{"abstract":"Time charges come out 60 times too small.","category":"Ride-hailing fare and surge pricing","checks":7,"contract":"All money is integer cents. Distance charge = meters x per_km / 1000 and time charge = seconds x per_min / 60, each rounded half up to a cent on its own. Subtotal = base + distance + time. The minimum fare applies to that subtotal only; the booking fee is added after the minimum. Return the distance charge, time charge and fare.","contract_signature":"trip, rate","evaluation_group":"w2-ride-hailing-fare-surge-metered-fare","failed_approach":"Billing each started minute in full overcharges partial minutes.","family":"w2-ride-hailing-fare-surge-metered-fare-per-minute-proration","id":"FA-85351","implementations":{"attempt":{"sha256":"0778331171eaafa94cd26ed757050685b485fc39adb1db71d4968f3f0694b62e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(trip, rate):\n    def half_up(num, den):\n        return (num * 2 + den) // (den * 2)\n    dist = half_up(trip['meters'] * rate['per_km'], 1000)\n    tm = -(-trip['seconds'] // 60) * rate['per_min']\n    sub = rate['base'] + dist + tm\n    fare = max(sub, rate['minimum']) + rate['booking_fee']\n    return {'distance': dist, 'time': tm, 'fare': fare}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: per-minute proration',\n   [{'meters': 500, 'seconds': 61},\n    {'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 30}],\n   {'distance': 125, 'fare': 500, 'time': 31}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 12, 'seconds': 1229},\n    {'base': 0, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],\n   {'distance': 2, 'fare': 1250, 'time': 307}),\n  ('second regression',\n   [{'meters': 0, 'seconds': 2352},\n    {'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 110, 'per_min': 25}],\n   {'distance': 0, 'fare': 1380, 'time': 980}),\n  ('normal control 1',\n   [{'meters': 0, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 30}],\n   {'distance': 0, 'fare': 750, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 15062, 'seconds': 0},\n    {'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 125, 'per_min': 30}],\n   {'distance': 1883, 'fare': 1883, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 12, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 250, 'per_min': 15}],\n   {'distance': 3, 'fare': 1175, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 500, 'seconds': 0},\n    {'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],\n   {'distance': 63, 'fare': 1175, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 500, 'seconds': 61},\n    {'base': 0, 'booking_fee': 0, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],\n   {'distance': 125, 'fare': 1000, 'time': 31}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 12, 'seconds': 826},\n    {'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 95, 'per_min': 35}],\n   {'distance': 1, 'fare': 1000, 'time': 482}),\n  ('second regression',\n   [{'meters': 1004, 'seconds': 90},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],\n   {'distance': 126, 'fare': 1175, 'time': 38}),\n  ('normal control 1',\n   [{'meters': 500, 'seconds': 0},\n    {'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 250, 'per_min': 30}],\n   {'distance': 125, 'fare': 750, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 18143, 'seconds': 0},\n    {'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],\n   {'distance': 2268, 'fare': 2518, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 12, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 35}],\n   {'distance': 1, 'fare': 1175, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 15}],\n   {'distance': 238, 'fare': 750, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 12, 'seconds': 61},\n    {'base': 250, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],\n   {'distance': 1, 'fare': 675, 'time': 15}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 500, 'seconds': 90},\n    {'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 125, 'per_min': 30}],\n   {'distance': 63, 'fare': 675, 'time': 45}),\n  ('second regression',\n   [{'meters': 0, 'seconds': 30},\n    {'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 25}],\n   {'distance': 0, 'fare': 750, 'time': 13}),\n  ('normal control 1',\n   [{'meters': 0, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],\n   {'distance': 0, 'fare': 675, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 250, 'per_min': 15}],\n   {'distance': 1, 'fare': 1175, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 15}],\n   {'distance': 1, 'fare': 950, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],\n   {'distance': 1, 'fare': 875, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 3247, 'seconds': 2905},\n    {'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 25}],\n   {'distance': 357, 'fare': 1817, 'time': 1210}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 16003, 'seconds': 1892},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 25}],\n   {'distance': 4001, 'fare': 5289, 'time': 788}),\n  ('second regression',\n   [{'meters': 3374, 'seconds': 61},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 25}],\n   {'distance': 422, 'fare': 950, 'time': 25}),\n  ('normal control 1',\n   [{'meters': 500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],\n   {'distance': 63, 'fare': 1175, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 150, 'booking_fee': 175, 'minimum': 700, 'per_km': 125, 'per_min': 25}],\n   {'distance': 1, 'fare': 875, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 1004, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 30}],\n   {'distance': 251, 'fare': 950, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 35}],\n   {'distance': 275, 'fare': 950, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 4, 'seconds': 30},\n    {'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],\n   {'distance': 0, 'fare': 675, 'time': 8}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 1004, 'seconds': 30},\n    {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 30}],\n   {'distance': 95, 'fare': 700, 'time': 15}),\n  ('second regression',\n   [{'meters': 500, 'seconds': 1245},\n    {'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],\n   {'distance': 55, 'fare': 1175, 'time': 623}),\n  ('normal control 1',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],\n   {'distance': 625, 'fare': 1050, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 20828, 'seconds': 0},\n    {'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 25}],\n   {'distance': 2604, 'fare': 2854, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 0, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 25}],\n   {'distance': 0, 'fare': 875, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 15}],\n   {'distance': 625, 'fare': 1250, 'time': 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":"476201a308cb47c32101521f4ff6c808b3ea418bad060b0a0816de9af05ccea1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(trip, rate):\n    def half_up(num, den):\n        return (num * 2 + den) // (den * 2)\n    dist = half_up(trip['meters'] * rate['per_km'], 1000)\n    tm = half_up(trip['seconds'] * rate['per_min'], 3600)\n    sub = rate['base'] + dist + tm\n    fare = max(sub, rate['minimum']) + rate['booking_fee']\n    return {'distance': dist, 'time': tm, 'fare': fare}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: per-minute proration',\n   [{'meters': 500, 'seconds': 61},\n    {'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 30}],\n   {'distance': 125, 'fare': 500, 'time': 31}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 12, 'seconds': 1229},\n    {'base': 0, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],\n   {'distance': 2, 'fare': 1250, 'time': 307}),\n  ('second regression',\n   [{'meters': 0, 'seconds': 2352},\n    {'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 110, 'per_min': 25}],\n   {'distance': 0, 'fare': 1380, 'time': 980}),\n  ('normal control 1',\n   [{'meters': 0, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 30}],\n   {'distance': 0, 'fare': 750, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 15062, 'seconds': 0},\n    {'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 125, 'per_min': 30}],\n   {'distance': 1883, 'fare': 1883, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 12, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 250, 'per_min': 15}],\n   {'distance': 3, 'fare': 1175, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 500, 'seconds': 0},\n    {'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],\n   {'distance': 63, 'fare': 1175, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 500, 'seconds': 61},\n    {'base': 0, 'booking_fee': 0, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],\n   {'distance': 125, 'fare': 1000, 'time': 31}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 12, 'seconds': 826},\n    {'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 95, 'per_min': 35}],\n   {'distance': 1, 'fare': 1000, 'time': 482}),\n  ('second regression',\n   [{'meters': 1004, 'seconds': 90},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],\n   {'distance': 126, 'fare': 1175, 'time': 38}),\n  ('normal control 1',\n   [{'meters': 500, 'seconds': 0},\n    {'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 250, 'per_min': 30}],\n   {'distance': 125, 'fare': 750, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 18143, 'seconds': 0},\n    {'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],\n   {'distance': 2268, 'fare': 2518, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 12, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 35}],\n   {'distance': 1, 'fare': 1175, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 15}],\n   {'distance': 238, 'fare': 750, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 12, 'seconds': 61},\n    {'base': 250, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],\n   {'distance': 1, 'fare': 675, 'time': 15}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 500, 'seconds': 90},\n    {'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 125, 'per_min': 30}],\n   {'distance': 63, 'fare': 675, 'time': 45}),\n  ('second regression',\n   [{'meters': 0, 'seconds': 30},\n    {'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 25}],\n   {'distance': 0, 'fare': 750, 'time': 13}),\n  ('normal control 1',\n   [{'meters': 0, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],\n   {'distance': 0, 'fare': 675, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 1000, 'per_km': 250, 'per_min': 15}],\n   {'distance': 1, 'fare': 1175, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 15}],\n   {'distance': 1, 'fare': 950, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],\n   {'distance': 1, 'fare': 875, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 3247, 'seconds': 2905},\n    {'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 25}],\n   {'distance': 357, 'fare': 1817, 'time': 1210}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 16003, 'seconds': 1892},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 25}],\n   {'distance': 4001, 'fare': 5289, 'time': 788}),\n  ('second regression',\n   [{'meters': 3374, 'seconds': 61},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 25}],\n   {'distance': 422, 'fare': 950, 'time': 25}),\n  ('normal control 1',\n   [{'meters': 500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],\n   {'distance': 63, 'fare': 1175, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 4, 'seconds': 0},\n    {'base': 150, 'booking_fee': 175, 'minimum': 700, 'per_km': 125, 'per_min': 25}],\n   {'distance': 1, 'fare': 875, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 1004, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 30}],\n   {'distance': 251, 'fare': 950, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 35}],\n   {'distance': 275, 'fare': 950, 'time': 0})],\n [('regression: per-minute proration',\n   [{'meters': 4, 'seconds': 30},\n    {'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],\n   {'distance': 0, 'fare': 675, 'time': 8}),\n  ('partial repair probe: per-minute proration',\n   [{'meters': 1004, 'seconds': 30},\n    {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 30}],\n   {'distance': 95, 'fare': 700, 'time': 15}),\n  ('second regression',\n   [{'meters': 500, 'seconds': 1245},\n    {'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],\n   {'distance': 55, 'fare': 1175, 'time': 623}),\n  ('normal control 1',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],\n   {'distance': 625, 'fare': 1050, 'time': 0}),\n  ('normal control 2',\n   [{'meters': 20828, 'seconds': 0},\n    {'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 25}],\n   {'distance': 2604, 'fare': 2854, 'time': 0}),\n  ('normal control 3',\n   [{'meters': 0, 'seconds': 0},\n    {'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 25}],\n   {'distance': 0, 'fare': 875, 'time': 0}),\n  ('normal control 4',\n   [{'meters': 2500, 'seconds': 0},\n    {'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 15}],\n   {'distance': 625, 'fare': 1250, 'time': 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 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-metered-fare-per-minute-proration","generated_at":"2026-09-29T14:50:39.639012+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.","root_cause":"Seconds are divided by 3600 although the rate is per minute.","sha256":"d726d4d456a1dc9c8f5de9d0b446029b4f1444e9bec5cb71bfed814ee2d37476","title":"Per-minute rate applied per hour · 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":41.446,"exit_code":1,"observations":[{"actual":{"distance":125,"fare":500,"time":60},"check":"regression: per-minute proration","expected":{"distance":125,"fare":500,"time":31},"passed":false},{"actual":{"distance":2,"fare":1250,"time":315},"check":"partial repair probe: per-minute proration","expected":{"distance":2,"fare":1250,"time":307},"passed":false},{"actual":{"distance":0,"fare":1400,"time":1000},"check":"second regression","expected":{"distance":0,"fare":1380,"time":980},"passed":false},{"actual":{"distance":0,"fare":750,"time":0},"check":"normal control 1","expected":{"distance":0,"fare":750,"time":0},"passed":true},{"actual":{"distance":1883,"fare":1883,"time":0},"check":"normal control 2","expected":{"distance":1883,"fare":1883,"time":0},"passed":true},{"actual":{"distance":3,"fare":1175,"time":0},"check":"normal control 3","expected":{"distance":3,"fare":1175,"time":0},"passed":true},{"actual":{"distance":63,"fare":1175,"time":0},"check":"normal control 4","expected":{"distance":63,"fare":1175,"time":0},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: per-minute proration\", \"actual\": {\"distance\": 125, \"time\": 60, \"fare\": 500}, \"expected\": {\"distance\": 125, \"fare\": 500, \"time\": 31}, \"passed\": false}, {\"check\": \"partial repair probe: per-minute proration\", \"actual\": {\"distance\": 2, \"time\": 315, \"fare\": 1250}, \"expected\": {\"distance\": 2, \"fare\": 1250, \"time\": 307}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"distance\": 0, \"time\": 1000, \"fare\": 1400}, \"expected\": {\"distance\": 0, \"fare\": 1380, \"time\": 980}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"distance\": 0, \"time\": 0, \"fare\": 750}, \"expected\": {\"distance\": 0, \"fare\": 750, \"time\": 0}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"distance\": 1883, \"time\": 0, \"fare\": 1883}, \"expected\": {\"distance\": 1883, \"fare\": 1883, \"time\": 0}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"distance\": 3, \"time\": 0, \"fare\": 1175}, \"expected\": {\"distance\": 3, \"fare\": 1175, \"time\": 0}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"distance\": 63, \"time\": 0, \"fare\": 1175}, \"expected\": {\"distance\": 63, \"fare\": 1175, \"time\": 0}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.768,"exit_code":1,"observations":[{"actual":{"distance":125,"fare":500,"time":1},"check":"regression: per-minute proration","expected":{"distance":125,"fare":500,"time":31},"passed":false},{"actual":{"distance":2,"fare":1250,"time":5},"check":"partial repair probe: per-minute proration","expected":{"distance":2,"fare":1250,"time":307},"passed":false},{"actual":{"distance":0,"fare":1250,"time":16},"check":"second regression","expected":{"distance":0,"fare":1380,"time":980},"passed":false},{"actual":{"distance":0,"fare":750,"time":0},"check":"normal control 1","expected":{"distance":0,"fare":750,"time":0},"passed":true},{"actual":{"distance":1883,"fare":1883,"time":0},"check":"normal control 2","expected":{"distance":1883,"fare":1883,"time":0},"passed":true},{"actual":{"distance":3,"fare":1175,"time":0},"check":"normal control 3","expected":{"distance":3,"fare":1175,"time":0},"passed":true},{"actual":{"distance":63,"fare":1175,"time":0},"check":"normal control 4","expected":{"distance":63,"fare":1175,"time":0},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: per-minute proration\", \"actual\": {\"distance\": 125, \"time\": 1, \"fare\": 500}, \"expected\": {\"distance\": 125, \"fare\": 500, \"time\": 31}, \"passed\": false}, {\"check\": \"partial repair probe: per-minute proration\", \"actual\": {\"distance\": 2, \"time\": 5, \"fare\": 1250}, \"expected\": {\"distance\": 2, \"fare\": 1250, \"time\": 307}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"distance\": 0, \"time\": 16, \"fare\": 1250}, \"expected\": {\"distance\": 0, \"fare\": 1380, \"time\": 980}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"distance\": 0, \"time\": 0, \"fare\": 750}, \"expected\": {\"distance\": 0, \"fare\": 750, \"time\": 0}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"distance\": 1883, \"time\": 0, \"fare\": 1883}, \"expected\": {\"distance\": 1883, \"fare\": 1883, \"time\": 0}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"distance\": 3, \"time\": 0, \"fare\": 1175}, \"expected\": {\"distance\": 3, \"fare\": 1175, \"time\": 0}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"distance\": 63, \"time\": 0, \"fare\": 1175}, \"expected\": {\"distance\": 63, \"fare\": 1175, \"time\": 0}, \"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."}}