FAILURE MAP
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FA-59716 / Subscription proration billing / Open access

Subscription schedule phases within a period: single final rounding · case 01

Multi-phase periods are a cent off the exact prorated total.

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

ROOT CAUSE

Each phase contribution is rounded to cents before summation.

THE FAILURE

Each phase contribution is rounded to cents before summation.

Unsuccessful approach: The attempt truncates each phase contribution, systematically under-charging.

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 = sorted(x['phases'], key=lambda p: p[0])
    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) * 2 + L) // (2 * L) * L
    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': 23, 'pe': 53, 'phases': [[39, 3769], [35, 2000], [44, 8184], [37, 7610]]}, 3724), ('regression', {'ps': 27, 'pe': 58, 'phases': [[22, 3500], [32, 8749]]}, 7902), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('normal control', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('normal control', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('normal control', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0)], [('regression', {'ps': 27, 'pe': 58, 'phases': [[22, 3500], [32, 8749]]}, 7902), ('regression', {'ps': 96, 'pe': 126, 'phases': [[114, 0], [86, 1000], [97, 2000], [94, 1000]]}, 1167), ('partial-repair probe', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('partial-repair probe', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767), ('normal control', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0), ('normal control', {'ps': 9, 'pe': 40, 'phases': [[-1, 2000], [3, 3500]]}, 3500)], [('regression', {'ps': 96, 'pe': 126, 'phases': [[114, 0], [86, 1000], [97, 2000], [94, 1000]]}, 1167), ('regression', {'ps': 83, 'pe': 114, 'phases': [[91, 3500], [73, 3500], [117, 0], [98, 2000]]}, 2726), ('partial-repair probe', {'ps': 79, 'pe': 110, 'phases': [[64, 1000], [102, 2000], [87, 3844], [79, 2607]]}, 3049), ('partial-repair probe', {'ps': 52, 'pe': 83, 'phases': [[50, 3500], [79, 4966], [52, 3260], [86, 1000]]}, 3480), ('normal control', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500), ('normal control', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100)], [('regression', {'ps': 83, 'pe': 114, 'phases': [[91, 3500], [73, 3500], [117, 0], [98, 2000]]}, 2726), ('regression', {'ps': 69, 'pe': 100, 'phases': [[96, 3500], [58, 0], [98, 3500], [89, 1000]]}, 677), ('partial-repair probe', {'ps': 55, 'pe': 86, 'phases': [[60, 1000], [37, 1000], [44, 0]]}, 839), ('partial-repair probe', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0), ('normal control', {'ps': 9, 'pe': 39, 'phases': [[1, 2000], [27, 7018], [24, 2000]]}, 4007), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000)], [('regression', {'ps': 69, 'pe': 100, 'phases': [[96, 3500], [58, 0], [98, 3500], [89, 1000]]}, 677), ('regression', {'ps': 9, 'pe': 40, 'phases': [[10, 2000], [9, 1000]]}, 1968), ('partial-repair probe', {'ps': 23, 'pe': 53, 'phases': [[39, 3769], [35, 2000], [44, 8184], [37, 7610]]}, 3724), ('partial-repair probe', {'ps': 2, 'pe': 33, 'phases': [[-10, 8526], [27, 2000]]}, 7263), ('normal control', {'ps': 16, 'pe': 47, 'phases': [[-4, 3500], [28, 3500], [18, 3500], [14, 0]]}, 3274), ('normal control', {'ps': 57, 'pe': 87, 'phases': [[42, 3500], [77, 1000], [52, 0]]}, 333), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0)]]
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 037233724Failed
regression 179037902Failed
partial-repair probe 210001000Passed
partial-repair probe 316941694Passed
normal control 418331833Passed
normal control 5791791Passed
normal control 616331633Passed
normal control 700Passed

SHA-256 / 73f7c59073848ab166d1352bf86122a6d3187d57adac414c74892726ef257bf8

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[0])
    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) // L * L
    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': 23, 'pe': 53, 'phases': [[39, 3769], [35, 2000], [44, 8184], [37, 7610]]}, 3724), ('regression', {'ps': 27, 'pe': 58, 'phases': [[22, 3500], [32, 8749]]}, 7902), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('normal control', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('normal control', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('normal control', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0)], [('regression', {'ps': 27, 'pe': 58, 'phases': [[22, 3500], [32, 8749]]}, 7902), ('regression', {'ps': 96, 'pe': 126, 'phases': [[114, 0], [86, 1000], [97, 2000], [94, 1000]]}, 1167), ('partial-repair probe', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('partial-repair probe', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767), ('normal control', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0), ('normal control', {'ps': 9, 'pe': 40, 'phases': [[-1, 2000], [3, 3500]]}, 3500)], [('regression', {'ps': 96, 'pe': 126, 'phases': [[114, 0], [86, 1000], [97, 2000], [94, 1000]]}, 1167), ('regression', {'ps': 83, 'pe': 114, 'phases': [[91, 3500], [73, 3500], [117, 0], [98, 2000]]}, 2726), ('partial-repair probe', {'ps': 79, 'pe': 110, 'phases': [[64, 1000], [102, 2000], [87, 3844], [79, 2607]]}, 3049), ('partial-repair probe', {'ps': 52, 'pe': 83, 'phases': [[50, 3500], [79, 4966], [52, 3260], [86, 1000]]}, 3480), ('normal control', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500), ('normal control', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100)], [('regression', {'ps': 83, 'pe': 114, 'phases': [[91, 3500], [73, 3500], [117, 0], [98, 2000]]}, 2726), ('regression', {'ps': 69, 'pe': 100, 'phases': [[96, 3500], [58, 0], [98, 3500], [89, 1000]]}, 677), ('partial-repair probe', {'ps': 55, 'pe': 86, 'phases': [[60, 1000], [37, 1000], [44, 0]]}, 839), ('partial-repair probe', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0), ('normal control', {'ps': 9, 'pe': 39, 'phases': [[1, 2000], [27, 7018], [24, 2000]]}, 4007), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000)], [('regression', {'ps': 69, 'pe': 100, 'phases': [[96, 3500], [58, 0], [98, 3500], [89, 1000]]}, 677), ('regression', {'ps': 9, 'pe': 40, 'phases': [[10, 2000], [9, 1000]]}, 1968), ('partial-repair probe', {'ps': 23, 'pe': 53, 'phases': [[39, 3769], [35, 2000], [44, 8184], [37, 7610]]}, 3724), ('partial-repair probe', {'ps': 2, 'pe': 33, 'phases': [[-10, 8526], [27, 2000]]}, 7263), ('normal control', {'ps': 16, 'pe': 47, 'phases': [[-4, 3500], [28, 3500], [18, 3500], [14, 0]]}, 3274), ('normal control', {'ps': 57, 'pe': 87, 'phases': [[42, 3500], [77, 1000], [52, 0]]}, 333), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0)]]
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 037233724Failed
regression 179017902Failed
partial-repair probe 29991000Failed
partial-repair probe 316931694Failed
normal control 418331833Passed
normal control 5791791Passed
normal control 616331633Passed
normal control 700Passed

SHA-256 / ed6cedb2d4987630bf1226233bb4b0d61d628fedf32febf4013b55db7577120e

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.

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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 2026-09-29T14:46:38.868290+00:00.

Case digest / 109758fb8ed66eb1ceb75fd813d97f00ea7b1a9d17bf75370da4e05d6b9c68a6