{"abstract":"Periods with a phase change are charged for both phases on the overlapping days.","category":"Subscription proration billing","checks":8,"contract":"Input {ps, pe (exclusive), phases: [[start_day, price per full period]]}. Phases are ordered by start; each lasts until the next phase starts (the last until pe). The period charge is sum(price * overlap days with [ps, pe)) / (pe - ps), rounded half-up once. Return cents.","evaluation_group":"w2-subscription-proration-schedule-phase-invoice","failed_approach":"The attempt ends each phase a day before the next one starts, leaving a gap day uncharged.","family":"w2-subscription-proration-schedule-phase-invoice-phase-end-from-next-start","id":"FA-59701","implementations":{"attempt":{"sha256":"8cd5f0bfe020b38e23c6e855d032f56ea18b80857e42c10a98afec6a4b4e43b0","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    ph = sorted(x['phases'], key=lambda p: p[0])\n    L = x['pe'] - x['ps']\n    total = 0\n    for i, (st, price) in enumerate(ph):\n        en = ph[i + 1][0] - 1 if i + 1 < len(ph) else x['pe']\n        a, b = max(st, x['ps']), min(en, x['pe'])\n        if b > a:\n            total += price * (b - a)\n    return (total * 2 + L) // (2 * L)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('regression', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500)], [('regression', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767)], [('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('partial-repair probe', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0), ('normal control', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 56, 'pe': 86, 'phases': [[60, 0]]}, 0)], [('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('partial-repair probe', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('normal control', {'ps': 86, 'pe': 117, 'phases': [[107, 2000], [119, 2395]]}, 645), ('normal control', {'ps': 51, 'pe': 82, 'phases': [[78, 2000], [64, 0]]}, 258), ('normal control', {'ps': 17, 'pe': 47, 'phases': [[18, 2000]]}, 1933), ('normal control', {'ps': 92, 'pe': 122, 'phases': [[91, 2000]]}, 2000)], [('regression', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 9, 'pe': 39, 'phases': [[1, 2000], [27, 7018], [24, 2000]]}, 4007), ('partial-repair probe', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000), ('normal control', {'ps': 37, 'pe': 67, 'phases': [[69, 1125]]}, 0), ('normal control', {'ps': 29, 'pe': 60, 'phases': [[27, 1965]]}, 1965), ('normal control', {'ps': 56, 'pe': 87, 'phases': [[83, 3500]]}, 452), ('normal control', {'ps': 53, 'pe': 84, 'phases': [[50, 2000]]}, 2000)]]\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":"e527005ae36f80870b69613fc7f75a3e194f6594c07c2bf281c1c7323ce4e8e8","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    ph = sorted(x['phases'], key=lambda p: p[0])\n    L = x['pe'] - x['ps']\n    total = 0\n    for i, (st, price) in enumerate(ph):\n        en = x['pe']\n        a, b = max(st, x['ps']), min(en, x['pe'])\n        if b > a:\n            total += price * (b - a)\n    return (total * 2 + L) // (2 * L)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('regression', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500)], [('regression', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767)], [('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('partial-repair probe', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0), ('normal control', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 56, 'pe': 86, 'phases': [[60, 0]]}, 0)], [('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('partial-repair probe', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('normal control', {'ps': 86, 'pe': 117, 'phases': [[107, 2000], [119, 2395]]}, 645), ('normal control', {'ps': 51, 'pe': 82, 'phases': [[78, 2000], [64, 0]]}, 258), ('normal control', {'ps': 17, 'pe': 47, 'phases': [[18, 2000]]}, 1933), ('normal control', {'ps': 92, 'pe': 122, 'phases': [[91, 2000]]}, 2000)], [('regression', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 9, 'pe': 39, 'phases': [[1, 2000], [27, 7018], [24, 2000]]}, 4007), ('partial-repair probe', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000), ('normal control', {'ps': 37, 'pe': 67, 'phases': [[69, 1125]]}, 0), ('normal control', {'ps': 29, 'pe': 60, 'phases': [[27, 1965]]}, 1965), ('normal control', {'ps': 56, 'pe': 87, 'phases': [[83, 3500]]}, 452), ('normal control', {'ps': 53, 'pe': 84, 'phases': [[50, 2000]]}, 2000)]]\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":"92e3253f6ebfb7f96d057a83a92f07921fca18ea05ceffadc4419e4529f0708e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    ph = sorted(x['phases'], key=lambda p: p[0])\n    L = x['pe'] - x['ps']\n    total = 0\n    for i, (st, price) in enumerate(ph):\n        en = ph[i + 1][0] if i + 1 < len(ph) else x['pe']\n        a, b = max(st, x['ps']), min(en, x['pe'])\n        if b > a:\n            total += price * (b - a)\n    return (total * 2 + L) // (2 * L)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'ps': 30, 'pe': 60, 'phases': [[53, 0], [60, 2000], [12, 3500], [36, 2000]]}, 1833), ('regression', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 95, 'pe': 125, 'phases': [[126, 434]]}, 0), ('normal control', {'ps': 87, 'pe': 117, 'phases': [[84, 0], [119, 2000]]}, 0), ('normal control', {'ps': 100, 'pe': 130, 'phases': [[84, 6736]]}, 6736), ('normal control', {'ps': 83, 'pe': 114, 'phases': [[82, 3500]]}, 3500)], [('regression', {'ps': 15, 'pe': 45, 'phases': [[45, 3500], [27, 1000], [39, 1957]]}, 791), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('normal control', {'ps': 44, 'pe': 74, 'phases': [[71, 1000], [46, 0], [76, 0], [41, 0]]}, 100), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[73, 0]]}, 0), ('normal control', {'ps': 24, 'pe': 54, 'phases': [[22, 1371]]}, 1371), ('normal control', {'ps': 6, 'pe': 36, 'phases': [[13, 1000]]}, 767)], [('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('regression', {'ps': 54, 'pe': 84, 'phases': [[70, 3500], [48, 3500], [40, 5707], [49, 0]]}, 1633), ('partial-repair probe', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('partial-repair probe', {'ps': 47, 'pe': 77, 'phases': [[42, 1000], [68, 2000]]}, 1300), ('normal control', {'ps': 58, 'pe': 88, 'phases': [[65, 3500]]}, 2683), ('normal control', {'ps': 42, 'pe': 72, 'phases': [[23, 0], [58, 0]]}, 0), ('normal control', {'ps': 81, 'pe': 111, 'phases': [[88, 1000]]}, 767), ('normal control', {'ps': 56, 'pe': 86, 'phases': [[60, 0]]}, 0)], [('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('regression', {'ps': 52, 'pe': 82, 'phases': [[60, 0], [44, 1000], [60, 1000], [32, 1000]]}, 1000), ('partial-repair probe', {'ps': 74, 'pe': 104, 'phases': [[63, 2000], [89, 1000]]}, 1500), ('partial-repair probe', {'ps': 86, 'pe': 117, 'phases': [[94, 1000], [82, 3500]]}, 1645), ('normal control', {'ps': 86, 'pe': 117, 'phases': [[107, 2000], [119, 2395]]}, 645), ('normal control', {'ps': 51, 'pe': 82, 'phases': [[78, 2000], [64, 0]]}, 258), ('normal control', {'ps': 17, 'pe': 47, 'phases': [[18, 2000]]}, 1933), ('normal control', {'ps': 92, 'pe': 122, 'phases': [[91, 2000]]}, 2000)], [('regression', {'ps': 21, 'pe': 52, 'phases': [[29, 6784], [10, 1000], [29, 5303], [34, 1000]]}, 1694), ('regression', {'ps': 25, 'pe': 55, 'phases': [[40, 6111], [52, 7893], [43, 3500]]}, 2450), ('partial-repair probe', {'ps': 9, 'pe': 39, 'phases': [[1, 2000], [27, 7018], [24, 2000]]}, 4007), ('partial-repair probe', {'ps': 100, 'pe': 130, 'phases': [[130, 1000], [88, 2000], [96, 2000]]}, 2000), ('normal control', {'ps': 37, 'pe': 67, 'phases': [[69, 1125]]}, 0), ('normal control', {'ps': 29, 'pe': 60, 'phases': [[27, 1965]]}, 1965), ('normal control', {'ps': 56, 'pe': 87, 'phases': [[83, 3500]]}, 452), ('normal control', {'ps': 53, 'pe': 84, 'phases': [[50, 2000]]}, 2000)]]\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-schedule-phase-invoice-phase-end-from-next-start","generated_at":"2026-09-29T14:46:38.823253+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Phase transitions inside a billing period must be charged exactly for their overlapping days.","repair":"Restore the contract rule at the phase end from next start step: use `en = ph[i + 1][0] if i + 1 < len(ph) else x['pe']`.","root_cause":"Every phase is assumed to run to the period end.","sha256":"0d13a7388d5c5b4c14b3165820c0b935fe194a947b0f019da2ae2de7b18d1e94","title":"Subscription schedule phases within a period: phase end from next start · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":39.372,"exit_code":1,"observations":[{"actual":1650,"check":"regression 0","expected":1833,"passed":false},{"actual":693,"check":"regression 1","expected":791,"passed":false},{"actual":967,"check":"partial-repair probe 2","expected":1000,"passed":false},{"actual":2130,"check":"partial-repair probe 3","expected":2450,"passed":false},{"actual":0,"check":"normal control 4","expected":0,"passed":true},{"actual":0,"check":"normal control 5","expected":0,"passed":true},{"actual":6736,"check":"normal control 6","expected":6736,"passed":true},{"actual":3500,"check":"normal control 7","expected":3500,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": 1650, \"expected\": 1833, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": 693, \"expected\": 791, \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": 967, \"expected\": 1000, \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": 2130, \"expected\": 2450, \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": 6736, \"expected\": 6736, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": 3500, \"expected\": 3500, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.368,"exit_code":1,"observations":[{"actual":5100,"check":"regression 0","expected":1833,"passed":false},{"actual":991,"check":"regression 1","expected":791,"passed":false},{"actual":2733,"check":"partial-repair probe 2","expected":1000,"passed":false},{"actual":5245,"check":"partial-repair probe 3","expected":2450,"passed":false},{"actual":0,"check":"normal control 4","expected":0,"passed":true},{"actual":0,"check":"normal control 5","expected":0,"passed":true},{"actual":6736,"check":"normal control 6","expected":6736,"passed":true},{"actual":3500,"check":"normal control 7","expected":3500,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": 5100, \"expected\": 1833, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": 991, \"expected\": 791, \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": 2733, \"expected\": 1000, \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": 5245, \"expected\": 2450, \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": 6736, \"expected\": 6736, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": 3500, \"expected\": 3500, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":45.467,"exit_code":0,"observations":[{"actual":1833,"check":"regression 0","expected":1833,"passed":true},{"actual":791,"check":"regression 1","expected":791,"passed":true},{"actual":1000,"check":"partial-repair probe 2","expected":1000,"passed":true},{"actual":2450,"check":"partial-repair probe 3","expected":2450,"passed":true},{"actual":0,"check":"normal control 4","expected":0,"passed":true},{"actual":0,"check":"normal control 5","expected":0,"passed":true},{"actual":6736,"check":"normal control 6","expected":6736,"passed":true},{"actual":3500,"check":"normal control 7","expected":3500,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": 1833, \"expected\": 1833, \"passed\": true}, {\"check\": \"regression 1\", \"actual\": 791, \"expected\": 791, \"passed\": true}, {\"check\": \"partial-repair probe 2\", \"actual\": 1000, \"expected\": 1000, \"passed\": true}, {\"check\": \"partial-repair probe 3\", \"actual\": 2450, \"expected\": 2450, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": 6736, \"expected\": 6736, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": 3500, \"expected\": 3500, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}