 5000, 2000]Failed
normal control 4[1667, 7500, None][1667, 7500, None]Passed
normal control 5[417, 7500, None][417, 7500, None]Passed
normal control 6[2100, 5000, None][2100, 5000, None]Passed
normal control 7[0, 1000, None][0, 1000, None]Passed

SHA-256 / 091addbc03a10326ad1242d83091c647e2b72a5d12a32f66101bad8d0f22ea3b

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': 1000, 'left': 28, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('normal control', {'cur': 5000, 'new': 7500, 'left': 20, 'period': 30, 'pending': None}, [1667, 7500, None]), ('normal control', {'cur': 5000, 'new': 7500, 'left': 5, 'period': 30, 'pending': None}, [417, 7500, None]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 21, 'period': 30, 'pending': None}, [2100, 5000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 21, 'period': 31, 'pending': None}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 14, 'period': 30, 'pending': 1000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000]), ('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]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 24, 'period': 30, 'pending': None}, [2400, 5000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 13, 'period': 30, 'pending': None}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('regression', {'cur': 5000, 'new': 1000, 'left': 16, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 2000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 2000]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 17, 'period': 30, 'pending': 2000}, [567, 2000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 272, 'period': 365, 'pending': 500}, [745, 2000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 1, 'period': 30, 'pending': 2000}, [33, 2000, None]), ('normal control', {'cur': 1000, 'new': 5000, 'left': 29, 'period': 30, 'pending': 1000}, [3867, 5000, None])], [('regression', {'cur': 2000, 'new': 1000, 'left': 252, 'period': 365, 'pending': 500}, [0, 2000, 1000]), ('regression', {'cur': 2000, 'new': 1000, 'left': 1, 'period': 31, 'pending': 1000}, [0, 2000, 1000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 11, 'period': 31, 'pending': 500}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 7, 'period': 31, 'pending': 500}, [0, 2000, 1000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 5000, 'left': 139, 'period': 365, 'pending': None}, [1142, 5000, None]), ('normal control', {'cur': 1000, 'new': 2000, 'left': 39, 'period': 365, 'pending': 2000}, [107, 2000, None]), ('normal control', {'cur': 1000, 'new': 1000, 'left': 20, 'period': 31, 'pending': 2000}, [0, 1000, None])], [('regression', {'cur': 5000, 'new': 1000, 'left': 25, 'period': 31, 'pending': 2000}, [0, 5000, 1000]), ('regression', {'cur': 5000, 'new': 2000, 'left': 30, 'period': 30, 'pending': 1000}, [0, 5000, 2000]), ('partial-repair probe', {'cur': 5000, 'new': 1000, 'left': 164, 'period': 365, 'pending': 2000}, [0, 5000, 1000]), ('partial-repair probe', {'cur': 2000, 'new': 1000, 'left': 31, 'period': 31, 'pending': 500}, [0, 2000, 1000]), ('normal control', {'cur': 5000, 'new': 5000, 'left': 24, 'period': 31, 'pending': None}, [0, 5000, None]), ('normal control', {'cur': 2000, 'new': 2000, 'left': 21, 'period': 30, 'pending': None}, [0, 2000, None]), ('normal control', {'cur': 1000, 'new': 5000, 'left': 9, 'period': 365, 'pending': 500}, [99, 5000, None]), ('normal control', {'cur': 2000, 'new': 7500, 'left': 2, 'period': 31, 'pending': None}, [355, 7500, 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[0, 5000, 1000][0, 5000, 1000]Passed
regression 1[0, 5000, 1000][0, 5000, 1000]Passed
partial-repair probe 2[0, 5000, 1000][0, 5000, 1000]Passed
partial-repair probe 3[0, 5000, 2000][0, 5000, 2000]Passed
normal control 4[1667, 7500, None][1667, 7500, None]Passed
normal control 5[417, 7500, None][417, 7500, None]Passed
normal control 6[2100, 5000, None][2100, 5000, None]Passed
normal control 7[0, 1000, None][0, 1000, None]Passed

SHA-256 / 628c07df8d9b8b2f05e4927ab3e1e4f4e7bbaaac8e103fc332976d7cd7082baa

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 2026-09-29T14:46:38.486148+00:00.

Case digest / 6cec9c715a8f8f072c723c4dca16e1e6b13d7668a95d7f350902ee6bf9443614