FA-59711 / Subscription proration billing / Open access

Subscription schedule phases within a period: phase ordering · case 01

Schedules entered out of order produce wrong phase boundaries.

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

ROOT CAUSE

Phases are used in input order rather than by start day.

THE FAILURE

Phases are used in input order rather than by start day.

Unsuccessful approach: The attempt sorts phases by price instead of by start.

Case contract

Input {ps, pe (exclusive), phases: [[start_day, price per full period]]}. Phases are ordered by start; each lasts until the next phase starts (the last until pe). The period charge is sum(price * overlap days with [ps, pe)) / (pe - ps), rounded half-up once. Return cents.

Why this case matters

Phase transitions inside a billing period must be charged exactly for their overlapping days.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(x):
    ph = x['phases']
    L = x['pe'] - x['ps']
    total = 0
    for i, (st, price) in enumerate(ph):
        en = ph[i + 1][0] if i + 1 < len(ph) else x['pe']
        a, b = max(st, x['ps']), min(en, x['pe'])
        if b > a:
            total += price * (b - a)
    return (total * 2 + L) // (2 * L)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0), ('normal control', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0)], [('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('normal control', {'ps': 9, 'pe': 40, 'phases': [[-1, 2000], [3, 3500]]}, 3500), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0)], [('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('partial-repair probe', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683)], [('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('regression', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('partial-repair probe', {'ps': 79, 'pe': 110, 'phases': [[64, 1000], [102, 2000], [87, 3844], [79, 2607]]}, 3049), ('partial-repair probe', {'ps': 16, 'pe': 47, 'phases': [[-4, 3500], [28, 3500], [18, 3500], [14, 0]]}, 3274), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0), ('normal control', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 56, 'pe': 86, 'phases': [[60, 0]]}, 0), ('normal control', {'ps': 86, 'pe': 117, 'phases': [[107, 2000], [119, 2395]]}, 645)], [('regression', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('regression', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100), ('partial-repair probe', {'ps': 16, 'pe': 47, 'phases': [[-4, 3500], [28, 3500], [18, 3500], [14, 0]]}, 3274), ('partial-repair probe', {'ps': 52, 'pe': 83, 'phases': [[50, 3500], [79, 4966], [52, 3260], [86, 1000]]}, 3480), ('normal control', {'ps': 17, 'pe': 47, 'phases': [[18, 2000]]}, 1933), ('normal control', {'ps': 89, 'pe': 120, 'phases': [[113, 1000], [101, 1000], [71, 1000]]}, 1000), ('normal control', {'ps': 27, 'pe': 58, 'phases': [[22, 3500], [32, 8749]]}, 7902), ('normal control', {'ps': 92, 'pe': 122, 'phases': [[91, 2000]]}, 2000)]]
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	`2300`	`1833`	Failed
regression 1	`0`	`1633`	Failed
partial-repair probe 2	`1267`	`1000`	Failed
partial-repair probe 3	`3844`	`2450`	Failed
normal control 4	`791`	`791`	Passed
normal control 5	`0`	`0`	Passed
normal control 6	`1300`	`1300`	Passed
normal control 7	`0`	`0`	Passed

SHA-256 / c4e78cfd13483664f572d1e08959d54eaeea5932bb22d20ede8a945a3c6425d6

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    ph = sorted(x['phases'], key=lambda p: p[1])
    L = x['pe'] - x['ps']
    total = 0
    for i, (st, price) in enumerate(ph):
        en = ph[i + 1][0] if i + 1 < len(ph) else x['pe']
        a, b = max(st, x['ps']), min(en, x['pe'])
        if b > a:
            total += price * (b - a)
    return (total * 2 + L) // (2 * L)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0), ('normal control', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0)], [('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('normal control', {'ps': 9, 'pe': 40, 'phases': [[-1, 2000], [3, 3500]]}, 3500), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0)], [('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('partial-repair probe', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683)], [('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('regression', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('partial-repair probe', {'ps': 79, 'pe': 110, 'phases': [[64, 1000], [102, 2000], [87, 3844], [79, 2607]]}, 3049), ('partial-repair probe', {'ps': 16, 'pe': 47, 'phases': [[-4, 3500], [28, 3500], [18, 3500], [14, 0]]}, 3274), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0), ('normal control', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 56, 'pe': 86, 'phases': [[60, 0]]}, 0), ('normal control', {'ps': 86, 'pe': 117, 'phases': [[107, 2000], [119, 2395]]}, 645)], [('regression', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('regression', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100), ('partial-repair probe', {'ps': 16, 'pe': 47, 'phases': [[-4, 3500], [28, 3500], [18, 3500], [14, 0]]}, 3274), ('partial-repair probe', {'ps': 52, 'pe': 83, 'phases': [[50, 3500], [79, 4966], [52, 3260], [86, 1000]]}, 3480), ('normal control', {'ps': 17, 'pe': 47, 'phases': [[18, 2000]]}, 1933), ('normal control', {'ps': 89, 'pe': 120, 'phases': [[113, 1000], [101, 1000], [71, 1000]]}, 1000), ('normal control', {'ps': 27, 'pe': 58, 'phases': [[22, 3500], [32, 8749]]}, 7902), ('normal control', {'ps': 92, 'pe': 122, 'phases': [[91, 2000]]}, 2000)]]
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	`3500`	`1833`	Failed
regression 1	`5707`	`1633`	Failed
partial-repair probe 2	`1267`	`1000`	Failed
partial-repair probe 3	`3234`	`2450`	Failed
normal control 4	`791`	`791`	Passed
normal control 5	`0`	`0`	Passed
normal control 6	`1300`	`1300`	Passed
normal control 7	`0`	`0`	Passed

SHA-256 / b023eaf2da01d085ac60d791f040e260670219f17afad238c54ef063abc1aada

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 8 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.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

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 2026-09-29T14:46:38.828267+00:00.

Case digest / d69f908c9646d6bd43e2b65431eb20ad9bec0ab4a27680748658398a358fe7bc