{"abstract":"Customers joining late in the period get the full monthly allowance.","category":"Subscription proration billing","checks":8,"contract":"Input {usage, tiers: [[up_to|None, unit_price, flat_fee]], mode volume|graduated, included, active_days, period_days}. Allowance = included*active_days//period_days. Billable = max(0, usage - allowance); zero billable costs 0. Volume: all billable units at the first tier with billable <= up_to, plus that tier's flat fee. Graduated: each tier bills the units falling in its range plus its flat fee if any unit falls in it. Return [allowance, cost].","contract_signature":"x","evaluation_group":"w2-subscription-proration-usage-allowance-tiers","failed_approach":"The attempt prorates but rounds the allowance up instead of down.","family":"w2-subscription-proration-usage-allowance-tiers-allowance-proration","id":"FA-59496","implementations":{"attempt":{"sha256":"c14a662554dcb9c0d3fa46b89519485ab5602558fa07c5f024580d632e550425","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    allow = -(-x['included'] * x['active_days'] // x['period_days'])\n    units = max(0, x['usage'] - allow)\n    if units == 0:\n        return [allow, 0]\n    if x['mode'] == 'volume':\n        for up, price, flat in x['tiers']:\n            if up is None or units <= up:\n                return [allow, units * price + flat]\n    cost = 0\n    prev = 0\n    for up, price, flat in x['tiers']:\n        top = units if up is None else min(units, up)\n        if top > prev:\n            cost += (top - prev) * price + flat\n        if up is None or units <= up:\n            break\n        prev = up\n    return [allow, cost]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('regression', {'usage': 215, 'tiers': [[215, 32, 0], [1030, 38, 300], [None, 21, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('partial-repair probe', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('normal control', {'usage': 195, 'tiers': [[263, 41, 0], [444, 16, 300], [None, 18, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 31}, [0, 7995]), ('normal control', {'usage': 149, 'tiers': [[96, 6, 0], [296, 34, 0], [None, 9, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 27, 'period_days': 30}, [0, 2378]), ('normal control', {'usage': 132, 'tiers': [[102, 43, 500], [1090, 39, 300], [None, 1, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 31, 'period_days': 31}, [100, 1876]), ('normal control', {'usage': 1908, 'tiers': [[438, 11, 500], [1328, 8, 0], [None, 10, 0]], 'mode': 'volume', 'included': 0, 'active_days': 3, 'period_days': 31}, [0, 19080])], [('regression', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('regression', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('partial-repair probe', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('partial-repair probe', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('normal control', {'usage': 2285, 'tiers': [[395, 26, 500], [1142, 40, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 13, 'period_days': 30}, [0, 68082]), ('normal control', {'usage': 340, 'tiers': [[481, 48, 0], [1132, 35, 300], [None, 6, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 0]), ('normal control', {'usage': 392, 'tiers': [[392, 7, 0], [719, 26, 0], [None, 8, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 22, 'period_days': 30}, [0, 2744]), ('normal control', {'usage': 422, 'tiers': [[422, 16, 0], [1350, 20, 0], [None, 20, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 31, 'period_days': 31}, [1000, 0])], [('regression', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('regression', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160]), ('partial-repair probe', {'usage': 1862, 'tiers': [[90, 6, 0], [1073, 39, 0], [None, 20, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 29, 'period_days': 31}, [93, 52797]), ('normal control', {'usage': 107, 'tiers': [[490, 47, 0], [1088, 17, 0], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 0]), ('normal control', {'usage': 106, 'tiers': [[301, 42, 500], [930, 23, 300], [None, 29, 0]], 'mode': 'volume', 'included': 0, 'active_days': 24, 'period_days': 30}, [0, 4952]), ('normal control', {'usage': 143, 'tiers': [[493, 29, 0], [634, 36, 0], [None, 23, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 2, 'period_days': 31}, [0, 4147]), ('normal control', {'usage': 1774, 'tiers': [[155, 27, 0], [290, 6, 0], [None, 14, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 18, 'period_days': 31}, [0, 25771])], [('regression', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('regression', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('partial-repair probe', {'usage': 129, 'tiers': [[129, 9, 500], [845, 8, 300], [None, 18, 0]], 'mode': 'volume', 'included': 250, 'active_days': 29, 'period_days': 30}, [241, 0]), ('partial-repair probe', {'usage': 205, 'tiers': [[205, 36, 0], [1123, 17, 300], [None, 4, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 16, 'period_days': 31}, [516, 0]), ('normal control', {'usage': 310, 'tiers': [[237, 10, 0], [892, 12, 0], [None, 15, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 14, 'period_days': 31}, [0, 3246]), ('normal control', {'usage': 66, 'tiers': [[349, 9, 0], [1199, 11, 0], [None, 7, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 30}, [0, 594]), ('normal control', {'usage': 344, 'tiers': [[344, 8, 0], [602, 31, 0], [None, 12, 0]], 'mode': 'volume', 'included': 0, 'active_days': 5, 'period_days': 30}, [0, 2752]), ('normal control', {'usage': 270, 'tiers': [[270, 9, 500], [388, 14, 0], [None, 27, 0]], 'mode': 'volume', 'included': 100, 'active_days': 30, 'period_days': 30}, [100, 2030])], [('regression', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160]), ('regression', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('partial-repair probe', {'usage': 286, 'tiers': [[286, 31, 500], [1169, 13, 0], [None, 22, 0]], 'mode': 'volume', 'included': 250, 'active_days': 30, 'period_days': 31}, [241, 1895]), ('partial-repair probe', {'usage': 22, 'tiers': [[271, 40, 0], [731, 39, 0], [None, 19, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 23, 'period_days': 30}, [191, 0]), ('normal control', {'usage': 255, 'tiers': [[255, 8, 0], [492, 6, 0], [None, 26, 0]], 'mode': 'volume', 'included': 0, 'active_days': 17, 'period_days': 30}, [0, 2040]), ('normal control', {'usage': 187, 'tiers': [[153, 21, 500], [679, 13, 300], [None, 25, 0]], 'mode': 'volume', 'included': 0, 'active_days': 27, 'period_days': 31}, [0, 2731]), ('normal control', {'usage': 197, 'tiers': [[288, 8, 0], [714, 8, 0], [None, 2, 0]], 'mode': 'volume', 'included': 0, 'active_days': 24, 'period_days': 31}, [0, 1576]), ('normal control', {'usage': 317, 'tiers': [[317, 39, 500], [991, 40, 0], [None, 22, 0]], 'mode': 'volume', 'included': 0, 'active_days': 10, 'period_days': 31}, [0, 12863])]]\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":"b719eaf58d858e13a2abe89b30ec706403bd1ff36894e802cd37c7a8d26e0098","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    allow = x['included']\n    units = max(0, x['usage'] - allow)\n    if units == 0:\n        return [allow, 0]\n    if x['mode'] == 'volume':\n        for up, price, flat in x['tiers']:\n            if up is None or units <= up:\n                return [allow, units * price + flat]\n    cost = 0\n    prev = 0\n    for up, price, flat in x['tiers']:\n        top = units if up is None else min(units, up)\n        if top > prev:\n            cost += (top - prev) * price + flat\n        if up is None or units <= up:\n            break\n        prev = up\n    return [allow, cost]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('regression', {'usage': 215, 'tiers': [[215, 32, 0], [1030, 38, 300], [None, 21, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 27, 'period_days': 30}, [900, 0]), ('partial-repair probe', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('normal control', {'usage': 195, 'tiers': [[263, 41, 0], [444, 16, 300], [None, 18, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 31}, [0, 7995]), ('normal control', {'usage': 149, 'tiers': [[96, 6, 0], [296, 34, 0], [None, 9, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 27, 'period_days': 30}, [0, 2378]), ('normal control', {'usage': 132, 'tiers': [[102, 43, 500], [1090, 39, 300], [None, 1, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 31, 'period_days': 31}, [100, 1876]), ('normal control', {'usage': 1908, 'tiers': [[438, 11, 500], [1328, 8, 0], [None, 10, 0]], 'mode': 'volume', 'included': 0, 'active_days': 3, 'period_days': 31}, [0, 19080])], [('regression', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('regression', {'usage': 2690, 'tiers': [[446, 10, 0], [1155, 29, 300], [None, 24, 0]], 'mode': 'volume', 'included': 250, 'active_days': 1, 'period_days': 31}, [8, 64368]), ('partial-repair probe', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('partial-repair probe', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('normal control', {'usage': 2285, 'tiers': [[395, 26, 500], [1142, 40, 0], [None, 24, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 13, 'period_days': 30}, [0, 68082]), ('normal control', {'usage': 340, 'tiers': [[481, 48, 0], [1132, 35, 300], [None, 6, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 0]), ('normal control', {'usage': 392, 'tiers': [[392, 7, 0], [719, 26, 0], [None, 8, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 22, 'period_days': 30}, [0, 2744]), ('normal control', {'usage': 422, 'tiers': [[422, 16, 0], [1350, 20, 0], [None, 20, 0]], 'mode': 'volume', 'included': 1000, 'active_days': 31, 'period_days': 31}, [1000, 0])], [('regression', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('regression', {'usage': 520, 'tiers': [[335, 27, 0], [725, 26, 0], [None, 26, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 30}, [733, 0]), ('partial-repair probe', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160]), ('partial-repair probe', {'usage': 1862, 'tiers': [[90, 6, 0], [1073, 39, 0], [None, 20, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 29, 'period_days': 31}, [93, 52797]), ('normal control', {'usage': 107, 'tiers': [[490, 47, 0], [1088, 17, 0], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 30, 'period_days': 30}, [1000, 0]), ('normal control', {'usage': 106, 'tiers': [[301, 42, 500], [930, 23, 300], [None, 29, 0]], 'mode': 'volume', 'included': 0, 'active_days': 24, 'period_days': 30}, [0, 4952]), ('normal control', {'usage': 143, 'tiers': [[493, 29, 0], [634, 36, 0], [None, 23, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 2, 'period_days': 31}, [0, 4147]), ('normal control', {'usage': 1774, 'tiers': [[155, 27, 0], [290, 6, 0], [None, 14, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 18, 'period_days': 31}, [0, 25771])], [('regression', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('regression', {'usage': 152, 'tiers': [[116, 12, 0], [540, 33, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 22, 'period_days': 31}, [709, 0]), ('partial-repair probe', {'usage': 129, 'tiers': [[129, 9, 500], [845, 8, 300], [None, 18, 0]], 'mode': 'volume', 'included': 250, 'active_days': 29, 'period_days': 30}, [241, 0]), ('partial-repair probe', {'usage': 205, 'tiers': [[205, 36, 0], [1123, 17, 300], [None, 4, 0]], 'mode': 'graduated', 'included': 1000, 'active_days': 16, 'period_days': 31}, [516, 0]), ('normal control', {'usage': 310, 'tiers': [[237, 10, 0], [892, 12, 0], [None, 15, 0]], 'mode': 'graduated', 'included': 0, 'active_days': 14, 'period_days': 31}, [0, 3246]), ('normal control', {'usage': 66, 'tiers': [[349, 9, 0], [1199, 11, 0], [None, 7, 0]], 'mode': 'volume', 'included': 0, 'active_days': 28, 'period_days': 30}, [0, 594]), ('normal control', {'usage': 344, 'tiers': [[344, 8, 0], [602, 31, 0], [None, 12, 0]], 'mode': 'volume', 'included': 0, 'active_days': 5, 'period_days': 30}, [0, 2752]), ('normal control', {'usage': 270, 'tiers': [[270, 9, 500], [388, 14, 0], [None, 27, 0]], 'mode': 'volume', 'included': 100, 'active_days': 30, 'period_days': 30}, [100, 2030])], [('regression', {'usage': 335, 'tiers': [[335, 30, 500], [789, 38, 300], [None, 29, 0]], 'mode': 'graduated', 'included': 100, 'active_days': 4, 'period_days': 30}, [13, 10160]), ('regression', {'usage': 158, 'tiers': [[158, 6, 0], [662, 22, 0], [None, 15, 0]], 'mode': 'volume', 'included': 100, 'active_days': 10, 'period_days': 30}, [33, 750]), ('partial-repair probe', {'usage': 286, 'tiers': [[286, 31, 500], [1169, 13, 0], [None, 22, 0]], 'mode': 'volume', 'included': 250, 'active_days': 30, 'period_days': 31}, [241, 1895]), ('partial-repair probe', {'usage': 22, 'tiers': [[271, 40, 0], [731, 39, 0], [None, 19, 0]], 'mode': 'graduated', 'included': 250, 'active_days': 23, 'period_days': 30}, [191, 0]), ('normal control', {'usage': 255, 'tiers': [[255, 8, 0], [492, 6, 0], [None, 26, 0]], 'mode': 'volume', 'included': 0, 'active_days': 17, 'period_days': 30}, [0, 2040]), ('normal control', {'usage': 187, 'tiers': [[153, 21, 500], [679, 13, 300], [None, 25, 0]], 'mode': 'volume', 'included': 0, 'active_days': 27, 'period_days': 31}, [0, 2731]), ('normal control', {'usage': 197, 'tiers': [[288, 8, 0], [714, 8, 0], [None, 2, 0]], 'mode': 'volume', 'included': 0, 'active_days': 24, 'period_days': 31}, [0, 1576]), ('normal control', {'usage': 317, 'tiers': [[317, 39, 500], [991, 40, 0], [None, 22, 0]], 'mode': 'volume', 'included': 0, 'active_days': 10, 'period_days': 31}, [0, 12863])]]\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-usage-allowance-tiers-allowance-proration","generated_at":"2026-09-29T14:46:36.878451+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A partial first period shrinks the included allowance, and volume versus graduated tiers price the remainder differently.","root_cause":"The included allowance is not prorated to the active days.","sha256":"ead3c71d82a22531fa781ea31f7c4c95ff3855312f46a578752aa2301a0cc5d8","title":"Usage tiers after a prorated included allowance: allowance proration · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":44.216,"exit_code":1,"observations":[{"actual":[9,64344],"check":"regression 0","expected":[8,64368],"passed":false},{"actual":[900,0],"check":"regression 1","expected":[900,0],"passed":true},{"actual":[734,0],"check":"partial-repair probe 2","expected":[733,0],"passed":false},{"actual":[710,0],"check":"partial-repair probe 3","expected":[709,0],"passed":false},{"actual":[0,7995],"check":"normal control 4","expected":[0,7995],"passed":true},{"actual":[0,2378],"check":"normal control 5","expected":[0,2378],"passed":true},{"actual":[100,1876],"check":"normal control 6","expected":[100,1876],"passed":true},{"actual":[0,19080],"check":"normal control 7","expected":[0,19080],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": [9, 64344], \"expected\": [8, 64368], \"passed\": false}, {\"check\": \"regression 1\", \"actual\": [900, 0], \"expected\": [900, 0], \"passed\": true}, {\"check\": \"partial-repair probe 2\", \"actual\": [734, 0], \"expected\": [733, 0], \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": [710, 0], \"expected\": [709, 0], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [0, 7995], \"expected\": [0, 7995], \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": [0, 2378], \"expected\": [0, 2378], \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": [100, 1876], \"expected\": [100, 1876], \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": [0, 19080], \"expected\": [0, 19080], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":42.103,"exit_code":1,"observations":[{"actual":[250,58560],"check":"regression 0","expected":[8,64368],"passed":false},{"actual":[1000,0],"check":"regression 1","expected":[900,0],"passed":false},{"actual":[1000,0],"check":"partial-repair probe 2","expected":[733,0],"passed":false},{"actual":[1000,0],"check":"partial-repair probe 3","expected":[709,0],"passed":false},{"actual":[0,7995],"check":"normal control 4","expected":[0,7995],"passed":true},{"actual":[0,2378],"check":"normal control 5","expected":[0,2378],"passed":true},{"actual":[100,1876],"check":"normal control 6","expected":[100,1876],"passed":true},{"actual":[0,19080],"check":"normal control 7","expected":[0,19080],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": [250, 58560], \"expected\": [8, 64368], \"passed\": false}, {\"check\": \"regression 1\", \"actual\": [1000, 0], \"expected\": [900, 0], \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": [1000, 0], \"expected\": [733, 0], \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": [1000, 0], \"expected\": [709, 0], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [0, 7995], \"expected\": [0, 7995], \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": [0, 2378], \"expected\": [0, 2378], \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": [100, 1876], \"expected\": [100, 1876], \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": [0, 19080], \"expected\": [0, 19080], \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}