{"abstract":"Annual periods beginning on a leap day and periods ending on one are classified the wrong way round.","category":"Bond day-count conventions","checks":8,"contract":"Inputs start, end ([y,m,d]) and coupon frequency. Days are actual days. For annual frequency the denominator is 366 if any 29 February lies in (start, end], else 365. For other frequencies the denominator is 366 if the end date year is a leap year, else 365. Return days/denominator rounded to 9 decimals.","evaluation_group":"w2-bond_day_count_conventions-act-365-leap","failed_approach":"Closing both ends still counts a 29 February start date.","family":"w2-bond_day_count_conventions-act-365-leap-february-29-interval-ends","id":"FA-60936","implementations":{"attempt":{"sha256":"e1165ed204b96415d72ae2824ac08cd20422c3f11b7a5197ac9fe530d6f21b0d","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(a, b, freq):\n    A = datetime.date(*a)\n    B = datetime.date(*b)\n    def leap(y):\n        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0\n    days = (B - A).days\n    if freq == 1:\n        has29 = any(leap(y) and A <= datetime.date(y, 2, 29) <= B for y in range(A.year, B.year + 1))\n        den = 366 if has29 else 365\n    else:\n        den = 366 if leap(B.year) else 365\n    return round(days / den, 9)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression february 29 interval ends 1', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['regression february 29 interval ends 2', [[2028, 2, 29], [2029, 2, 28], 1], 1.0], ['partial repair probe 1', [[2104, 2, 29], [2106, 2, 28], 1], 2.0], ['partial repair probe 2', [[1996, 2, 29], [1997, 6, 27], 1], 1.326027397], ['boundary control 1', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['boundary control 2', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['normal control 1', [[2071, 8, 3], [2072, 12, 10], 1], 1.352459016], ['normal control 2', [[2027, 12, 29], [2030, 12, 29], 1], 2.994535519]], [['regression february 29 interval ends 1', [[2096, 2, 29], [2099, 2, 28], 1], 3.0], ['regression february 29 interval ends 2', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 1', [[2000, 2, 29], [2001, 6, 25], 1], 1.320547945], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2028, 12, 15], [2029, 3, 15], 4], 0.246575342], ['normal control 2', [[2077, 12, 6], [2078, 9, 23], 1], 0.797260274], ['normal control 3', [[2025, 4, 30], [2026, 4, 30], 1], 1.0]], [['regression february 29 interval ends 1', [[2096, 2, 29], [2097, 2, 28], 1], 1.0], ['regression february 29 interval ends 2', [[2096, 2, 29], [2096, 6, 30], 1], 0.334246575], ['partial repair probe 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 2', [[2104, 2, 29], [2105, 2, 28], 1], 1.0], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[1935, 3, 4], [1936, 3, 12], 4], 1.021857923], ['normal control 2', [[2024, 3, 29], [2024, 9, 29], 4], 0.50273224]], [['regression february 29 interval ends 1', [[2024, 2, 29], [2027, 2, 28], 1], 3.0], ['regression february 29 interval ends 2', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 1', [[1996, 2, 29], [1996, 4, 30], 1], 0.167123288], ['partial repair probe 2', [[2024, 2, 29], [2025, 4, 23], 1], 1.147945205], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[2103, 12, 29], [2104, 3, 29], 4], 0.24863388], ['normal control 2', [[2076, 11, 19], [2077, 11, 19], 1], 1.0]], [['regression february 29 interval ends 1', [[2048, 2, 29], [2049, 2, 28], 1], 1.0], ['regression february 29 interval ends 2', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['partial repair probe 1', [[1980, 2, 29], [1981, 2, 28], 1], 1.0], ['partial repair probe 2', [[2028, 2, 29], [2030, 2, 28], 1], 2.0], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[1896, 1, 30], [1896, 4, 30], 4], 0.24863388], ['normal control 2', [[2015, 3, 17], [2016, 3, 17], 1], 1.0]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, 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":"b947b526f2540242fc9ef05c64872810ab5a1383d65215153677ac397b008be4","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(a, b, freq):\n    A = datetime.date(*a)\n    B = datetime.date(*b)\n    def leap(y):\n        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0\n    days = (B - A).days\n    if freq == 1:\n        has29 = any(leap(y) and A <= datetime.date(y, 2, 29) < B for y in range(A.year, B.year + 1))\n        den = 366 if has29 else 365\n    else:\n        den = 366 if leap(B.year) else 365\n    return round(days / den, 9)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression february 29 interval ends 1', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['regression february 29 interval ends 2', [[2028, 2, 29], [2029, 2, 28], 1], 1.0], ['partial repair probe 1', [[2104, 2, 29], [2106, 2, 28], 1], 2.0], ['partial repair probe 2', [[1996, 2, 29], [1997, 6, 27], 1], 1.326027397], ['boundary control 1', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['boundary control 2', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['normal control 1', [[2071, 8, 3], [2072, 12, 10], 1], 1.352459016], ['normal control 2', [[2027, 12, 29], [2030, 12, 29], 1], 2.994535519]], [['regression february 29 interval ends 1', [[2096, 2, 29], [2099, 2, 28], 1], 3.0], ['regression february 29 interval ends 2', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 1', [[2000, 2, 29], [2001, 6, 25], 1], 1.320547945], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2028, 12, 15], [2029, 3, 15], 4], 0.246575342], ['normal control 2', [[2077, 12, 6], [2078, 9, 23], 1], 0.797260274], ['normal control 3', [[2025, 4, 30], [2026, 4, 30], 1], 1.0]], [['regression february 29 interval ends 1', [[2096, 2, 29], [2097, 2, 28], 1], 1.0], ['regression february 29 interval ends 2', [[2096, 2, 29], [2096, 6, 30], 1], 0.334246575], ['partial repair probe 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 2', [[2104, 2, 29], [2105, 2, 28], 1], 1.0], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[1935, 3, 4], [1936, 3, 12], 4], 1.021857923], ['normal control 2', [[2024, 3, 29], [2024, 9, 29], 4], 0.50273224]], [['regression february 29 interval ends 1', [[2024, 2, 29], [2027, 2, 28], 1], 3.0], ['regression february 29 interval ends 2', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 1', [[1996, 2, 29], [1996, 4, 30], 1], 0.167123288], ['partial repair probe 2', [[2024, 2, 29], [2025, 4, 23], 1], 1.147945205], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[2103, 12, 29], [2104, 3, 29], 4], 0.24863388], ['normal control 2', [[2076, 11, 19], [2077, 11, 19], 1], 1.0]], [['regression february 29 interval ends 1', [[2048, 2, 29], [2049, 2, 28], 1], 1.0], ['regression february 29 interval ends 2', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['partial repair probe 1', [[1980, 2, 29], [1981, 2, 28], 1], 1.0], ['partial repair probe 2', [[2028, 2, 29], [2030, 2, 28], 1], 2.0], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[1896, 1, 30], [1896, 4, 30], 4], 0.24863388], ['normal control 2', [[2015, 3, 17], [2016, 3, 17], 1], 1.0]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, 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":"bdae5fd37ea6c661b981e68b3bbc0f317e3aaa4d6c8afe5bb7b443322ca44645","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(a, b, freq):\n    A = datetime.date(*a)\n    B = datetime.date(*b)\n    def leap(y):\n        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0\n    days = (B - A).days\n    if freq == 1:\n        has29 = any(leap(y) and A < datetime.date(y, 2, 29) <= B for y in range(A.year, B.year + 1))\n        den = 366 if has29 else 365\n    else:\n        den = 366 if leap(B.year) else 365\n    return round(days / den, 9)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression february 29 interval ends 1', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['regression february 29 interval ends 2', [[2028, 2, 29], [2029, 2, 28], 1], 1.0], ['partial repair probe 1', [[2104, 2, 29], [2106, 2, 28], 1], 2.0], ['partial repair probe 2', [[1996, 2, 29], [1997, 6, 27], 1], 1.326027397], ['boundary control 1', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['boundary control 2', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['normal control 1', [[2071, 8, 3], [2072, 12, 10], 1], 1.352459016], ['normal control 2', [[2027, 12, 29], [2030, 12, 29], 1], 2.994535519]], [['regression february 29 interval ends 1', [[2096, 2, 29], [2099, 2, 28], 1], 3.0], ['regression february 29 interval ends 2', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 1', [[2000, 2, 29], [2001, 6, 25], 1], 1.320547945], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2028, 12, 15], [2029, 3, 15], 4], 0.246575342], ['normal control 2', [[2077, 12, 6], [2078, 9, 23], 1], 0.797260274], ['normal control 3', [[2025, 4, 30], [2026, 4, 30], 1], 1.0]], [['regression february 29 interval ends 1', [[2096, 2, 29], [2097, 2, 28], 1], 1.0], ['regression february 29 interval ends 2', [[2096, 2, 29], [2096, 6, 30], 1], 0.334246575], ['partial repair probe 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 2', [[2104, 2, 29], [2105, 2, 28], 1], 1.0], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[1935, 3, 4], [1936, 3, 12], 4], 1.021857923], ['normal control 2', [[2024, 3, 29], [2024, 9, 29], 4], 0.50273224]], [['regression february 29 interval ends 1', [[2024, 2, 29], [2027, 2, 28], 1], 3.0], ['regression february 29 interval ends 2', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['partial repair probe 1', [[1996, 2, 29], [1996, 4, 30], 1], 0.167123288], ['partial repair probe 2', [[2024, 2, 29], [2025, 4, 23], 1], 1.147945205], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[2103, 12, 29], [2104, 3, 29], 4], 0.24863388], ['normal control 2', [[2076, 11, 19], [2077, 11, 19], 1], 1.0]], [['regression february 29 interval ends 1', [[2048, 2, 29], [2049, 2, 28], 1], 1.0], ['regression february 29 interval ends 2', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['partial repair probe 1', [[1980, 2, 29], [1981, 2, 28], 1], 1.0], ['partial repair probe 2', [[2028, 2, 29], [2030, 2, 28], 1], 2.0], ['boundary control 1', [[2023, 3, 1], [2024, 3, 1], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[1896, 1, 30], [1896, 4, 30], 4], 0.24863388], ['normal control 2', [[2015, 3, 17], [2016, 3, 17], 1], 1.0]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, 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 toy contract stated explicitly in the contract field; no claim of conformance to any published convention text. 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-bond_day_count_conventions-act-365-leap-february-29-interval-ends","generated_at":"2026-09-29T14:46:50.292701+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.","repair":"Count 29 February only when start < 29 Feb <= end.","root_cause":"The leap-day membership test uses [start, end) instead of (start, end].","sha256":"de254554e39c018b705e59a20ed67cad1850e8a3072bd0b5715bf21d8ef72fc2","title":"Act/365L denominator selection: a period starting on 29 February uses a 366-day year · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":45.58,"exit_code":1,"observations":[{"actual":1.0,"check":"regression february 29 interval ends 1","expected":1.0,"passed":true},{"actual":0.99726776,"check":"regression february 29 interval ends 2","expected":1.0,"passed":false},{"actual":1.994535519,"check":"partial repair probe 1","expected":2.0,"passed":false},{"actual":1.322404372,"check":"partial repair probe 2","expected":1.326027397,"passed":false},{"actual":0.252054795,"check":"boundary control 1","expected":0.252054795,"passed":true},{"actual":1.0,"check":"boundary control 2","expected":1.0,"passed":true},{"actual":1.352459016,"check":"normal control 1","expected":1.352459016,"passed":true},{"actual":2.994535519,"check":"normal control 2","expected":2.994535519,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression february 29 interval ends 1\", \"actual\": 1.0, \"expected\": 1.0, \"passed\": true}, {\"check\": \"regression february 29 interval ends 2\", \"actual\": 0.99726776, \"expected\": 1.0, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 1.994535519, \"expected\": 2.0, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 1.322404372, \"expected\": 1.326027397, \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": 0.252054795, \"expected\": 0.252054795, \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": 1.0, \"expected\": 1.0, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 1.352459016, \"expected\": 1.352459016, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 2.994535519, \"expected\": 2.994535519, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":44.238,"exit_code":1,"observations":[{"actual":1.002739726,"check":"regression february 29 interval ends 1","expected":1.0,"passed":false},{"actual":0.99726776,"check":"regression february 29 interval ends 2","expected":1.0,"passed":false},{"actual":1.994535519,"check":"partial repair probe 1","expected":2.0,"passed":false},{"actual":1.322404372,"check":"partial repair probe 2","expected":1.326027397,"passed":false},{"actual":0.252054795,"check":"boundary control 1","expected":0.252054795,"passed":true},{"actual":1.0,"check":"boundary control 2","expected":1.0,"passed":true},{"actual":1.352459016,"check":"normal control 1","expected":1.352459016,"passed":true},{"actual":2.994535519,"check":"normal control 2","expected":2.994535519,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression february 29 interval ends 1\", \"actual\": 1.002739726, \"expected\": 1.0, \"passed\": false}, {\"check\": \"regression february 29 interval ends 2\", \"actual\": 0.99726776, \"expected\": 1.0, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 1.994535519, \"expected\": 2.0, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 1.322404372, \"expected\": 1.326027397, \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": 0.252054795, \"expected\": 0.252054795, \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": 1.0, \"expected\": 1.0, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 1.352459016, \"expected\": 1.352459016, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 2.994535519, \"expected\": 2.994535519, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":43.583,"exit_code":0,"observations":[{"actual":1.0,"check":"regression february 29 interval ends 1","expected":1.0,"passed":true},{"actual":1.0,"check":"regression february 29 interval ends 2","expected":1.0,"passed":true},{"actual":2.0,"check":"partial repair probe 1","expected":2.0,"passed":true},{"actual":1.326027397,"check":"partial repair probe 2","expected":1.326027397,"passed":true},{"actual":0.252054795,"check":"boundary control 1","expected":0.252054795,"passed":true},{"actual":1.0,"check":"boundary control 2","expected":1.0,"passed":true},{"actual":1.352459016,"check":"normal control 1","expected":1.352459016,"passed":true},{"actual":2.994535519,"check":"normal control 2","expected":2.994535519,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression february 29 interval ends 1\", \"actual\": 1.0, \"expected\": 1.0, \"passed\": true}, {\"check\": \"regression february 29 interval ends 2\", \"actual\": 1.0, \"expected\": 1.0, \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": 2.0, \"expected\": 2.0, \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": 1.326027397, \"expected\": 1.326027397, \"passed\": true}, {\"check\": \"boundary control 1\", \"actual\": 0.252054795, \"expected\": 0.252054795, \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": 1.0, \"expected\": 1.0, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 1.352459016, \"expected\": 1.352459016, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 2.994535519, \"expected\": 2.994535519, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}