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FA-59551 / Subscription proration billing / Open access

Annual-to-monthly switch credit: credit division order · case 01

Credits for annual prices not divisible by twelve lose cents per month.

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

ROOT CAUSE

The monthly share is truncated before multiplying by remaining months.

VERIFIED REPAIR

Restore the contract rule at the credit division order step: use `credit = x['annual'] * (12 - started) // 12`.

Unsuccessful approach: The attempt rounds half-up instead of flooring the credit.

Case contract

Input {annual, months_used, days_into_month, month_days, monthly}. Months started = months_used plus one if the current month has begun (days_into_month > 0), capped at 12. Credit = annual*(12 - started)//12. The credit pays whole future monthly invoices; the remainder stays as balance. Return [credit, covered_invoices, leftover].

Why this case matters

Switching billing intervals converts unused prepaid time into credit that funds future invoices.

1 / The failure

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

N = 1
observations = []
def solve(x):
    started = x['months_used'] + (1 if x['days_into_month'] > 0 else 0)
    started = min(started, 12)
    credit = x['annual'] // 12 * (12 - started)
    covered = credit // x['monthly']
    left = credit - covered * x['monthly']
    return [credit, covered, left]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'annual': 119900, 'months_used': 4, 'days_into_month': 1, 'month_days': 30, 'monthly': 999}, [69941, 70, 11]), ('regression', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('partial-repair probe', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('partial-repair probe', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('normal control', {'annual': 12000, 'months_used': 9, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [2000, 2, 2]), ('normal control', {'annual': 99900, 'months_used': 3, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [66600, 5, 2100]), ('normal control', {'annual': 99900, 'months_used': 3, 'days_into_month': 1, 'month_days': 31, 'monthly': 16870}, [66600, 3, 15990]), ('normal control', {'annual': 99900, 'months_used': 10, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [8325, 8, 325])], [('regression', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('regression', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('partial-repair probe', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('partial-repair probe', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('normal control', {'annual': 12000, 'months_used': 10, 'days_into_month': 0, 'month_days': 30, 'monthly': 12900}, [2000, 0, 2000]), ('normal control', {'annual': 119900, 'months_used': 11, 'days_into_month': 15, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 409273, 'months_used': 9, 'days_into_month': 16, 'month_days': 30, 'monthly': 999}, [68212, 68, 280]), ('normal control', {'annual': 99900, 'months_used': 0, 'days_into_month': 0, 'month_days': 30, 'monthly': 38952}, [99900, 2, 21996])], [('regression', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('regression', {'annual': 423149, 'months_used': 6, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [176312, 176, 312]), ('partial-repair probe', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083]), ('partial-repair probe', {'annual': 326637, 'months_used': 9, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [54439, 4, 2839]), ('normal control', {'annual': 99900, 'months_used': 6, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [41625, 41, 625]), ('normal control', {'annual': 23283, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [0, 0, 0]), ('normal control', {'annual': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0])], [('regression', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('regression', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('partial-repair probe', {'annual': 119900, 'months_used': 10, 'days_into_month': 1, 'month_days': 30, 'monthly': 3797}, [9991, 2, 2397]), ('partial-repair probe', {'annual': 119900, 'months_used': 5, 'days_into_month': 0, 'month_days': 30, 'monthly': 10366}, [69941, 6, 7745]), ('normal control', {'annual': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('normal control', {'annual': 99900, 'months_used': 2, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [74925, 74, 925]), ('normal control', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0])], [('regression', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083]), ('regression', {'annual': 427427, 'months_used': 3, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [284951, 22, 1151]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 0, 'month_days': 28, 'monthly': 999}, [9991, 10, 1]), ('partial-repair probe', {'annual': 78337, 'months_used': 2, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [58752, 4, 7152]), ('normal control', {'annual': 99900, 'months_used': 9, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [16650, 16, 650]), ('normal control', {'annual': 12000, 'months_used': 8, 'days_into_month': 16, 'month_days': 30, 'monthly': 10436}, [3000, 0, 3000]), ('normal control', {'annual': 99900, 'months_used': 9, 'days_into_month': 15, 'month_days': 30, 'monthly': 23011}, [16650, 0, 16650]), ('normal control', {'annual': 12000, 'months_used': 4, 'days_into_month': 0, 'month_days': 28, 'monthly': 999}, [8000, 8, 8])]]
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[69937, 70, 7][69941, 70, 11]Failed
regression 1[109901, 109, 901][109908, 109, 908]Failed
partial-repair probe 2[358236, 358, 594][358243, 358, 601]Failed
partial-repair probe 3[99910, 99, 910][99916, 99, 916]Failed
normal control 4[2000, 2, 2][2000, 2, 2]Passed
normal control 5[66600, 5, 2100][66600, 5, 2100]Passed
normal control 6[66600, 3, 15990][66600, 3, 15990]Passed
normal control 7[8325, 8, 325][8325, 8, 325]Passed

SHA-256 / a7b2bbfcc50b3f6490a1fb4f3d9d1bda29611ffe00b3891b24ac10c5c0131818

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(x):
    started = x['months_used'] + (1 if x['days_into_month'] > 0 else 0)
    started = min(started, 12)
    credit = (x['annual'] * (12 - started) + 6) // 12
    covered = credit // x['monthly']
    left = credit - covered * x['monthly']
    return [credit, covered, left]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'annual': 119900, 'months_used': 4, 'days_into_month': 1, 'month_days': 30, 'monthly': 999}, [69941, 70, 11]), ('regression', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('partial-repair probe', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('partial-repair probe', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('normal control', {'annual': 12000, 'months_used': 9, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [2000, 2, 2]), ('normal control', {'annual': 99900, 'months_used': 3, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [66600, 5, 2100]), ('normal control', {'annual': 99900, 'months_used': 3, 'days_into_month': 1, 'month_days': 31, 'monthly': 16870}, [66600, 3, 15990]), ('normal control', {'annual': 99900, 'months_used': 10, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [8325, 8, 325])], [('regression', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('regression', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('partial-repair probe', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('partial-repair probe', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('normal control', {'annual': 12000, 'months_used': 10, 'days_into_month': 0, 'month_days': 30, 'monthly': 12900}, [2000, 0, 2000]), ('normal control', {'annual': 119900, 'months_used': 11, 'days_into_month': 15, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 409273, 'months_used': 9, 'days_into_month': 16, 'month_days': 30, 'monthly': 999}, [68212, 68, 280]), ('normal control', {'annual': 99900, 'months_used': 0, 'days_into_month': 0, 'month_days': 30, 'monthly': 38952}, [99900, 2, 21996])], [('regression', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('regression', {'annual': 423149, 'months_used': 6, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [176312, 176, 312]), ('partial-repair probe', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083]), ('partial-repair probe', {'annual': 326637, 'months_used': 9, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [54439, 4, 2839]), ('normal control', {'annual': 99900, 'months_used': 6, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [41625, 41, 625]), ('normal control', {'annual': 23283, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [0, 0, 0]), ('normal control', {'annual': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0])], [('regression', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('regression', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('partial-repair probe', {'annual': 119900, 'months_used': 10, 'days_into_month': 1, 'month_days': 30, 'monthly': 3797}, [9991, 2, 2397]), ('partial-repair probe', {'annual': 119900, 'months_used': 5, 'days_into_month': 0, 'month_days': 30, 'monthly': 10366}, [69941, 6, 7745]), ('normal control', {'annual': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('normal control', {'annual': 99900, 'months_used': 2, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [74925, 74, 925]), ('normal control', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0])], [('regression', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083]), ('regression', {'annual': 427427, 'months_used': 3, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [284951, 22, 1151]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 0, 'month_days': 28, 'monthly': 999}, [9991, 10, 1]), ('partial-repair probe', {'annual': 78337, 'months_used': 2, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [58752, 4, 7152]), ('normal control', {'annual': 99900, 'months_used': 9, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [16650, 16, 650]), ('normal control', {'annual': 12000, 'months_used': 8, 'days_into_month': 16, 'month_days': 30, 'monthly': 10436}, [3000, 0, 3000]), ('normal control', {'annual': 99900, 'months_used': 9, 'days_into_month': 15, 'month_days': 30, 'monthly': 23011}, [16650, 0, 16650]), ('normal control', {'annual': 12000, 'months_used': 4, 'days_into_month': 0, 'month_days': 28, 'monthly': 999}, [8000, 8, 8])]]
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[69942, 70, 12][69941, 70, 11]Failed
regression 1[109908, 109, 908][109908, 109, 908]Passed
partial-repair probe 2[358244, 358, 602][358243, 358, 601]Failed
partial-repair probe 3[99917, 99, 917][99916, 99, 916]Failed
normal control 4[2000, 2, 2][2000, 2, 2]Passed
normal control 5[66600, 5, 2100][66600, 5, 2100]Passed
normal control 6[66600, 3, 15990][66600, 3, 15990]Passed
normal control 7[8325, 8, 325][8325, 8, 325]Passed

SHA-256 / 7d4786b3fc56f84d81f90d8b9676d4ebd7f61c008925ad22d877febf8f0362d2

3 / The verified repair

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

N = 1
observations = []
def solve(x):
    started = x['months_used'] + (1 if x['days_into_month'] > 0 else 0)
    started = min(started, 12)
    credit = x['annual'] * (12 - started) // 12
    covered = credit // x['monthly']
    left = credit - covered * x['monthly']
    return [credit, covered, left]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'annual': 119900, 'months_used': 4, 'days_into_month': 1, 'month_days': 30, 'monthly': 999}, [69941, 70, 11]), ('regression', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('partial-repair probe', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('partial-repair probe', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('normal control', {'annual': 12000, 'months_used': 9, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [2000, 2, 2]), ('normal control', {'annual': 99900, 'months_used': 3, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [66600, 5, 2100]), ('normal control', {'annual': 99900, 'months_used': 3, 'days_into_month': 1, 'month_days': 31, 'monthly': 16870}, [66600, 3, 15990]), ('normal control', {'annual': 99900, 'months_used': 10, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [8325, 8, 325])], [('regression', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('regression', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('partial-repair probe', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('partial-repair probe', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('normal control', {'annual': 12000, 'months_used': 10, 'days_into_month': 0, 'month_days': 30, 'monthly': 12900}, [2000, 0, 2000]), ('normal control', {'annual': 119900, 'months_used': 11, 'days_into_month': 15, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 409273, 'months_used': 9, 'days_into_month': 16, 'month_days': 30, 'monthly': 999}, [68212, 68, 280]), ('normal control', {'annual': 99900, 'months_used': 0, 'days_into_month': 0, 'month_days': 30, 'monthly': 38952}, [99900, 2, 21996])], [('regression', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('regression', {'annual': 423149, 'months_used': 6, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [176312, 176, 312]), ('partial-repair probe', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083]), ('partial-repair probe', {'annual': 326637, 'months_used': 9, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [54439, 4, 2839]), ('normal control', {'annual': 99900, 'months_used': 6, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [41625, 41, 625]), ('normal control', {'annual': 23283, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [0, 0, 0]), ('normal control', {'annual': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0])], [('regression', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('regression', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601]), ('partial-repair probe', {'annual': 119900, 'months_used': 10, 'days_into_month': 1, 'month_days': 30, 'monthly': 3797}, [9991, 2, 2397]), ('partial-repair probe', {'annual': 119900, 'months_used': 5, 'days_into_month': 0, 'month_days': 30, 'monthly': 10366}, [69941, 6, 7745]), ('normal control', {'annual': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('normal control', {'annual': 99900, 'months_used': 2, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [74925, 74, 925]), ('normal control', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0])], [('regression', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083]), ('regression', {'annual': 427427, 'months_used': 3, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [284951, 22, 1151]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 0, 'month_days': 28, 'monthly': 999}, [9991, 10, 1]), ('partial-repair probe', {'annual': 78337, 'months_used': 2, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [58752, 4, 7152]), ('normal control', {'annual': 99900, 'months_used': 9, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [16650, 16, 650]), ('normal control', {'annual': 12000, 'months_used': 8, 'days_into_month': 16, 'month_days': 30, 'monthly': 10436}, [3000, 0, 3000]), ('normal control', {'annual': 99900, 'months_used': 9, 'days_into_month': 15, 'month_days': 30, 'monthly': 23011}, [16650, 0, 16650]), ('normal control', {'annual': 12000, 'months_used': 4, 'days_into_month': 0, 'month_days': 28, 'monthly': 999}, [8000, 8, 8])]]
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[69941, 70, 11][69941, 70, 11]Passed
regression 1[109908, 109, 908][109908, 109, 908]Passed
partial-repair probe 2[358243, 358, 601][358243, 358, 601]Passed
partial-repair probe 3[99916, 99, 916][99916, 99, 916]Passed
normal control 4[2000, 2, 2][2000, 2, 2]Passed
normal control 5[66600, 5, 2100][66600, 5, 2100]Passed
normal control 6[66600, 3, 15990][66600, 3, 15990]Passed
normal control 7[8325, 8, 325][8325, 8, 325]Passed

SHA-256 / c76d5cf851133931b3a01d5c0428b40298ffebefd01c385ba46568f2e0d631c7

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 2026-09-29T14:46:37.442307+00:00.

Case digest / ea78c83179714df25dd6dca1aefaf691bc3c5b726d04f0df7e2c76a654672d48