FA-59546 / Subscription proration billing / Open access
Annual-to-monthly switch credit: started month cap · case 01
Customers switching after the annual term produce a negative credit.
ROOT CAUSE
The number of started months is not capped at twelve.
VERIFIED REPAIR
Restore the contract rule at the started month cap step: use `started = min(started, 12)`.
Unsuccessful approach: The attempt caps at eleven, crediting a month on expired annual plans.
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 = max(started, 0)
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': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 15, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 23283, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [0, 0, 0]), ('normal control', {'annual': 12000, 'months_used': 9, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [2000, 2, 2]), ('normal control', {'annual': 119900, 'months_used': 4, 'days_into_month': 1, 'month_days': 30, 'monthly': 999}, [69941, 70, 11]), ('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])], [('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('partial-repair probe', {'annual': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('normal control', {'annual': 99900, 'months_used': 10, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [8325, 8, 325]), ('normal control', {'annual': 12000, 'months_used': 10, 'days_into_month': 0, 'month_days': 30, 'monthly': 12900}, [2000, 0, 2000]), ('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': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('normal control', {'annual': 423149, 'months_used': 6, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [176312, 176, 312]), ('normal control', {'annual': 99900, 'months_used': 6, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [41625, 41, 625]), ('normal control', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601])], [('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0]), ('partial-repair probe', {'annual': 12000, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 20303}, [0, 0, 0]), ('normal control', {'annual': 427427, 'months_used': 3, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [284951, 22, 1151]), ('normal control', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('normal control', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('normal control', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083])], [('regression', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 12000, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 20303}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [0, 0, 0]), ('partial-repair probe', {'annual': 99900, 'months_used': 13, 'days_into_month': 15, 'month_days': 30, 'monthly': 12900}, [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': 3, 'days_into_month': 23, 'month_days': 28, 'monthly': 12900}, [79933, 6, 2533]), ('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])]]
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 | [-1000, -2, 998] | [0, 0, 0] | Failed |
| regression 1 | [-9992, -11, 997] | [0, 0, 0] | Failed |
| partial-repair probe 2 | [0, 0, 0] | [0, 0, 0] | Passed |
| partial-repair probe 3 | [0, 0, 0] | [0, 0, 0] | Passed |
| normal control 4 | [2000, 2, 2] | [2000, 2, 2] | Passed |
| normal control 5 | [69941, 70, 11] | [69941, 70, 11] | Passed |
| normal control 6 | [66600, 5, 2100] | [66600, 5, 2100] | Passed |
| normal control 7 | [66600, 3, 15990] | [66600, 3, 15990] | Passed |
SHA-256 / de6ae10a079a4fad4614e229da3503ea1f58b0476c00f734d875f10803db4d41
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, 11)
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': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 15, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 23283, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [0, 0, 0]), ('normal control', {'annual': 12000, 'months_used': 9, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [2000, 2, 2]), ('normal control', {'annual': 119900, 'months_used': 4, 'days_into_month': 1, 'month_days': 30, 'monthly': 999}, [69941, 70, 11]), ('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])], [('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('partial-repair probe', {'annual': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('normal control', {'annual': 99900, 'months_used': 10, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [8325, 8, 325]), ('normal control', {'annual': 12000, 'months_used': 10, 'days_into_month': 0, 'month_days': 30, 'monthly': 12900}, [2000, 0, 2000]), ('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': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('normal control', {'annual': 423149, 'months_used': 6, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [176312, 176, 312]), ('normal control', {'annual': 99900, 'months_used': 6, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [41625, 41, 625]), ('normal control', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601])], [('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0]), ('partial-repair probe', {'annual': 12000, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 20303}, [0, 0, 0]), ('normal control', {'annual': 427427, 'months_used': 3, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [284951, 22, 1151]), ('normal control', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('normal control', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('normal control', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083])], [('regression', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 12000, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 20303}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [0, 0, 0]), ('partial-repair probe', {'annual': 99900, 'months_used': 13, 'days_into_month': 15, 'month_days': 30, 'monthly': 12900}, [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': 3, 'days_into_month': 23, 'month_days': 28, 'monthly': 12900}, [79933, 6, 2533]), ('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])]]
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 | [1000, 1, 1] | [0, 0, 0] | Failed |
| regression 1 | [9991, 10, 1] | [0, 0, 0] | Failed |
| partial-repair probe 2 | [9991, 10, 1] | [0, 0, 0] | Failed |
| partial-repair probe 3 | [1940, 0, 1940] | [0, 0, 0] | Failed |
| normal control 4 | [2000, 2, 2] | [2000, 2, 2] | Passed |
| normal control 5 | [69941, 70, 11] | [69941, 70, 11] | Passed |
| normal control 6 | [66600, 5, 2100] | [66600, 5, 2100] | Passed |
| normal control 7 | [66600, 3, 15990] | [66600, 3, 15990] | Passed |
SHA-256 / 18d6c5f63a5dde3976bb985660989f17131433800fec9eaab8c5a18b1a87f7e1
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': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 15, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 23283, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [0, 0, 0]), ('normal control', {'annual': 12000, 'months_used': 9, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [2000, 2, 2]), ('normal control', {'annual': 119900, 'months_used': 4, 'days_into_month': 1, 'month_days': 30, 'monthly': 999}, [69941, 70, 11]), ('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])], [('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 26, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('partial-repair probe', {'annual': 12000, 'months_used': 12, 'days_into_month': 15, 'month_days': 30, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('normal control', {'annual': 99900, 'months_used': 10, 'days_into_month': 15, 'month_days': 31, 'monthly': 1000}, [8325, 8, 325]), ('normal control', {'annual': 12000, 'months_used': 10, 'days_into_month': 0, 'month_days': 30, 'monthly': 12900}, [2000, 0, 2000]), ('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': 99900, 'months_used': 13, 'days_into_month': 0, 'month_days': 30, 'monthly': 27694}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('normal control', {'annual': 119900, 'months_used': 0, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [109908, 109, 908]), ('normal control', {'annual': 423149, 'months_used': 6, 'days_into_month': 16, 'month_days': 28, 'monthly': 1000}, [176312, 176, 312]), ('normal control', {'annual': 99900, 'months_used': 6, 'days_into_month': 15, 'month_days': 28, 'monthly': 1000}, [41625, 41, 625]), ('normal control', {'annual': 477658, 'months_used': 2, 'days_into_month': 16, 'month_days': 31, 'monthly': 999}, [358243, 358, 601])], [('regression', {'annual': 119900, 'months_used': 12, 'days_into_month': 10, 'month_days': 28, 'monthly': 9029}, [0, 0, 0]), ('regression', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 11, 'days_into_month': 16, 'month_days': 31, 'monthly': 1512}, [0, 0, 0]), ('partial-repair probe', {'annual': 12000, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 20303}, [0, 0, 0]), ('normal control', {'annual': 427427, 'months_used': 3, 'days_into_month': 16, 'month_days': 31, 'monthly': 12900}, [284951, 22, 1151]), ('normal control', {'annual': 119900, 'months_used': 2, 'days_into_month': 0, 'month_days': 31, 'monthly': 1000}, [99916, 99, 916]), ('normal control', {'annual': 422066, 'months_used': 1, 'days_into_month': 1, 'month_days': 31, 'monthly': 1000}, [351721, 351, 721]), ('normal control', {'annual': 228567, 'months_used': 5, 'days_into_month': 1, 'month_days': 31, 'monthly': 12900}, [114283, 8, 11083])], [('regression', {'annual': 119900, 'months_used': 13, 'days_into_month': 16, 'month_days': 28, 'monthly': 999}, [0, 0, 0]), ('regression', {'annual': 12000, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 20303}, [0, 0, 0]), ('partial-repair probe', {'annual': 119900, 'months_used': 12, 'days_into_month': 16, 'month_days': 28, 'monthly': 12900}, [0, 0, 0]), ('partial-repair probe', {'annual': 99900, 'months_used': 13, 'days_into_month': 15, 'month_days': 30, 'monthly': 12900}, [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': 3, 'days_into_month': 23, 'month_days': 28, 'monthly': 12900}, [79933, 6, 2533]), ('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])]]
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 | [0, 0, 0] | [0, 0, 0] | Passed |
| regression 1 | [0, 0, 0] | [0, 0, 0] | Passed |
| partial-repair probe 2 | [0, 0, 0] | [0, 0, 0] | Passed |
| partial-repair probe 3 | [0, 0, 0] | [0, 0, 0] | Passed |
| normal control 4 | [2000, 2, 2] | [2000, 2, 2] | Passed |
| normal control 5 | [69941, 70, 11] | [69941, 70, 11] | Passed |
| normal control 6 | [66600, 5, 2100] | [66600, 5, 2100] | Passed |
| normal control 7 | [66600, 3, 15990] | [66600, 3, 15990] | Passed |
SHA-256 / a966087647c34324cb27207125321495a8e96814fbe6f3433b97c8620e697002
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 2026-09-29T14:46:37.379902+00:00.
Case digest / 44a697a2596beb4daac1bdaed8d2ffd311a6cf016df5959e43eb1c5e354fa48e