{"abstract":"Waiting in traffic costs sixty times the tariff.","category":"Ride-hailing fare and surge pricing","checks":7,"contract":"A single-tariff taximeter bills each segment [seconds, meters] by distance when its speed is at least v m/s (meters >= v*seconds, so a zero-duration movement bills distance) and by time otherwise, never both. Charges accumulate exactly (per_km per 1000 m, per_hour per 3600 s) and the total is rounded half up to a cent once, then the flag fall is added.","evaluation_group":"w2-ride-hailing-fare-surge-taximeter-single-tariff","failed_approach":"Flooring each time segment to whole cents loses fractional waiting charges.","family":"w2-ride-hailing-fare-surge-taximeter-single-tariff-hourly-rate-unit","id":"FA-85786","implementations":{"attempt":{"sha256":"84cdb236bdf32fbb89a197e14513c75a1bb4d3c08fc21673656f5ba120d4b89b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(segments, rate):\n    acc = Fraction(0)\n    for dt, dm in segments:\n        if dm >= rate['v'] * dt:\n            acc += Fraction(dm * rate['per_km'], 1000)\n        else:\n            acc += dt * rate['per_hour'] // 3600\n    cents = int(acc * 2 + 1) // 2\n    return rate['flag'] + cents\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: hourly rate unit',\n   [[[0, 7], [30, 150], [10, 50], [10, 0], [10, 49], [1, 7]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   453),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 7], [5, 7], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 430),\n  ('second regression', [[[5, 24], [10, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 315),\n  ('normal control 1', [[[0, 0], [1, 7]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 302),\n  ('normal control 2', [[[0, 3], [10, 110], [1, 5]], {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],\n   327),\n  ('normal control 3', [[[30, 330]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 381),\n  ('normal control 4', [[[5, 28], [1, 5]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 397)],\n [('regression: hourly rate unit',\n   [[[10, 20], [0, 0], [30, 330], [5, 25], [1, 0], [0, 0]],\n    {'flag': 390, 'per_hour': 3900, 'per_km': 245, 'v': 5}],\n   489),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 149], [1, 11], [5, 25]], {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 342),\n  ('second regression',\n   [[[10, 0], [30, 0], [10, 50], [30, 150], [10, 110], [1, 11]],\n    {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}],\n   510),\n  ('normal control 1', [[[1, 5], [1, 11]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 304),\n  ('normal control 2', [[[5, 25], [30, 330]], {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 387),\n  ('normal control 3', [[[5, 28]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 396),\n  ('normal control 4', [[[0, 0]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 300)],\n [('regression: hourly rate unit',\n   [[[10, 0], [1, 11], [0, 0], [0, 7], [0, 0], [10, 7]],\n    {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   324),\n  ('partial repair probe: hourly rate unit',\n   [[[0, 0], [30, 330], [5, 10], [30, 330]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 527),\n  ('second regression', [[[0, 0], [10, 20]], {'flag': 300, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 310),\n  ('normal control 1', [[[1, 7], [1, 8], [0, 3]], {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}],\n   304),\n  ('normal control 2', [[[1, 5]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 391),\n  ('normal control 3', [[[0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 390),\n  ('normal control 4', [[[30, 150]], {'flag': 300, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 335)],\n [('regression: hourly rate unit',\n   [[[30, 153], [30, 330], [30, 149], [1, 7], [30, 149]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   548),\n  ('partial repair probe: hourly rate unit',\n   [[[0, 0], [10, 0], [10, 50], [10, 7], [10, 110], [1, 8]],\n    {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}],\n   363),\n  ('second regression',\n   [[[5, 0], [10, 110], [0, 0], [10, 53], [0, 0], [10, 7]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   437),\n  ('normal control 1', [[[0, 0], [10, 53]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 313),\n  ('normal control 2', [[[30, 150]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 337),\n  ('normal control 3',\n   [[[0, 0], [10, 110], [0, 0], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 412),\n  ('normal control 4', [[[10, 53], [1, 5]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 403)],\n [('regression: hourly rate unit',\n   [[[0, 0], [10, 0], [1, 7], [1, 2]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 312),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 153], [5, 10], [30, 149], [1, 8]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 460),\n  ('second regression',\n   [[[1, 11], [1, 5], [1, 7], [10, 0], [30, 150]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],\n   444),\n  ('normal control 1',\n   [[[1, 7], [30, 150], [10, 53], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 441),\n  ('normal control 2', [[[0, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 300),\n  ('normal control 3', [[[10, 50]], {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 312),\n  ('normal control 4', [[[0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 390)]]\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":"311ff52e570b0dcfabf9876a0386906c3f29d044c3ec7a5c956be211ac340eaa","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(segments, rate):\n    acc = Fraction(0)\n    for dt, dm in segments:\n        if dm >= rate['v'] * dt:\n            acc += Fraction(dm * rate['per_km'], 1000)\n        else:\n            acc += Fraction(dt * rate['per_hour'], 60)\n    cents = int(acc * 2 + 1) // 2\n    return rate['flag'] + cents\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: hourly rate unit',\n   [[[0, 7], [30, 150], [10, 50], [10, 0], [10, 49], [1, 7]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   453),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 7], [5, 7], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 430),\n  ('second regression', [[[5, 24], [10, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 315),\n  ('normal control 1', [[[0, 0], [1, 7]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 302),\n  ('normal control 2', [[[0, 3], [10, 110], [1, 5]], {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],\n   327),\n  ('normal control 3', [[[30, 330]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 381),\n  ('normal control 4', [[[5, 28], [1, 5]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 397)],\n [('regression: hourly rate unit',\n   [[[10, 20], [0, 0], [30, 330], [5, 25], [1, 0], [0, 0]],\n    {'flag': 390, 'per_hour': 3900, 'per_km': 245, 'v': 5}],\n   489),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 149], [1, 11], [5, 25]], {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 342),\n  ('second regression',\n   [[[10, 0], [30, 0], [10, 50], [30, 150], [10, 110], [1, 11]],\n    {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}],\n   510),\n  ('normal control 1', [[[1, 5], [1, 11]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 304),\n  ('normal control 2', [[[5, 25], [30, 330]], {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 387),\n  ('normal control 3', [[[5, 28]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 396),\n  ('normal control 4', [[[0, 0]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 300)],\n [('regression: hourly rate unit',\n   [[[10, 0], [1, 11], [0, 0], [0, 7], [0, 0], [10, 7]],\n    {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   324),\n  ('partial repair probe: hourly rate unit',\n   [[[0, 0], [30, 330], [5, 10], [30, 330]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 527),\n  ('second regression', [[[0, 0], [10, 20]], {'flag': 300, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 310),\n  ('normal control 1', [[[1, 7], [1, 8], [0, 3]], {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}],\n   304),\n  ('normal control 2', [[[1, 5]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 391),\n  ('normal control 3', [[[0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 390),\n  ('normal control 4', [[[30, 150]], {'flag': 300, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 335)],\n [('regression: hourly rate unit',\n   [[[30, 153], [30, 330], [30, 149], [1, 7], [30, 149]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   548),\n  ('partial repair probe: hourly rate unit',\n   [[[0, 0], [10, 0], [10, 50], [10, 7], [10, 110], [1, 8]],\n    {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}],\n   363),\n  ('second regression',\n   [[[5, 0], [10, 110], [0, 0], [10, 53], [0, 0], [10, 7]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   437),\n  ('normal control 1', [[[0, 0], [10, 53]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 313),\n  ('normal control 2', [[[30, 150]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 337),\n  ('normal control 3',\n   [[[0, 0], [10, 110], [0, 0], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 412),\n  ('normal control 4', [[[10, 53], [1, 5]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 403)],\n [('regression: hourly rate unit',\n   [[[0, 0], [10, 0], [1, 7], [1, 2]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 312),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 153], [5, 10], [30, 149], [1, 8]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 460),\n  ('second regression',\n   [[[1, 11], [1, 5], [1, 7], [10, 0], [30, 150]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],\n   444),\n  ('normal control 1',\n   [[[1, 7], [30, 150], [10, 53], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 441),\n  ('normal control 2', [[[0, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 300),\n  ('normal control 3', [[[10, 50]], {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 312),\n  ('normal control 4', [[[0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 390)]]\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":"cdaf4ee9a2f53ecc03dbbbb53a1e7ee8983eb12f1c8e152f2db5e1a2bfb37c03","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(segments, rate):\n    acc = Fraction(0)\n    for dt, dm in segments:\n        if dm >= rate['v'] * dt:\n            acc += Fraction(dm * rate['per_km'], 1000)\n        else:\n            acc += Fraction(dt * rate['per_hour'], 3600)\n    cents = int(acc * 2 + 1) // 2\n    return rate['flag'] + cents\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: hourly rate unit',\n   [[[0, 7], [30, 150], [10, 50], [10, 0], [10, 49], [1, 7]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   453),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 7], [5, 7], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 430),\n  ('second regression', [[[5, 24], [10, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 315),\n  ('normal control 1', [[[0, 0], [1, 7]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 302),\n  ('normal control 2', [[[0, 3], [10, 110], [1, 5]], {'flag': 300, 'per_hour': 3900, 'per_km': 230, 'v': 5}],\n   327),\n  ('normal control 3', [[[30, 330]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 381),\n  ('normal control 4', [[[5, 28], [1, 5]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 397)],\n [('regression: hourly rate unit',\n   [[[10, 20], [0, 0], [30, 330], [5, 25], [1, 0], [0, 0]],\n    {'flag': 390, 'per_hour': 3900, 'per_km': 245, 'v': 5}],\n   489),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 149], [1, 11], [5, 25]], {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 342),\n  ('second regression',\n   [[[10, 0], [30, 0], [10, 50], [30, 150], [10, 110], [1, 11]],\n    {'flag': 390, 'per_hour': 4150, 'per_km': 230, 'v': 5}],\n   510),\n  ('normal control 1', [[[1, 5], [1, 11]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 304),\n  ('normal control 2', [[[5, 25], [30, 330]], {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 387),\n  ('normal control 3', [[[5, 28]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 396),\n  ('normal control 4', [[[0, 0]], {'flag': 300, 'per_hour': 4150, 'per_km': 230, 'v': 5}], 300)],\n [('regression: hourly rate unit',\n   [[[10, 0], [1, 11], [0, 0], [0, 7], [0, 0], [10, 7]],\n    {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   324),\n  ('partial repair probe: hourly rate unit',\n   [[[0, 0], [30, 330], [5, 10], [30, 330]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 527),\n  ('second regression', [[[0, 0], [10, 20]], {'flag': 300, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 310),\n  ('normal control 1', [[[1, 7], [1, 8], [0, 3]], {'flag': 300, 'per_hour': 4150, 'per_km': 199, 'v': 5}],\n   304),\n  ('normal control 2', [[[1, 5]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 391),\n  ('normal control 3', [[[0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 390),\n  ('normal control 4', [[[30, 150]], {'flag': 300, 'per_hour': 3600, 'per_km': 230, 'v': 5}], 335)],\n [('regression: hourly rate unit',\n   [[[30, 153], [30, 330], [30, 149], [1, 7], [30, 149]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   548),\n  ('partial repair probe: hourly rate unit',\n   [[[0, 0], [10, 0], [10, 50], [10, 7], [10, 110], [1, 8]],\n    {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}],\n   363),\n  ('second regression',\n   [[[5, 0], [10, 110], [0, 0], [10, 53], [0, 0], [10, 7]],\n    {'flag': 390, 'per_hour': 3600, 'per_km': 199, 'v': 5}],\n   437),\n  ('normal control 1', [[[0, 0], [10, 53]], {'flag': 300, 'per_hour': 3600, 'per_km': 245, 'v': 5}], 313),\n  ('normal control 2', [[[30, 150]], {'flag': 300, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 337),\n  ('normal control 3',\n   [[[0, 0], [10, 110], [0, 0], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 199, 'v': 5}], 412),\n  ('normal control 4', [[[10, 53], [1, 5]], {'flag': 390, 'per_hour': 3900, 'per_km': 230, 'v': 5}], 403)],\n [('regression: hourly rate unit',\n   [[[0, 0], [10, 0], [1, 7], [1, 2]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 312),\n  ('partial repair probe: hourly rate unit',\n   [[[30, 153], [5, 10], [30, 149], [1, 8]], {'flag': 390, 'per_hour': 3900, 'per_km': 199, 'v': 5}], 460),\n  ('second regression',\n   [[[1, 11], [1, 5], [1, 7], [10, 0], [30, 150]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}],\n   444),\n  ('normal control 1',\n   [[[1, 7], [30, 150], [10, 53], [0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 441),\n  ('normal control 2', [[[0, 0]], {'flag': 300, 'per_hour': 3600, 'per_km': 199, 'v': 5}], 300),\n  ('normal control 3', [[[10, 50]], {'flag': 300, 'per_hour': 3900, 'per_km': 245, 'v': 5}], 312),\n  ('normal control 4', [[[0, 0]], {'flag': 390, 'per_hour': 4150, 'per_km': 245, 'v': 5}], 390)]]\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 pricing contract stipulated for this example; it does not reproduce the pricing of any real ride-hailing operator or regulator. 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-ride-hailing-fare-surge-taximeter-single-tariff-hourly-rate-unit","generated_at":"2026-09-29T14:50:43.563841+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Ride-hailing prices are computed per trip at scale; ordering, unit and boundary slips become systematic over- or under-charging.","repair":"Divide seconds by 3600 for per-hour rates.","root_cause":"Seconds are divided by 60 for an hourly rate.","sha256":"ad5b28cde0bc6954d31dee0c5bd8d3dca75ec59f92d6eedd21de007bddddafd7","title":"Hourly waiting rate applied per minute · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":42.193,"exit_code":1,"observations":[{"actual":453,"check":"regression: hourly rate unit","expected":453,"passed":true},{"actual":429,"check":"partial repair probe: hourly rate unit","expected":430,"passed":false},{"actual":315,"check":"second regression","expected":315,"passed":true},{"actual":302,"check":"normal control 1","expected":302,"passed":true},{"actual":327,"check":"normal control 2","expected":327,"passed":true},{"actual":381,"check":"normal control 3","expected":381,"passed":true},{"actual":397,"check":"normal control 4","expected":397,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: hourly rate unit\", \"actual\": 453, \"expected\": 453, \"passed\": true}, {\"check\": \"partial repair probe: hourly rate unit\", \"actual\": 429, \"expected\": 430, \"passed\": false}, {\"check\": \"second regression\", \"actual\": 315, \"expected\": 315, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 302, \"expected\": 302, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 327, \"expected\": 327, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 381, \"expected\": 381, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 397, \"expected\": 397, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.202,"exit_code":1,"observations":[{"actual":1633,"check":"regression: hourly rate unit","expected":453,"passed":false},{"actual":2811,"check":"partial repair probe: hourly rate unit","expected":430,"passed":false},{"actual":1200,"check":"second regression","expected":315,"passed":false},{"actual":302,"check":"normal control 1","expected":302,"passed":true},{"actual":327,"check":"normal control 2","expected":327,"passed":true},{"actual":381,"check":"normal control 3","expected":381,"passed":true},{"actual":397,"check":"normal control 4","expected":397,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: hourly rate unit\", \"actual\": 1633, \"expected\": 453, \"passed\": false}, {\"check\": \"partial repair probe: hourly rate unit\", \"actual\": 2811, \"expected\": 430, \"passed\": false}, {\"check\": \"second regression\", \"actual\": 1200, \"expected\": 315, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 302, \"expected\": 302, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 327, \"expected\": 327, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 381, \"expected\": 381, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 397, \"expected\": 397, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":43.712,"exit_code":0,"observations":[{"actual":453,"check":"regression: hourly rate unit","expected":453,"passed":true},{"actual":430,"check":"partial repair probe: hourly rate 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