{"abstract":"Terms that include Feb 29 prorate at a daily rate that sums to more than the annual price.","category":"Subscription proration billing","checks":8,"contract":"Input {price, start (annual term start), from, to (exclusive)}. The term is [start, same date next year); a Feb 29 start ends on Mar 1. The requested range is clipped to the term; days = max(0, clipped length). Amount = price*days/term_days half-up. Return [term_days, amount].","evaluation_group":"w2-subscription-proration-annual-leap-proration","failed_approach":"The attempt checks whether the start year is leap, which is wrong for terms starting after February or spanning the next February.","family":"w2-subscription-proration-annual-leap-proration-term-length","id":"FA-59626","implementations":{"attempt":{"sha256":"cc77505056a758431e7d43b31a7802a34af4a38da8ed0c497fad04cfeca2b517","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nimport calendar\nN = 1\nobservations = []\ndef solve(x):\n    s = datetime.date(*x['start'])\n    try:\n        e = s.replace(year=s.year + 1)\n    except ValueError:\n        e = datetime.date(s.year + 1, 3, 1)\n    f = max(datetime.date(*x['from']), s)\n    t = min(datetime.date(*x['to']), e)\n    days = max(0, (t - f).days)\n    total = 366 if calendar.isleap(s.year) else 365\n    return [total, (x['price'] * days * 2 + total) // (2 * total)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'price': 36600, 'start': [2023, 6, 28], 'from': [2024, 5, 21], 'to': [2024, 9, 14]}, [366, 3800]), ('regression', {'price': 36600, 'start': [2024, 1, 24], 'from': [2024, 4, 28], 'to': [2024, 5, 27]}, [366, 2900]), ('partial-repair probe', {'price': 617594, 'start': [2024, 4, 7], 'from': [2024, 5, 22], 'to': [2024, 7, 8]}, [365, 79526]), ('partial-repair probe', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('normal control', {'price': 119900, 'start': [2022, 1, 5], 'from': [2022, 2, 1], 'to': [2022, 5, 13]}, [365, 33178]), ('normal control', {'price': 119900, 'start': [2025, 7, 19], 'from': [2025, 8, 25], 'to': [2026, 1, 13]}, [365, 46318]), ('normal control', {'price': 401416, 'start': [2025, 2, 14], 'from': [2026, 1, 26], 'to': [2026, 2, 15]}, [365, 20896]), ('normal control', {'price': 36500, 'start': [2025, 4, 22], 'from': [2025, 10, 5], 'to': [2026, 2, 9]}, [365, 12700])], [('regression', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('regression', {'price': 36600, 'start': [2024, 1, 24], 'from': [2024, 4, 28], 'to': [2024, 5, 27]}, [366, 2900]), ('partial-repair probe', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('partial-repair probe', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('normal control', {'price': 100385, 'start': [2025, 10, 15], 'from': [2026, 10, 7], 'to': [2026, 10, 15]}, [365, 2200]), ('normal control', {'price': 36500, 'start': [2022, 5, 14], 'from': [2022, 8, 4], 'to': [2022, 11, 25]}, [365, 11300]), ('normal control', {'price': 835323, 'start': [2022, 3, 25], 'from': [2023, 1, 26], 'to': [2023, 6, 21]}, [365, 132736]), ('normal control', {'price': 36600, 'start': [2023, 1, 26], 'from': [2023, 10, 15], 'to': [2024, 3, 11]}, [365, 10328])], [('regression', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('regression', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('partial-repair probe', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('partial-repair probe', {'price': 36600, 'start': [2023, 4, 2], 'from': [2024, 2, 21], 'to': [2024, 5, 8]}, [366, 4100]), ('normal control', {'price': 36500, 'start': [2023, 2, 11], 'from': [2023, 11, 18], 'to': [2024, 3, 12]}, [365, 8500]), ('normal control', {'price': 119900, 'start': [2022, 10, 30], 'from': [2023, 10, 17], 'to': [2023, 10, 22]}, [365, 1642]), ('normal control', {'price': 36500, 'start': [2022, 2, 26], 'from': [2023, 1, 27], 'to': [2023, 5, 30]}, [365, 3000]), ('normal control', {'price': 36500, 'start': [2025, 11, 17], 'from': [2026, 9, 5], 'to': [2027, 3, 8]}, [365, 7300])], [('regression', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('regression', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('partial-repair probe', {'price': 119900, 'start': [2024, 6, 29], 'from': [2024, 9, 5], 'to': [2025, 3, 15]}, [365, 62742]), ('partial-repair probe', {'price': 36600, 'start': [2023, 8, 24], 'from': [2024, 4, 5], 'to': [2024, 5, 29]}, [366, 5400]), ('normal control', {'price': 119900, 'start': [2025, 10, 17], 'from': [2026, 10, 6], 'to': [2026, 10, 7]}, [365, 328]), ('normal control', {'price': 119900, 'start': [2025, 1, 2], 'from': [2025, 9, 22], 'to': [2025, 9, 22]}, [365, 0]), ('normal control', {'price': 36600, 'start': [2022, 8, 24], 'from': [2022, 10, 17], 'to': [2022, 12, 26]}, [365, 7019]), ('normal control', {'price': 36500, 'start': [2022, 11, 10], 'from': [2023, 9, 22], 'to': [2024, 1, 1]}, [365, 4900])], [('regression', {'price': 36600, 'start': [2023, 4, 2], 'from': [2024, 2, 21], 'to': [2024, 5, 8]}, [366, 4100]), ('regression', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('partial-repair probe', {'price': 36600, 'start': [2023, 5, 5], 'from': [2023, 7, 11], 'to': [2023, 7, 15]}, [366, 400]), ('partial-repair probe', {'price': 119900, 'start': [2023, 8, 26], 'from': [2023, 12, 8], 'to': [2023, 12, 16]}, [366, 2621]), ('normal control', {'price': 36500, 'start': [2022, 1, 31], 'from': [2022, 12, 24], 'to': [2023, 1, 10]}, [365, 1700]), ('normal control', {'price': 964155, 'start': [2022, 10, 13], 'from': [2023, 2, 27], 'to': [2023, 4, 11]}, [365, 113585]), ('normal control', {'price': 382740, 'start': [2022, 5, 28], 'from': [2022, 9, 1], 'to': [2022, 9, 17]}, [365, 16778]), ('normal control', {'price': 119900, 'start': [2022, 5, 10], 'from': [2023, 3, 14], 'to': [2023, 9, 22]}, [365, 18724])]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"536989464cefb9a3ae1a2abd719aa1961ddb29e90146d51dd26462718e889ffb","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nimport calendar\nN = 1\nobservations = []\ndef solve(x):\n    s = datetime.date(*x['start'])\n    try:\n        e = s.replace(year=s.year + 1)\n    except ValueError:\n        e = datetime.date(s.year + 1, 3, 1)\n    f = max(datetime.date(*x['from']), s)\n    t = min(datetime.date(*x['to']), e)\n    days = max(0, (t - f).days)\n    total = 365\n    return [total, (x['price'] * days * 2 + total) // (2 * total)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'price': 36600, 'start': [2023, 6, 28], 'from': [2024, 5, 21], 'to': [2024, 9, 14]}, [366, 3800]), ('regression', {'price': 36600, 'start': [2024, 1, 24], 'from': [2024, 4, 28], 'to': [2024, 5, 27]}, [366, 2900]), ('partial-repair probe', {'price': 617594, 'start': [2024, 4, 7], 'from': [2024, 5, 22], 'to': [2024, 7, 8]}, [365, 79526]), ('partial-repair probe', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('normal control', {'price': 119900, 'start': [2022, 1, 5], 'from': [2022, 2, 1], 'to': [2022, 5, 13]}, [365, 33178]), ('normal control', {'price': 119900, 'start': [2025, 7, 19], 'from': [2025, 8, 25], 'to': [2026, 1, 13]}, [365, 46318]), ('normal control', {'price': 401416, 'start': [2025, 2, 14], 'from': [2026, 1, 26], 'to': [2026, 2, 15]}, [365, 20896]), ('normal control', {'price': 36500, 'start': [2025, 4, 22], 'from': [2025, 10, 5], 'to': [2026, 2, 9]}, [365, 12700])], [('regression', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('regression', {'price': 36600, 'start': [2024, 1, 24], 'from': [2024, 4, 28], 'to': [2024, 5, 27]}, [366, 2900]), ('partial-repair probe', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('partial-repair probe', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('normal control', {'price': 100385, 'start': [2025, 10, 15], 'from': [2026, 10, 7], 'to': [2026, 10, 15]}, [365, 2200]), ('normal control', {'price': 36500, 'start': [2022, 5, 14], 'from': [2022, 8, 4], 'to': [2022, 11, 25]}, [365, 11300]), ('normal control', {'price': 835323, 'start': [2022, 3, 25], 'from': [2023, 1, 26], 'to': [2023, 6, 21]}, [365, 132736]), ('normal control', {'price': 36600, 'start': [2023, 1, 26], 'from': [2023, 10, 15], 'to': [2024, 3, 11]}, [365, 10328])], [('regression', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('regression', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('partial-repair probe', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('partial-repair probe', {'price': 36600, 'start': [2023, 4, 2], 'from': [2024, 2, 21], 'to': [2024, 5, 8]}, [366, 4100]), ('normal control', {'price': 36500, 'start': [2023, 2, 11], 'from': [2023, 11, 18], 'to': [2024, 3, 12]}, [365, 8500]), ('normal control', {'price': 119900, 'start': [2022, 10, 30], 'from': [2023, 10, 17], 'to': [2023, 10, 22]}, [365, 1642]), ('normal control', {'price': 36500, 'start': [2022, 2, 26], 'from': [2023, 1, 27], 'to': [2023, 5, 30]}, [365, 3000]), ('normal control', {'price': 36500, 'start': [2025, 11, 17], 'from': [2026, 9, 5], 'to': [2027, 3, 8]}, [365, 7300])], [('regression', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('regression', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('partial-repair probe', {'price': 119900, 'start': [2024, 6, 29], 'from': [2024, 9, 5], 'to': [2025, 3, 15]}, [365, 62742]), ('partial-repair probe', {'price': 36600, 'start': [2023, 8, 24], 'from': [2024, 4, 5], 'to': [2024, 5, 29]}, [366, 5400]), ('normal control', {'price': 119900, 'start': [2025, 10, 17], 'from': [2026, 10, 6], 'to': [2026, 10, 7]}, [365, 328]), ('normal control', {'price': 119900, 'start': [2025, 1, 2], 'from': [2025, 9, 22], 'to': [2025, 9, 22]}, [365, 0]), ('normal control', {'price': 36600, 'start': [2022, 8, 24], 'from': [2022, 10, 17], 'to': [2022, 12, 26]}, [365, 7019]), ('normal control', {'price': 36500, 'start': [2022, 11, 10], 'from': [2023, 9, 22], 'to': [2024, 1, 1]}, [365, 4900])], [('regression', {'price': 36600, 'start': [2023, 4, 2], 'from': [2024, 2, 21], 'to': [2024, 5, 8]}, [366, 4100]), ('regression', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('partial-repair probe', {'price': 36600, 'start': [2023, 5, 5], 'from': [2023, 7, 11], 'to': [2023, 7, 15]}, [366, 400]), ('partial-repair probe', {'price': 119900, 'start': [2023, 8, 26], 'from': [2023, 12, 8], 'to': [2023, 12, 16]}, [366, 2621]), ('normal control', {'price': 36500, 'start': [2022, 1, 31], 'from': [2022, 12, 24], 'to': [2023, 1, 10]}, [365, 1700]), ('normal control', {'price': 964155, 'start': [2022, 10, 13], 'from': [2023, 2, 27], 'to': [2023, 4, 11]}, [365, 113585]), ('normal control', {'price': 382740, 'start': [2022, 5, 28], 'from': [2022, 9, 1], 'to': [2022, 9, 17]}, [365, 16778]), ('normal control', {'price': 119900, 'start': [2022, 5, 10], 'from': [2023, 3, 14], 'to': [2023, 9, 22]}, [365, 18724])]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"7b34393811d274daa7e3fa6ca0e4e935646eda47248295db4ba732e1988503a3","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nimport calendar\nN = 1\nobservations = []\ndef solve(x):\n    s = datetime.date(*x['start'])\n    try:\n        e = s.replace(year=s.year + 1)\n    except ValueError:\n        e = datetime.date(s.year + 1, 3, 1)\n    f = max(datetime.date(*x['from']), s)\n    t = min(datetime.date(*x['to']), e)\n    days = max(0, (t - f).days)\n    total = (e - s).days\n    return [total, (x['price'] * days * 2 + total) // (2 * total)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'price': 36600, 'start': [2023, 6, 28], 'from': [2024, 5, 21], 'to': [2024, 9, 14]}, [366, 3800]), ('regression', {'price': 36600, 'start': [2024, 1, 24], 'from': [2024, 4, 28], 'to': [2024, 5, 27]}, [366, 2900]), ('partial-repair probe', {'price': 617594, 'start': [2024, 4, 7], 'from': [2024, 5, 22], 'to': [2024, 7, 8]}, [365, 79526]), ('partial-repair probe', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('normal control', {'price': 119900, 'start': [2022, 1, 5], 'from': [2022, 2, 1], 'to': [2022, 5, 13]}, [365, 33178]), ('normal control', {'price': 119900, 'start': [2025, 7, 19], 'from': [2025, 8, 25], 'to': [2026, 1, 13]}, [365, 46318]), ('normal control', {'price': 401416, 'start': [2025, 2, 14], 'from': [2026, 1, 26], 'to': [2026, 2, 15]}, [365, 20896]), ('normal control', {'price': 36500, 'start': [2025, 4, 22], 'from': [2025, 10, 5], 'to': [2026, 2, 9]}, [365, 12700])], [('regression', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('regression', {'price': 36600, 'start': [2024, 1, 24], 'from': [2024, 4, 28], 'to': [2024, 5, 27]}, [366, 2900]), ('partial-repair probe', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('partial-repair probe', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('normal control', {'price': 100385, 'start': [2025, 10, 15], 'from': [2026, 10, 7], 'to': [2026, 10, 15]}, [365, 2200]), ('normal control', {'price': 36500, 'start': [2022, 5, 14], 'from': [2022, 8, 4], 'to': [2022, 11, 25]}, [365, 11300]), ('normal control', {'price': 835323, 'start': [2022, 3, 25], 'from': [2023, 1, 26], 'to': [2023, 6, 21]}, [365, 132736]), ('normal control', {'price': 36600, 'start': [2023, 1, 26], 'from': [2023, 10, 15], 'to': [2024, 3, 11]}, [365, 10328])], [('regression', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('regression', {'price': 975972, 'start': [2023, 4, 12], 'from': [2023, 10, 30], 'to': [2024, 1, 23]}, [366, 226660]), ('partial-repair probe', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('partial-repair probe', {'price': 36600, 'start': [2023, 4, 2], 'from': [2024, 2, 21], 'to': [2024, 5, 8]}, [366, 4100]), ('normal control', {'price': 36500, 'start': [2023, 2, 11], 'from': [2023, 11, 18], 'to': [2024, 3, 12]}, [365, 8500]), ('normal control', {'price': 119900, 'start': [2022, 10, 30], 'from': [2023, 10, 17], 'to': [2023, 10, 22]}, [365, 1642]), ('normal control', {'price': 36500, 'start': [2022, 2, 26], 'from': [2023, 1, 27], 'to': [2023, 5, 30]}, [365, 3000]), ('normal control', {'price': 36500, 'start': [2025, 11, 17], 'from': [2026, 9, 5], 'to': [2027, 3, 8]}, [365, 7300])], [('regression', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('regression', {'price': 505919, 'start': [2023, 11, 8], 'from': [2024, 5, 2], 'to': [2024, 8, 16]}, [366, 146523]), ('partial-repair probe', {'price': 119900, 'start': [2024, 6, 29], 'from': [2024, 9, 5], 'to': [2025, 3, 15]}, [365, 62742]), ('partial-repair probe', {'price': 36600, 'start': [2023, 8, 24], 'from': [2024, 4, 5], 'to': [2024, 5, 29]}, [366, 5400]), ('normal control', {'price': 119900, 'start': [2025, 10, 17], 'from': [2026, 10, 6], 'to': [2026, 10, 7]}, [365, 328]), ('normal control', {'price': 119900, 'start': [2025, 1, 2], 'from': [2025, 9, 22], 'to': [2025, 9, 22]}, [365, 0]), ('normal control', {'price': 36600, 'start': [2022, 8, 24], 'from': [2022, 10, 17], 'to': [2022, 12, 26]}, [365, 7019]), ('normal control', {'price': 36500, 'start': [2022, 11, 10], 'from': [2023, 9, 22], 'to': [2024, 1, 1]}, [365, 4900])], [('regression', {'price': 36600, 'start': [2023, 4, 2], 'from': [2024, 2, 21], 'to': [2024, 5, 8]}, [366, 4100]), ('regression', {'price': 36600, 'start': [2023, 7, 11], 'from': [2024, 5, 20], 'to': [2024, 7, 20]}, [366, 5200]), ('partial-repair probe', {'price': 36600, 'start': [2023, 5, 5], 'from': [2023, 7, 11], 'to': [2023, 7, 15]}, [366, 400]), ('partial-repair probe', {'price': 119900, 'start': [2023, 8, 26], 'from': [2023, 12, 8], 'to': [2023, 12, 16]}, [366, 2621]), ('normal control', {'price': 36500, 'start': [2022, 1, 31], 'from': [2022, 12, 24], 'to': [2023, 1, 10]}, [365, 1700]), ('normal control', {'price': 964155, 'start': [2022, 10, 13], 'from': [2023, 2, 27], 'to': [2023, 4, 11]}, [365, 113585]), ('normal control', {'price': 382740, 'start': [2022, 5, 28], 'from': [2022, 9, 1], 'to': [2022, 9, 17]}, [365, 16778]), ('normal control', {'price': 119900, 'start': [2022, 5, 10], 'from': [2023, 3, 14], 'to': [2023, 9, 22]}, [365, 18724])]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-subscription-proration-annual-leap-proration-term-length","generated_at":"2026-09-29T14:46:38.136944+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Annual terms have 365 or 366 days depending on whether they contain Feb 29, which changes the daily rate.","repair":"Restore the contract rule at the term length step: use `total = (e - s).days`.","root_cause":"Every term is assumed to have 365 days.","sha256":"0fdf192346b95f54ded2babab255f9117c639a33a4843093e2d6ec11ae73b655","title":"Annual plan proration across leap years: term length · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":44.111,"exit_code":1,"observations":[{"actual":[365,3810],"check":"regression 0","expected":[366,3800],"passed":false},{"actual":[366,2900],"check":"regression 1","expected":[366,2900],"passed":true},{"actual":[366,79309],"check":"partial-repair probe 2","expected":[365,79526],"passed":false},{"actual":[365,227281],"check":"partial-repair probe 3","expected":[366,226660],"passed":false},{"actual":[365,33178],"check":"normal control 4","expected":[365,33178],"passed":true},{"actual":[365,46318],"check":"normal control 5","expected":[365,46318],"passed":true},{"actual":[365,20896],"check":"normal control 6","expected":[365,20896],"passed":true},{"actual":[365,12700],"check":"normal control 7","expected":[365,12700],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": [365, 3810], \"expected\": [366, 3800], \"passed\": false}, {\"check\": \"regression 1\", \"actual\": [366, 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\"actual\": [365, 227281], \"expected\": [366, 226660], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [365, 33178], \"expected\": [365, 33178], \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": [365, 46318], \"expected\": [365, 46318], \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": [365, 20896], \"expected\": [365, 20896], \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": [365, 12700], \"expected\": [365, 12700], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":43.832,"exit_code":0,"observations":[{"actual":[366,3800],"check":"regression 0","expected":[366,3800],"passed":true},{"actual":[366,2900],"check":"regression 1","expected":[366,2900],"passed":true},{"actual":[365,79526],"check":"partial-repair probe 2","expected":[365,79526],"passed":true},{"actual":[366,226660],"check":"partial-repair probe 3","expected":[366,226660],"passed":true},{"actual":[365,33178],"check":"normal control 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