{"abstract":"Out-of-order uploads zigzag the trace and inflate or reject distance.","category":"Ride-hailing fare and surge pricing","checks":7,"contract":"Billable distance from GPS pings [t seconds, x m, y m, accuracy m, paused]. Sort by time (stable); drop pings with accuracy worse than max_acc; among remaining pings with the same timestamp keep the first. Walk the kept pings with an anchor: segment length is the floored Euclidean distance (isqrt); if it implies a speed above max_speed m/s the ping is discarded and the anchor stays; otherwise the segment is billed unless either endpoint is paused, and the anchor moves. Return total meters.","contract_signature":"pings, max_acc, max_speed","evaluation_group":"w2-ride-hailing-fare-surge-gps-trace-distance","failed_approach":"Reversing a descending sort reverses same-timestamp pings, so duplicates keep the last one.","family":"w2-ride-hailing-fare-surge-gps-trace-distance-time-ordering","id":"FA-85521","implementations":{"attempt":{"sha256":"e262e4212d13f95e489b69eb42cddfff378cd31b4cf88fcd3e312224f3f94df8","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pings, max_acc, max_speed):\n    kept = []\n    seen = set()\n    for p in sorted(pings, key=lambda p: -p[0])[::-1]:\n        if p[3] > max_acc:\n            continue\n        if p[0] in seen:\n            continue\n        seen.add(p[0])\n        kept.append(p)\n    total = 0\n    anchor = None\n    for p in kept:\n        if anchor is None:\n            anchor = p\n            continue\n        d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)\n        dt = p[0] - anchor[0]\n        if d > max_speed * dt:\n            continue\n        if not (p[4] or anchor[4]):\n            total += d\n        anchor = p\n    return total\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: time ordering',\n   [[[40, 1100, 58, 20, False], [25, 270, 5, 20, False], [35, 300, 5, 5, True], [10, 90, 20, 20, False],\n     [10, 150, 20, 20, False], [35, 700, 38, 20, False], [15, 210, 5, 25, False], [5, 45, 0, 20, True]],\n    20, 30],\n   180),\n  ('partial repair probe: time ordering',\n   [[[0, 45, 0, 10, False], [5, 75, 20, 5, False], [5, 75, 5, 5, True], [5, 105, 25, 10, False]], 20, 30],\n   36),\n  ('second regression',\n   [[[5, 60, -15, 60, True], [15, 60, 5, 25, True], [20, 120, -10, 20, False], [20, 150, 10, 20, True],\n     [30, 150, -5, 20, False]],\n    20, 30],\n   30),\n  ('normal control 1',\n   [[[5, 45, 20, 10, True], [5, 75, 53, 60, False], [15, 135, 73, 5, True], [20, 165, 73, 20, False],\n     [30, 565, 106, 5, True]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 60, -15, 20, False], [10, 60, 5, 5, True], [15, 120, 25, 60, True], [25, 150, 10, 20, False]], 20,\n    30],\n   0),\n  ('normal control 3',\n   [[[5, 60, -15, 60, True], [10, 60, 18, 20, True], [10, 460, 38, 60, False], [15, 490, 23, 20, False],\n     [20, 535, 8, 25, False], [20, 595, 41, 60, True], [25, 595, 74, 20, False], [35, 640, 74, 20, False]],\n    20, 30],\n   0),\n  ('normal control 4',\n   [[[5, 400, 0, 25, True], [10, 445, 33, 60, False], [20, 445, 66, 25, False], [25, 490, 86, 10, False]], 20,\n    30],\n   0)],\n [('regression: time ordering',\n   [[[20, 120, 20, 10, False], [30, 610, 53, 60, False], [20, 165, 20, 60, True], [10, 60, 20, 20, False],\n     [35, 670, 38, 20, True], [25, 210, 20, 60, False]],\n    20, 30],\n   60),\n  ('partial repair probe: time ordering',\n   [[[10, 400, 0, 5, False], [20, 460, 20, 20, False], [20, 490, 5, 60, True], [20, 490, 25, 5, True],\n     [20, 490, 10, 20, False]],\n    20, 30],\n   63),\n  ('second regression',\n   [[[15, 505, 38, 10, False], [5, 400, -15, 60, False], [20, 535, 23, 20, False], [10, 460, 18, 5, False]],\n    20, 30],\n   82),\n  ('normal control 1',\n   [[[0, 60, 33, 20, False], [0, 90, 18, 20, False], [0, 135, 38, 20, False], [5, 535, 58, 20, False],\n     [5, 595, 58, 10, True]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 400, -15, 10, False], [15, 400, -30, 10, False], [25, 430, -45, 20, False],\n     [35, 830, -60, 60, False], [35, 1230, -40, 20, False], [35, 1290, -40, 25, False]],\n    20, 30],\n   48),\n  ('normal control 3',\n   [[[5, 0, 20, 60, False], [10, 400, 20, 5, False], [15, 430, 53, 20, False], [15, 430, 38, 60, False]], 20,\n    30],\n   44),\n  ('normal control 4',\n   [[[5, 400, 20, 5, True], [15, 400, 20, 5, False], [25, 445, 5, 60, False], [35, 490, 5, 25, False],\n     [35, 520, 38, 60, True], [45, 550, 58, 20, False]],\n    20, 30],\n   154)],\n [('regression: time ordering',\n   [[[20, 980, 113, 20, False], [5, 490, 73, 20, False], [20, 980, 113, 60, False], [0, 400, 20, 20, True],\n     [5, 430, 53, 20, False], [10, 535, 93, 20, False], [20, 935, 93, 5, False]],\n    20, 30],\n   49),\n  ('partial repair probe: time ordering',\n   [[[5, 0, 0, 5, False], [15, 45, 20, 5, False], [20, 45, 5, 10, False], [20, 90, 38, 10, False],\n     [25, 490, 38, 20, False]],\n    20, 30],\n   64),\n  ('second regression',\n   [[[10, 30, 33, 60, False], [20, 135, 33, 20, False], [15, 75, 33, 60, False], [35, 935, 73, 20, False],\n     [35, 980, 106, 20, True], [30, 535, 53, 5, False], [30, 135, 53, 20, False]],\n    20, 30],\n   0),\n  ('normal control 1',\n   [[[10, 400, 0, 60, True], [15, 430, 20, 10, False], [15, 830, 40, 20, False], [25, 890, 73, 60, False]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[10, 400, -15, 5, False], [15, 460, -30, 10, True], [20, 520, 3, 10, True], [25, 550, 23, 20, True],\n     [30, 950, 8, 60, False]],\n    20, 30],\n   0),\n  ('normal control 3',\n   [[[10, 60, 20, 20, False], [15, 60, 20, 60, False], [25, 460, 5, 60, False], [25, 490, 25, 25, True]], 20,\n    30],\n   0),\n  ('normal control 4',\n   [[[10, 400, 0, 25, False], [10, 460, 0, 25, False], [15, 520, -15, 20, False], [15, 520, -15, 5, True]],\n    20, 30],\n   0)],\n [('regression: time ordering',\n   [[[15, 860, 23, 5, True], [25, 1010, 23, 5, False], [20, 965, 23, 20, False], [10, 460, -10, 5, False],\n     [20, 905, 23, 5, False], [5, 400, 20, 5, False], [10, 430, 5, 5, True]],\n    20, 30],\n   67),\n  ('partial repair probe: time ordering',\n   [[[15, 60, 20, 20, False], [25, 120, 58, 20, False], [25, 90, 25, 20, False], [15, 90, 40, 20, True],\n     [40, 165, 28, 60, False], [35, 165, 43, 20, False], [45, 195, 48, 5, False], [10, 0, 20, 20, True]],\n    20, 30],\n   148),\n  ('second regression',\n   [[[35, 995, 38, 10, False], [20, 460, 20, 60, True], [35, 950, 53, 20, True], [40, 1055, 23, 20, False],\n     [10, 60, 20, 5, False], [25, 490, 20, 10, False], [15, 460, 20, 20, False], [35, 890, 53, 25, False]],\n    20, 30],\n   430),\n  ('normal control 1',\n   [[[5, 60, 20, 5, False], [5, 90, 40, 5, False], [10, 150, 60, 60, False], [15, 195, 60, 20, True],\n     [25, 225, 93, 20, False], [30, 255, 93, 20, False]],\n    20, 30],\n   30),\n  ('normal control 2',\n   [[[10, 445, -15, 20, False], [10, 400, 0, 25, True], [10, 490, -30, 5, False]], 20, 30], 0),\n  ('normal control 3',\n   [[[10, 400, 20, 20, False], [10, 845, 38, 60, False], [15, 905, 38, 25, False], [10, 800, 20, 20, False],\n     [10, 845, 53, 20, True]],\n    20, 30],\n   0),\n  ('normal control 4',\n   [[[5, 45, 20, 20, True], [15, 90, 20, 25, False], [20, 490, 53, 10, False], [20, 490, 86, 20, False]], 20,\n    30],\n   0)],\n [('regression: time ordering',\n   [[[45, 1115, 153, 20, False], [15, 90, 20, 25, False], [20, 210, 60, 5, False], [15, 150, 40, 10, False],\n     [40, 1055, 133, 10, False], [35, 655, 113, 60, False], [10, 45, 20, 20, False],\n     [25, 255, 93, 25, False]],\n    20, 30],\n   169),\n  ('partial repair probe: time ordering',\n   [[[40, 980, 3, 25, False], [10, 400, 0, 20, False], [15, 475, 18, 20, False], [10, 430, 33, 5, False],\n     [25, 905, 18, 10, True], [15, 875, 18, 20, False], [50, 980, 3, 25, True], [30, 935, 3, 5, False]],\n    20, 30],\n   77),\n  ('second regression',\n   [[[10, 60, 53, 60, True], [25, 550, 139, 5, False], [15, 120, 86, 20, False], [5, 60, 33, 20, False],\n     [25, 150, 106, 10, True]],\n    20, 30],\n   80),\n  ('normal control 1',\n   [[[15, 445, 40, 5, True], [15, 445, 20, 25, False], [15, 475, 25, 20, True], [10, 400, 20, 25, False]], 20,\n    30],\n   0),\n  ('normal control 2',\n   [[[5, 45, 33, 20, False], [10, 90, 66, 25, True], [20, 520, 132, 20, False], [15, 120, 99, 10, False]], 20,\n    30],\n   99),\n  ('normal control 3',\n   [[[5, 60, -15, 20, False], [5, 90, -15, 60, False], [15, 490, 18, 20, False], [15, 520, 18, 20, False],\n     [25, 565, 3, 20, False]],\n    20, 30],\n   505),\n  ('normal control 4',\n   [[[5, 400, 0, 20, True], [15, 800, -15, 25, False], [15, 800, 18, 10, False], [25, 845, 51, 5, False]], 20,\n    30],\n   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":"3e4e3ccb580395eef4a4f4467578dd5a5535a5d4bf68f5816b593d804f10974c","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(pings, max_acc, max_speed):\n    kept = []\n    seen = set()\n    for p in pings:\n        if p[3] > max_acc:\n            continue\n        if p[0] in seen:\n            continue\n        seen.add(p[0])\n        kept.append(p)\n    total = 0\n    anchor = None\n    for p in kept:\n        if anchor is None:\n            anchor = p\n            continue\n        d = math.isqrt((p[1] - anchor[1]) ** 2 + (p[2] - anchor[2]) ** 2)\n        dt = p[0] - anchor[0]\n        if d > max_speed * dt:\n            continue\n        if not (p[4] or anchor[4]):\n            total += d\n        anchor = p\n    return total\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: time ordering',\n   [[[40, 1100, 58, 20, False], [25, 270, 5, 20, False], [35, 300, 5, 5, True], [10, 90, 20, 20, False],\n     [10, 150, 20, 20, False], [35, 700, 38, 20, False], [15, 210, 5, 25, False], [5, 45, 0, 20, True]],\n    20, 30],\n   180),\n  ('partial repair probe: time ordering',\n   [[[0, 45, 0, 10, False], [5, 75, 20, 5, False], [5, 75, 5, 5, True], [5, 105, 25, 10, False]], 20, 30],\n   36),\n  ('second regression',\n   [[[5, 60, -15, 60, True], [15, 60, 5, 25, True], [20, 120, -10, 20, False], [20, 150, 10, 20, True],\n     [30, 150, -5, 20, False]],\n    20, 30],\n   30),\n  ('normal control 1',\n   [[[5, 45, 20, 10, True], [5, 75, 53, 60, False], [15, 135, 73, 5, True], [20, 165, 73, 20, False],\n     [30, 565, 106, 5, True]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 60, -15, 20, False], [10, 60, 5, 5, True], [15, 120, 25, 60, True], [25, 150, 10, 20, False]], 20,\n    30],\n   0),\n  ('normal control 3',\n   [[[5, 60, -15, 60, True], [10, 60, 18, 20, True], [10, 460, 38, 60, False], [15, 490, 23, 20, False],\n     [20, 535, 8, 25, False], [20, 595, 41, 60, True], [25, 595, 74, 20, False], [35, 640, 74, 20, False]],\n    20, 30],\n   0),\n  ('normal control 4',\n   [[[5, 400, 0, 25, True], [10, 445, 33, 60, False], [20, 445, 66, 25, False], [25, 490, 86, 10, False]], 20,\n    30],\n   0)],\n [('regression: time ordering',\n   [[[20, 120, 20, 10, False], [30, 610, 53, 60, False], [20, 165, 20, 60, True], [10, 60, 20, 20, False],\n     [35, 670, 38, 20, True], [25, 210, 20, 60, False]],\n    20, 30],\n   60),\n  ('partial repair probe: time ordering',\n   [[[10, 400, 0, 5, False], [20, 460, 20, 20, False], [20, 490, 5, 60, True], [20, 490, 25, 5, True],\n     [20, 490, 10, 20, False]],\n    20, 30],\n   63),\n  ('second regression',\n   [[[15, 505, 38, 10, False], [5, 400, -15, 60, False], [20, 535, 23, 20, False], [10, 460, 18, 5, False]],\n    20, 30],\n   82),\n  ('normal control 1',\n   [[[0, 60, 33, 20, False], [0, 90, 18, 20, False], [0, 135, 38, 20, False], [5, 535, 58, 20, False],\n     [5, 595, 58, 10, True]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[5, 400, -15, 10, False], [15, 400, -30, 10, False], [25, 430, -45, 20, False],\n     [35, 830, -60, 60, False], [35, 1230, -40, 20, False], [35, 1290, -40, 25, False]],\n    20, 30],\n   48),\n  ('normal control 3',\n   [[[5, 0, 20, 60, False], [10, 400, 20, 5, False], [15, 430, 53, 20, False], [15, 430, 38, 60, False]], 20,\n    30],\n   44),\n  ('normal control 4',\n   [[[5, 400, 20, 5, True], [15, 400, 20, 5, False], [25, 445, 5, 60, False], [35, 490, 5, 25, False],\n     [35, 520, 38, 60, True], [45, 550, 58, 20, False]],\n    20, 30],\n   154)],\n [('regression: time ordering',\n   [[[20, 980, 113, 20, False], [5, 490, 73, 20, False], [20, 980, 113, 60, False], [0, 400, 20, 20, True],\n     [5, 430, 53, 20, False], [10, 535, 93, 20, False], [20, 935, 93, 5, False]],\n    20, 30],\n   49),\n  ('partial repair probe: time ordering',\n   [[[5, 0, 0, 5, False], [15, 45, 20, 5, False], [20, 45, 5, 10, False], [20, 90, 38, 10, False],\n     [25, 490, 38, 20, False]],\n    20, 30],\n   64),\n  ('second regression',\n   [[[10, 30, 33, 60, False], [20, 135, 33, 20, False], [15, 75, 33, 60, False], [35, 935, 73, 20, False],\n     [35, 980, 106, 20, True], [30, 535, 53, 5, False], [30, 135, 53, 20, False]],\n    20, 30],\n   0),\n  ('normal control 1',\n   [[[10, 400, 0, 60, True], [15, 430, 20, 10, False], [15, 830, 40, 20, False], [25, 890, 73, 60, False]],\n    20, 30],\n   0),\n  ('normal control 2',\n   [[[10, 400, -15, 5, False], [15, 460, -30, 10, True], [20, 520, 3, 10, True], [25, 550, 23, 20, True],\n     [30, 950, 8, 60, False]],\n    20, 30],\n   0),\n  ('normal control 3',\n   [[[10, 60, 20, 20, False], [15, 60, 20, 60, False], [25, 460, 5, 60, False], [25, 490, 25, 25, True]], 20,\n    30],\n   0),\n  ('normal control 4',\n   [[[10, 400, 0, 25, False], [10, 460, 0, 25, False], [15, 520, -15, 20, False], [15, 520, -15, 5, True]],\n    20, 30],\n   0)],\n [('regression: time ordering',\n   [[[15, 860, 23, 5, True], [25, 1010, 23, 5, False], [20, 965, 23, 20, False], [10, 460, -10, 5, False],\n     [20, 905, 23, 5, False], [5, 400, 20, 5, False], [10, 430, 5, 5, True]],\n    20, 30],\n   67),\n  ('partial repair probe: time ordering',\n   [[[15, 60, 20, 20, False], [25, 120, 58, 20, False], [25, 90, 25, 20, False], [15, 90, 40, 20, True],\n     [40, 165, 28, 60, False], [35, 165, 43, 20, False], [45, 195, 48, 5, False], [10, 0, 20, 20, True]],\n    20, 30],\n   148),\n  ('second regression',\n   [[[35, 995, 38, 10, False], [20, 460, 20, 60, True], [35, 950, 53, 20, True], [40, 1055, 23, 20, False],\n     [10, 60, 20, 5, False], [25, 490, 20, 10, False], [15, 460, 20, 20, False], [35, 890, 53, 25, False]],\n    20, 30],\n   430),\n  ('normal control 1',\n   [[[5, 60, 20, 5, False], [5, 90, 40, 5, False], [10, 150, 60, 60, False], [15, 195, 60, 20, True],\n     [25, 225, 93, 20, False], [30, 255, 93, 20, False]],\n    20, 30],\n   30),\n  ('normal control 2',\n   [[[10, 445, -15, 20, False], [10, 400, 0, 25, True], [10, 490, -30, 5, False]], 20, 30], 0),\n  ('normal control 3',\n   [[[10, 400, 20, 20, False], [10, 845, 38, 60, False], [15, 905, 38, 25, False], [10, 800, 20, 20, False],\n     [10, 845, 53, 20, True]],\n    20, 30],\n   0),\n  ('normal control 4',\n   [[[5, 45, 20, 20, True], [15, 90, 20, 25, False], [20, 490, 53, 10, False], [20, 490, 86, 20, False]], 20,\n    30],\n   0)],\n [('regression: time ordering',\n   [[[45, 1115, 153, 20, False], [15, 90, 20, 25, False], [20, 210, 60, 5, False], [15, 150, 40, 10, False],\n     [40, 1055, 133, 10, False], [35, 655, 113, 60, False], [10, 45, 20, 20, False],\n     [25, 255, 93, 25, False]],\n    20, 30],\n   169),\n  ('partial repair probe: time ordering',\n   [[[40, 980, 3, 25, False], [10, 400, 0, 20, False], [15, 475, 18, 20, False], [10, 430, 33, 5, False],\n     [25, 905, 18, 10, True], [15, 875, 18, 20, False], [50, 980, 3, 25, True], [30, 935, 3, 5, False]],\n    20, 30],\n   77),\n  ('second regression',\n   [[[10, 60, 53, 60, True], [25, 550, 139, 5, False], [15, 120, 86, 20, False], [5, 60, 33, 20, False],\n     [25, 150, 106, 10, True]],\n    20, 30],\n   80),\n  ('normal control 1',\n   [[[15, 445, 40, 5, True], [15, 445, 20, 25, False], [15, 475, 25, 20, True], [10, 400, 20, 25, False]], 20,\n    30],\n   0),\n  ('normal control 2',\n   [[[5, 45, 33, 20, False], [10, 90, 66, 25, True], [20, 520, 132, 20, False], [15, 120, 99, 10, False]], 20,\n    30],\n   99),\n  ('normal control 3',\n   [[[5, 60, -15, 20, False], [5, 90, -15, 60, False], [15, 490, 18, 20, False], [15, 520, 18, 20, False],\n     [25, 565, 3, 20, False]],\n    20, 30],\n   505),\n  ('normal control 4',\n   [[[5, 400, 0, 20, True], [15, 800, -15, 25, False], [15, 800, 18, 10, False], [25, 845, 51, 5, False]], 20,\n    30],\n   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-gps-trace-distance-time-ordering","generated_at":"2026-09-29T14:50:41.149608+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":"Pings are processed in the order they were received instead of by timestamp.","sha256":"8476f8cf6c1c87c22d520de7756793aa0a21034fa04c97b64cbc2bbb290eb93b","title":"Pings billed in arrival order · 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":39.879,"exit_code":1,"observations":[{"actual":120,"check":"regression: time ordering","expected":180,"passed":false},{"actual":65,"check":"partial repair probe: time ordering","expected":36,"passed":false},{"actual":0,"check":"second regression","expected":30,"passed":false},{"actual":0,"check":"normal control 1","expected":0,"passed":true},{"actual":0,"check":"normal control 2","expected":0,"passed":true},{"actual":0,"check":"normal control 3","expected":0,"passed":true},{"actual":0,"check":"normal control 4","expected":0,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: time ordering\", \"actual\": 120, \"expected\": 180, \"passed\": false}, {\"check\": \"partial repair probe: time ordering\", \"actual\": 65, \"expected\": 36, \"passed\": false}, {\"check\": \"second regression\", \"actual\": 0, \"expected\": 30, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":41.645,"exit_code":1,"observations":[{"actual":0,"check":"regression: time ordering","expected":180,"passed":false},{"actual":36,"check":"partial repair probe: time ordering","expected":36,"passed":true},{"actual":30,"check":"second regression","expected":30,"passed":true},{"actual":0,"check":"normal control 1","expected":0,"passed":true},{"actual":0,"check":"normal control 2","expected":0,"passed":true},{"actual":0,"check":"normal control 3","expected":0,"passed":true},{"actual":0,"check":"normal control 4","expected":0,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: time ordering\", \"actual\": 0, \"expected\": 180, \"passed\": false}, {\"check\": \"partial repair probe: time ordering\", \"actual\": 36, \"expected\": 36, \"passed\": true}, {\"check\": \"second regression\", \"actual\": 30, \"expected\": 30, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 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."}}