{"abstract":"Every wait fee is capped at a few cents.","category":"Ride-hailing fare and surge pricing","checks":7,"contract":"Wait time starts at driver arrival, or at the scheduled pickup time if the ride was scheduled and the driver arrived early. The first grace seconds are free; beyond that each started minute is billed at per_min cents, up to cap_min billed minutes. Return the wait fee in cents.","evaluation_group":"w2-ride-hailing-fare-surge-pickup-wait-billing","failed_approach":"Scaling the cap as if it were dollars still mixes units.","family":"w2-ride-hailing-fare-surge-pickup-wait-billing-cap-unit","id":"FA-85436","implementations":{"attempt":{"sha256":"a9b7d57b06603a46013785675f59b82631dc32ff54aa4759531d9cf65da8dc69","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(ev, policy):\n    start = ev['arrive'] if ev['scheduled'] is None else max(ev['arrive'], ev['scheduled'])\n    wait = ev['board'] - start\n    if wait <= policy['grace']:\n        return 0\n    minutes = -(-(wait - policy['grace']) // 60)\n    return min(minutes * policy['per_min'], policy['cap_min'] * 100)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: cap unit',\n   [{'arrive': 10000, 'board': 10121, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 35),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11563, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}],\n   125),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10480, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 25),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10300, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10240, 'scheduled': 10120}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10701, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   125),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11200, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   250),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10301, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 80),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10360, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10240, 'scheduled': 10120}, {'cap_min': 10, 'grace': 120, 'per_min': 35}],\n   0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10241, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 35),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11497, 'scheduled': 9940}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 125),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   200),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10360, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 40}],\n   320),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 175),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10900, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   200),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10060, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10420, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10900, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 35}],\n   280),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 200),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10421, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 40}],\n   40),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10060, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10420, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10300, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10180, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 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":"e8108fbda2341b94c85e24831c1dfc52bfbd32f6fb99bc2221ccf6f2de49d971","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(ev, policy):\n    start = ev['arrive'] if ev['scheduled'] is None else max(ev['arrive'], ev['scheduled'])\n    wait = ev['board'] - start\n    if wait <= policy['grace']:\n        return 0\n    minutes = -(-(wait - policy['grace']) // 60)\n    return min(minutes * policy['per_min'], policy['cap_min'])\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: cap unit',\n   [{'arrive': 10000, 'board': 10121, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 35),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11563, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}],\n   125),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10480, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 25),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10300, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10240, 'scheduled': 10120}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10701, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   125),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11200, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   250),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10301, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 80),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10360, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10240, 'scheduled': 10120}, {'cap_min': 10, 'grace': 120, 'per_min': 35}],\n   0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10241, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 35),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11497, 'scheduled': 9940}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 125),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   200),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10360, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 40}],\n   320),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 175),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10900, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   200),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10060, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10420, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10900, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 35}],\n   280),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 200),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10421, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 40}],\n   40),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10060, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10420, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10300, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10180, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 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"},"fixed":{"sha256":"73a1c2af0f1b65c46ceb258ed71c77f7bfe96dcfa29b93e4c716f75e5ba9f620","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(ev, policy):\n    start = ev['arrive'] if ev['scheduled'] is None else max(ev['arrive'], ev['scheduled'])\n    wait = ev['board'] - start\n    if wait <= policy['grace']:\n        return 0\n    minutes = -(-(wait - policy['grace']) // 60)\n    return min(minutes, policy['cap_min']) * policy['per_min']\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: cap unit',\n   [{'arrive': 10000, 'board': 10121, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 35),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11563, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}],\n   125),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10480, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 25),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10300, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10240, 'scheduled': 10120}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10701, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   125),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11200, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   250),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10301, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 80),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10360, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10240, 'scheduled': 10120}, {'cap_min': 10, 'grace': 120, 'per_min': 35}],\n   0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10241, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 35),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 11497, 'scheduled': 9940}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 125),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   200),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10000, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10360, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 35}], 0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 40}],\n   320),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 35}], 175),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10900, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 25}],\n   200),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10060, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10420, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10120, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0)],\n [('regression: cap unit',\n   [{'arrive': 10000, 'board': 10900, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 35}],\n   280),\n  ('partial repair probe: cap unit',\n   [{'arrive': 10000, 'board': 10600, 'scheduled': None}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 200),\n  ('second regression',\n   [{'arrive': 10000, 'board': 10421, 'scheduled': 10300}, {'cap_min': 10, 'grace': 120, 'per_min': 40}],\n   40),\n  ('normal control 1',\n   [{'arrive': 10000, 'board': 10060, 'scheduled': 9940}, {'cap_min': 10, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 2',\n   [{'arrive': 10000, 'board': 10420, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 3',\n   [{'arrive': 10000, 'board': 10300, 'scheduled': 10300}, {'cap_min': 5, 'grace': 120, 'per_min': 40}], 0),\n  ('normal control 4',\n   [{'arrive': 10000, 'board': 10180, 'scheduled': 10120}, {'cap_min': 5, 'grace': 120, 'per_min': 25}], 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-pickup-wait-billing-cap-unit","generated_at":"2026-09-29T14:50:40.362401+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":"Cap the billed minutes before multiplying by the rate.","root_cause":"The minute cap is applied to the cent amount.","sha256":"e4dd8ab75d885953c48aa5a6686bc9b8accc94d5d4a4760379fc952a28ea8521","title":"Wait cap in minutes compared against cents · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":42.345,"exit_code":1,"observations":[{"actual":35,"check":"regression: cap unit","expected":35,"passed":true},{"actual":500,"check":"partial repair probe: cap unit","expected":125,"passed":false},{"actual":25,"check":"second regression","expected":25,"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: cap unit\", \"actual\": 35, \"expected\": 35, \"passed\": true}, {\"check\": \"partial repair probe: cap unit\", \"actual\": 500, \"expected\": 125, \"passed\": false}, {\"check\": \"second regression\", \"actual\": 25, \"expected\": 25, \"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"},"broken":{"elapsed_ms":38.739,"exit_code":1,"observations":[{"actual":10,"check":"regression: cap unit","expected":35,"passed":false},{"actual":5,"check":"partial repair probe: cap unit","expected":125,"passed":false},{"actual":5,"check":"second regression","expected":25,"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: cap unit\", \"actual\": 10, \"expected\": 35, \"passed\": false}, {\"check\": \"partial repair probe: cap unit\", \"actual\": 5, \"expected\": 125, \"passed\": false}, {\"check\": \"second regression\", \"actual\": 5, \"expected\": 25, \"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"},"fixed":{"elapsed_ms":42.282,"exit_code":0,"observations":[{"actual":35,"check":"regression: cap unit","expected":35,"passed":true},{"actual":125,"check":"partial repair probe: cap unit","expected":125,"passed":true},{"actual":25,"check":"second regression","expected":25,"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":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: cap unit\", \"actual\": 35, \"expected\": 35, \"passed\": true}, {\"check\": \"partial repair probe: cap unit\", \"actual\": 125, \"expected\": 125, \"passed\": true}, {\"check\": \"second regression\", \"actual\": 25, \"expected\": 25, \"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\": true}\n"}},"verified":true,"visibility":"public"}