FA-59721 / Subscription proration billing / Open access
Current billing period containing a date: boundary date ownership · case 01
A change on a renewal date is prorated against the period that just ended.
ROOT CAUSE
A date equal to the next period start is kept in the previous period.
VERIFIED REPAIR
Restore the contract rule at the boundary date ownership step: use `while shift(k + 1) <= t:`.
Unsuccessful approach: The attempt advances while the current start is before the date, overshooting by one period.
Case contract
Input {anchor, interval week|month|year, count, at >= anchor}. Period k starts at anchor + k*count intervals (weeks as 7 days; months/years by calendar month with the anchor day clamped to month length). Return [start ISO, end ISO] of the period with start <= at < end.
Why this case matters
Proration needs the exact period containing a change date; boundary dates belong to the new period.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
import calendar
N = 1
observations = []
def solve(x):
a = datetime.date(*x['anchor'])
t = datetime.date(*x['at'])
def shift(k):
if x['interval'] == 'week':
return a + datetime.timedelta(days=7 * x['count'] * k)
months = k * x['count'] * (12 if x['interval'] == 'year' else 1)
tot = a.month - 1 + months
y, m = a.year + tot // 12, tot % 12 + 1
return datetime.date(y, m, min(a.day, calendar.monthrange(y, m)[1]))
k = 0
while shift(k + 1) < t:
k += 1
return [shift(k).isoformat(), shift(k + 1).isoformat()]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', {'anchor': [2024, 1, 31], 'interval': 'month', 'count': 1, 'at': [2024, 2, 29]}, ['2024-02-29', '2024-03-31']), ('regression (boundary)', {'anchor': [2024, 1, 10], 'interval': 'week', 'count': 2, 'at': [2024, 1, 24]}, ['2024-01-24', '2024-02-07']), ('partial-repair probe', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('partial-repair probe', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('additional oracle', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19'])], [('regression (boundary)', {'anchor': [2024, 1, 10], 'interval': 'week', 'count': 2, 'at': [2024, 1, 24]}, ['2024-01-24', '2024-02-07']), ('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('partial-repair probe', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19']), ('partial-repair probe', {'anchor': [2020, 8, 2], 'interval': 'week', 'count': 3, 'at': [2020, 9, 4]}, ['2020-08-23', '2020-09-13']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29'])], [('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 1, 'at': [2023, 11, 30]}, ['2023-11-30', '2023-12-31']), ('partial-repair probe', {'anchor': [2021, 11, 24], 'interval': 'year', 'count': 3, 'at': [2022, 4, 18]}, ['2021-11-24', '2024-11-24']), ('partial-repair probe', {'anchor': [2020, 8, 7], 'interval': 'week', 'count': 2, 'at': [2023, 10, 30]}, ['2023-10-27', '2023-11-10']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29'])], [('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 1, 'at': [2023, 11, 30]}, ['2023-11-30', '2023-12-31']), ('regression', {'anchor': [2022, 8, 21], 'interval': 'week', 'count': 1, 'at': [2025, 1, 26]}, ['2025-01-26', '2025-02-02']), ('partial-repair probe', {'anchor': [2021, 2, 28], 'interval': 'year', 'count': 2, 'at': [2021, 8, 29]}, ['2021-02-28', '2023-02-28']), ('partial-repair probe', {'anchor': [2020, 11, 28], 'interval': 'year', 'count': 2, 'at': [2023, 9, 6]}, ['2022-11-28', '2024-11-28']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31'])], [('regression', {'anchor': [2022, 8, 21], 'interval': 'week', 'count': 1, 'at': [2025, 1, 26]}, ['2025-01-26', '2025-02-02']), ('regression', {'anchor': [2020, 7, 31], 'interval': 'month', 'count': 1, 'at': [2023, 5, 31]}, ['2023-05-31', '2023-06-30']), ('partial-repair probe', {'anchor': [2023, 12, 31], 'interval': 'month', 'count': 1, 'at': [2024, 7, 11]}, ['2024-06-30', '2024-07-31']), ('partial-repair probe', {'anchor': [2020, 2, 20], 'interval': 'month', 'count': 1, 'at': [2022, 12, 10]}, ['2022-11-20', '2022-12-20']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('additional oracle', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19'])]]
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 (boundary) 0 | ['2024-01-31', '2024-02-29'] | ['2024-02-29', '2024-03-31'] | Failed |
| regression (boundary) 1 | ['2024-01-10', '2024-01-24'] | ['2024-01-24', '2024-02-07'] | Failed |
| partial-repair probe 2 | ['2021-10-31', '2022-10-31'] | ['2021-10-31', '2022-10-31'] | Passed |
| partial-repair probe 3 | ['2023-12-31', '2024-02-29'] | ['2023-12-31', '2024-02-29'] | Passed |
| normal control 4 | ['2020-01-01', '2020-01-15'] | ['2020-01-01', '2020-01-15'] | Passed |
| additional oracle 5 | ['2023-04-30', '2023-06-30'] | ['2023-06-30', '2023-08-31'] | Failed |
| additional oracle 6 | ['2025-05-19', '2026-05-19'] | ['2025-05-19', '2026-05-19'] | Passed |
SHA-256 / d41b23141488d8634e1f0cfe4342897dbeb87c260fa55f44e97bfde715d37d53
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
import calendar
N = 1
observations = []
def solve(x):
a = datetime.date(*x['anchor'])
t = datetime.date(*x['at'])
def shift(k):
if x['interval'] == 'week':
return a + datetime.timedelta(days=7 * x['count'] * k)
months = k * x['count'] * (12 if x['interval'] == 'year' else 1)
tot = a.month - 1 + months
y, m = a.year + tot // 12, tot % 12 + 1
return datetime.date(y, m, min(a.day, calendar.monthrange(y, m)[1]))
k = 0
while shift(k) < t:
k += 1
return [shift(k).isoformat(), shift(k + 1).isoformat()]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', {'anchor': [2024, 1, 31], 'interval': 'month', 'count': 1, 'at': [2024, 2, 29]}, ['2024-02-29', '2024-03-31']), ('regression (boundary)', {'anchor': [2024, 1, 10], 'interval': 'week', 'count': 2, 'at': [2024, 1, 24]}, ['2024-01-24', '2024-02-07']), ('partial-repair probe', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('partial-repair probe', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('additional oracle', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19'])], [('regression (boundary)', {'anchor': [2024, 1, 10], 'interval': 'week', 'count': 2, 'at': [2024, 1, 24]}, ['2024-01-24', '2024-02-07']), ('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('partial-repair probe', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19']), ('partial-repair probe', {'anchor': [2020, 8, 2], 'interval': 'week', 'count': 3, 'at': [2020, 9, 4]}, ['2020-08-23', '2020-09-13']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29'])], [('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 1, 'at': [2023, 11, 30]}, ['2023-11-30', '2023-12-31']), ('partial-repair probe', {'anchor': [2021, 11, 24], 'interval': 'year', 'count': 3, 'at': [2022, 4, 18]}, ['2021-11-24', '2024-11-24']), ('partial-repair probe', {'anchor': [2020, 8, 7], 'interval': 'week', 'count': 2, 'at': [2023, 10, 30]}, ['2023-10-27', '2023-11-10']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29'])], [('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 1, 'at': [2023, 11, 30]}, ['2023-11-30', '2023-12-31']), ('regression', {'anchor': [2022, 8, 21], 'interval': 'week', 'count': 1, 'at': [2025, 1, 26]}, ['2025-01-26', '2025-02-02']), ('partial-repair probe', {'anchor': [2021, 2, 28], 'interval': 'year', 'count': 2, 'at': [2021, 8, 29]}, ['2021-02-28', '2023-02-28']), ('partial-repair probe', {'anchor': [2020, 11, 28], 'interval': 'year', 'count': 2, 'at': [2023, 9, 6]}, ['2022-11-28', '2024-11-28']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31'])], [('regression', {'anchor': [2022, 8, 21], 'interval': 'week', 'count': 1, 'at': [2025, 1, 26]}, ['2025-01-26', '2025-02-02']), ('regression', {'anchor': [2020, 7, 31], 'interval': 'month', 'count': 1, 'at': [2023, 5, 31]}, ['2023-05-31', '2023-06-30']), ('partial-repair probe', {'anchor': [2023, 12, 31], 'interval': 'month', 'count': 1, 'at': [2024, 7, 11]}, ['2024-06-30', '2024-07-31']), ('partial-repair probe', {'anchor': [2020, 2, 20], 'interval': 'month', 'count': 1, 'at': [2022, 12, 10]}, ['2022-11-20', '2022-12-20']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('additional oracle', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19'])]]
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 (boundary) 0 | ['2024-02-29', '2024-03-31'] | ['2024-02-29', '2024-03-31'] | Passed |
| regression (boundary) 1 | ['2024-01-24', '2024-02-07'] | ['2024-01-24', '2024-02-07'] | Passed |
| partial-repair probe 2 | ['2022-10-31', '2023-10-31'] | ['2021-10-31', '2022-10-31'] | Failed |
| partial-repair probe 3 | ['2024-02-29', '2024-04-30'] | ['2023-12-31', '2024-02-29'] | Failed |
| normal control 4 | ['2020-01-01', '2020-01-15'] | ['2020-01-01', '2020-01-15'] | Passed |
| additional oracle 5 | ['2023-06-30', '2023-08-31'] | ['2023-06-30', '2023-08-31'] | Passed |
| additional oracle 6 | ['2026-05-19', '2027-05-19'] | ['2025-05-19', '2026-05-19'] | Failed |
SHA-256 / 972331d3ea0763b7f21605f9def1368904203e20532ee8ff6bfbbe04c53e1adc
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
import calendar
N = 1
observations = []
def solve(x):
a = datetime.date(*x['anchor'])
t = datetime.date(*x['at'])
def shift(k):
if x['interval'] == 'week':
return a + datetime.timedelta(days=7 * x['count'] * k)
months = k * x['count'] * (12 if x['interval'] == 'year' else 1)
tot = a.month - 1 + months
y, m = a.year + tot // 12, tot % 12 + 1
return datetime.date(y, m, min(a.day, calendar.monthrange(y, m)[1]))
k = 0
while shift(k + 1) <= t:
k += 1
return [shift(k).isoformat(), shift(k + 1).isoformat()]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', {'anchor': [2024, 1, 31], 'interval': 'month', 'count': 1, 'at': [2024, 2, 29]}, ['2024-02-29', '2024-03-31']), ('regression (boundary)', {'anchor': [2024, 1, 10], 'interval': 'week', 'count': 2, 'at': [2024, 1, 24]}, ['2024-01-24', '2024-02-07']), ('partial-repair probe', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('partial-repair probe', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('additional oracle', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19'])], [('regression (boundary)', {'anchor': [2024, 1, 10], 'interval': 'week', 'count': 2, 'at': [2024, 1, 24]}, ['2024-01-24', '2024-02-07']), ('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('partial-repair probe', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19']), ('partial-repair probe', {'anchor': [2020, 8, 2], 'interval': 'week', 'count': 3, 'at': [2020, 9, 4]}, ['2020-08-23', '2020-09-13']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29'])], [('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 1, 'at': [2023, 11, 30]}, ['2023-11-30', '2023-12-31']), ('partial-repair probe', {'anchor': [2021, 11, 24], 'interval': 'year', 'count': 3, 'at': [2022, 4, 18]}, ['2021-11-24', '2024-11-24']), ('partial-repair probe', {'anchor': [2020, 8, 7], 'interval': 'week', 'count': 2, 'at': [2023, 10, 30]}, ['2023-10-27', '2023-11-10']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2020, 10, 31], 'interval': 'year', 'count': 1, 'at': [2022, 2, 15]}, ['2021-10-31', '2022-10-31']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29'])], [('regression', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 1, 'at': [2023, 11, 30]}, ['2023-11-30', '2023-12-31']), ('regression', {'anchor': [2022, 8, 21], 'interval': 'week', 'count': 1, 'at': [2025, 1, 26]}, ['2025-01-26', '2025-02-02']), ('partial-repair probe', {'anchor': [2021, 2, 28], 'interval': 'year', 'count': 2, 'at': [2021, 8, 29]}, ['2021-02-28', '2023-02-28']), ('partial-repair probe', {'anchor': [2020, 11, 28], 'interval': 'year', 'count': 2, 'at': [2023, 9, 6]}, ['2022-11-28', '2024-11-28']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2023, 10, 31], 'interval': 'month', 'count': 2, 'at': [2024, 2, 3]}, ['2023-12-31', '2024-02-29']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31'])], [('regression', {'anchor': [2022, 8, 21], 'interval': 'week', 'count': 1, 'at': [2025, 1, 26]}, ['2025-01-26', '2025-02-02']), ('regression', {'anchor': [2020, 7, 31], 'interval': 'month', 'count': 1, 'at': [2023, 5, 31]}, ['2023-05-31', '2023-06-30']), ('partial-repair probe', {'anchor': [2023, 12, 31], 'interval': 'month', 'count': 1, 'at': [2024, 7, 11]}, ['2024-06-30', '2024-07-31']), ('partial-repair probe', {'anchor': [2020, 2, 20], 'interval': 'month', 'count': 1, 'at': [2022, 12, 10]}, ['2022-11-20', '2022-12-20']), ('normal control', {'anchor': [2020, 1, 1], 'interval': 'week', 'count': 2, 'at': [2020, 1, 1]}, ['2020-01-01', '2020-01-15']), ('additional oracle', {'anchor': [2022, 10, 31], 'interval': 'month', 'count': 2, 'at': [2023, 6, 30]}, ['2023-06-30', '2023-08-31']), ('additional oracle', {'anchor': [2023, 5, 19], 'interval': 'year', 'count': 1, 'at': [2025, 8, 18]}, ['2025-05-19', '2026-05-19'])]]
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 (boundary) 0 | ['2024-02-29', '2024-03-31'] | ['2024-02-29', '2024-03-31'] | Passed |
| regression (boundary) 1 | ['2024-01-24', '2024-02-07'] | ['2024-01-24', '2024-02-07'] | Passed |
| partial-repair probe 2 | ['2021-10-31', '2022-10-31'] | ['2021-10-31', '2022-10-31'] | Passed |
| partial-repair probe 3 | ['2023-12-31', '2024-02-29'] | ['2023-12-31', '2024-02-29'] | Passed |
| normal control 4 | ['2020-01-01', '2020-01-15'] | ['2020-01-01', '2020-01-15'] | Passed |
| additional oracle 5 | ['2023-06-30', '2023-08-31'] | ['2023-06-30', '2023-08-31'] | Passed |
| additional oracle 6 | ['2025-05-19', '2026-05-19'] | ['2025-05-19', '2026-05-19'] | Passed |
SHA-256 / 9f4fc47716d9b58fd0eac9503bd482053c4034be156da8b5af1783f486e466f6
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 2026-09-29T14:46:38.871657+00:00.
Case digest / 697a451b8f05ea9c63b05bb7ff806fec68070dbd5b2e6515909dd432c526fd8c