{"abstract":"Exactly 20 requests for 10 drivers publishes 1.5x instead of 2.0x.","category":"Ride-hailing fare and surge pricing","checks":7,"contract":"Each interval gives [open requests, idle drivers]. The raw multiplier (tenths) is 30 when the exact ratio requests/drivers >= 3, 20 when >= 2, 15 when >= 1.5, 12 when >= 1.2, else 10; with zero drivers it is 30 if any request is open, else 10. The published multiplier rises immediately to the raw value but falls by at most 2 tenths per interval, starting from prev. Return the published multiplier per interval.","evaluation_group":"w2-ride-hailing-fare-surge-surge-hysteresis","failed_approach":"Scanning the tiers from the lowest with an early exit stops at 1.2x for every surging zone.","family":"w2-ride-hailing-fare-surge-surge-hysteresis-tier-scan-order","id":"FA-85371","implementations":{"attempt":{"sha256":"3d34048b9cfc8aa0b37bc2c237cd76dbf260832ca0a2ce5737130e2d5dc9eb19","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(obs, prev):\n    tiers = [(30, 30), (20, 20), (15, 15), (12, 12)]\n    cur = prev\n    out = []\n    for req, drv in obs:\n        raw = 10\n        if drv == 0:\n            raw = 30 if req > 0 else 10\n        else:\n            for th, mult in reversed(tiers):\n                if req * 10 >= th * drv:\n                    raw = mult\n                    break\n        cur = raw if raw >= cur else max(raw, cur - 2)\n        out.append(cur)\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tier scan order', [[[10, 10], [11, 10], [29, 20], [26, 0], [30, 10], [15, 5]], 15],\n   [13, 11, 12, 30, 30, 30]),\n  ('partial repair probe: tier scan order', [[[38, 10], [9, 20], [37, 7]], 15], [30, 28, 30]),\n  ('second regression', [[[29, 5], [38, 0], [21, 10], [0, 0], [14, 5]], 30], [30, 30, 28, 26, 24]),\n  ('normal control 1', [[[5, 5], [1, 0]], 12], [10, 30]),\n  ('normal control 2', [[[3, 7], [13, 7], [20, 7], [40, 20], [-1, 0]], 30], [28, 26, 24, 22, 20]),\n  ('normal control 3', [[[7, 10], [10, 10]], 20], [18, 16]),\n  ('normal control 4', [[[3, 7], [1, 7], [4, 10]], 12], [10, 10, 10])],\n [('regression: tier scan order', [[[6, 5], [25, 10], [40, 0], [7, 7], [-1, 0], [28, 7]], 10],\n   [12, 20, 30, 28, 26, 30]),\n  ('partial repair probe: tier scan order', [[[9, 20], [37, 20], [0, 0]], 12], [10, 15, 13]),\n  ('second regression', [[[38, 0], [23, 10], [31, 20], [10, 5], [21, 5]], 12], [30, 28, 26, 24, 30]),\n  ('normal control 1', [[[-1, 0], [0, 0], [9, 10]], 30], [28, 26, 24]),\n  ('normal control 2', [[[22, 20], [0, 0], [7, 5]], 15], [13, 11, 12]),\n  ('normal control 3', [[[13, 7], [0, 0], [33, 0], [0, 0], [59, 20]], 20], [18, 16, 30, 28, 26]),\n  ('normal control 4', [[[0, 0], [0, 0], [10, 7], [20, 20]], 10], [10, 10, 12, 10])],\n [('regression: tier scan order', [[[6, 5], [0, 0], [0, 0], [4, 10]], 12], [12, 10, 10, 10]),\n  ('partial repair probe: tier scan order', [[[31, 10], [29, 10], [30, 20], [28, 20], [5, 10], [17, 5]], 30],\n   [30, 28, 26, 24, 22, 30]),\n  ('second regression', [[[19, 20], [9, 20], [30, 10], [12, 10]], 30], [28, 26, 30, 28]),\n  ('normal control 1', [[[5, 10], [5, 10], [0, 0]], 15], [13, 11, 10]),\n  ('normal control 2', [[[5, 10], [0, 0]], 10], [10, 10]),\n  ('normal control 3', [[[11, 10], [11, 10], [22, 20]], 20], [18, 16, 14]),\n  ('normal control 4', [[[20, 20], [5, 10]], 15], [13, 11])],\n [('regression: tier scan order', [[[60, 20], [60, 20], [7, 7], [21, 0], [12, 10], [7, 7]], 20],\n   [30, 30, 28, 30, 28, 26]),\n  ('partial repair probe: tier scan order', [[[30, 20], [8, 7], [21, 7], [9, 10], [2, 5]], 15],\n   [15, 13, 30, 28, 26]),\n  ('second regression', [[[11, 10], [10, 10], [40, 10]], 30], [28, 26, 30]),\n  ('normal control 1', [[[11, 10], [27, 0], [11, 10], [6, 10], [4, 5]], 30], [28, 30, 28, 26, 24]),\n  ('normal control 2', [[[27, 20], [39, 20], [31, 0], [20, 0], [37, 20], [20, 10]], 20],\n   [18, 16, 30, 30, 28, 26]),\n  ('normal control 3', [[[24, 0], [7, 7]], 20], [30, 28]),\n  ('normal control 4', [[[29, 0], [3, 7]], 10], [30, 28])],\n [('regression: tier scan order', [[[15, 5], [0, 20], [6, 10], [37, 7]], 30], [30, 28, 26, 30]),\n  ('partial repair probe: tier scan order', [[[28, 0], [38, 10], [7, 5], [10, 20]], 20], [30, 30, 28, 26]),\n  ('second regression', [[[38, 20], [9, 20], [29, 20], [12, 5], [21, 20], [19, 0]], 10],\n   [15, 13, 12, 20, 18, 30]),\n  ('normal control 1', [[[10, 10], [10, 10]], 12], [10, 10]),\n  ('normal control 2', [[[11, 10], [11, 10]], 20], [18, 16]),\n  ('normal control 3', [[[0, 0], [4, 5], [21, 20]], 10], [10, 10, 10]),\n  ('normal control 4', [[[20, 0], [37, 20], [11, 10]], 20], [30, 28, 26])]]\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":"f050289a630f8ee2f6d191cc44edf1f9a7639eca8198dc01f93ef4b4e9dc6a6e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(obs, prev):\n    tiers = [(30, 30), (20, 20), (15, 15), (12, 12)]\n    cur = prev\n    out = []\n    for req, drv in obs:\n        raw = 10\n        if drv == 0:\n            raw = 30 if req > 0 else 10\n        else:\n            for th, mult in tiers:\n                if req * 10 > th * drv:\n                    raw = mult\n                    break\n        cur = raw if raw >= cur else max(raw, cur - 2)\n        out.append(cur)\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tier scan order', [[[10, 10], [11, 10], [29, 20], [26, 0], [30, 10], [15, 5]], 15],\n   [13, 11, 12, 30, 30, 30]),\n  ('partial repair probe: tier scan order', [[[38, 10], [9, 20], [37, 7]], 15], [30, 28, 30]),\n  ('second regression', [[[29, 5], [38, 0], [21, 10], [0, 0], [14, 5]], 30], [30, 30, 28, 26, 24]),\n  ('normal control 1', [[[5, 5], [1, 0]], 12], [10, 30]),\n  ('normal control 2', [[[3, 7], [13, 7], [20, 7], [40, 20], [-1, 0]], 30], [28, 26, 24, 22, 20]),\n  ('normal control 3', [[[7, 10], [10, 10]], 20], [18, 16]),\n  ('normal control 4', [[[3, 7], [1, 7], [4, 10]], 12], [10, 10, 10])],\n [('regression: tier scan order', [[[6, 5], [25, 10], [40, 0], [7, 7], [-1, 0], [28, 7]], 10],\n   [12, 20, 30, 28, 26, 30]),\n  ('partial repair probe: tier scan order', [[[9, 20], [37, 20], [0, 0]], 12], [10, 15, 13]),\n  ('second regression', [[[38, 0], [23, 10], [31, 20], [10, 5], [21, 5]], 12], [30, 28, 26, 24, 30]),\n  ('normal control 1', [[[-1, 0], [0, 0], [9, 10]], 30], [28, 26, 24]),\n  ('normal control 2', [[[22, 20], [0, 0], [7, 5]], 15], [13, 11, 12]),\n  ('normal control 3', [[[13, 7], [0, 0], [33, 0], [0, 0], [59, 20]], 20], [18, 16, 30, 28, 26]),\n  ('normal control 4', [[[0, 0], [0, 0], [10, 7], [20, 20]], 10], [10, 10, 12, 10])],\n [('regression: tier scan order', [[[6, 5], [0, 0], [0, 0], [4, 10]], 12], [12, 10, 10, 10]),\n  ('partial repair probe: tier scan order', [[[31, 10], [29, 10], [30, 20], [28, 20], [5, 10], [17, 5]], 30],\n   [30, 28, 26, 24, 22, 30]),\n  ('second regression', [[[19, 20], [9, 20], [30, 10], [12, 10]], 30], [28, 26, 30, 28]),\n  ('normal control 1', [[[5, 10], [5, 10], [0, 0]], 15], [13, 11, 10]),\n  ('normal control 2', [[[5, 10], [0, 0]], 10], [10, 10]),\n  ('normal control 3', [[[11, 10], [11, 10], [22, 20]], 20], [18, 16, 14]),\n  ('normal control 4', [[[20, 20], [5, 10]], 15], [13, 11])],\n [('regression: tier scan order', [[[60, 20], [60, 20], [7, 7], [21, 0], [12, 10], [7, 7]], 20],\n   [30, 30, 28, 30, 28, 26]),\n  ('partial repair probe: tier scan order', [[[30, 20], [8, 7], [21, 7], [9, 10], [2, 5]], 15],\n   [15, 13, 30, 28, 26]),\n  ('second regression', [[[11, 10], [10, 10], [40, 10]], 30], [28, 26, 30]),\n  ('normal control 1', [[[11, 10], [27, 0], [11, 10], [6, 10], [4, 5]], 30], [28, 30, 28, 26, 24]),\n  ('normal control 2', [[[27, 20], [39, 20], [31, 0], [20, 0], [37, 20], [20, 10]], 20],\n   [18, 16, 30, 30, 28, 26]),\n  ('normal control 3', [[[24, 0], [7, 7]], 20], [30, 28]),\n  ('normal control 4', [[[29, 0], [3, 7]], 10], [30, 28])],\n [('regression: tier scan order', [[[15, 5], [0, 20], [6, 10], [37, 7]], 30], [30, 28, 26, 30]),\n  ('partial repair probe: tier scan order', [[[28, 0], [38, 10], [7, 5], [10, 20]], 20], [30, 30, 28, 26]),\n  ('second regression', [[[38, 20], [9, 20], [29, 20], [12, 5], [21, 20], [19, 0]], 10],\n   [15, 13, 12, 20, 18, 30]),\n  ('normal control 1', [[[10, 10], [10, 10]], 12], [10, 10]),\n  ('normal control 2', [[[11, 10], [11, 10]], 20], [18, 16]),\n  ('normal control 3', [[[0, 0], [4, 5], [21, 20]], 10], [10, 10, 10]),\n  ('normal control 4', [[[20, 0], [37, 20], [11, 10]], 20], [30, 28, 26])]]\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":"a06509a5987bd76d8e962f4aa74a1f026e812c203d8e7aa1694207a3de600da6","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(obs, prev):\n    tiers = [(30, 30), (20, 20), (15, 15), (12, 12)]\n    cur = prev\n    out = []\n    for req, drv in obs:\n        raw = 10\n        if drv == 0:\n            raw = 30 if req > 0 else 10\n        else:\n            for th, mult in tiers:\n                if req * 10 >= th * drv:\n                    raw = mult\n                    break\n        cur = raw if raw >= cur else max(raw, cur - 2)\n        out.append(cur)\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tier scan order', [[[10, 10], [11, 10], [29, 20], [26, 0], [30, 10], [15, 5]], 15],\n   [13, 11, 12, 30, 30, 30]),\n  ('partial repair probe: tier scan order', [[[38, 10], [9, 20], [37, 7]], 15], [30, 28, 30]),\n  ('second regression', [[[29, 5], [38, 0], [21, 10], [0, 0], [14, 5]], 30], [30, 30, 28, 26, 24]),\n  ('normal control 1', [[[5, 5], [1, 0]], 12], [10, 30]),\n  ('normal control 2', [[[3, 7], [13, 7], [20, 7], [40, 20], [-1, 0]], 30], [28, 26, 24, 22, 20]),\n  ('normal control 3', [[[7, 10], [10, 10]], 20], [18, 16]),\n  ('normal control 4', [[[3, 7], [1, 7], [4, 10]], 12], [10, 10, 10])],\n [('regression: tier scan order', [[[6, 5], [25, 10], [40, 0], [7, 7], [-1, 0], [28, 7]], 10],\n   [12, 20, 30, 28, 26, 30]),\n  ('partial repair probe: tier scan order', [[[9, 20], [37, 20], [0, 0]], 12], [10, 15, 13]),\n  ('second regression', [[[38, 0], [23, 10], [31, 20], [10, 5], [21, 5]], 12], [30, 28, 26, 24, 30]),\n  ('normal control 1', [[[-1, 0], [0, 0], [9, 10]], 30], [28, 26, 24]),\n  ('normal control 2', [[[22, 20], [0, 0], [7, 5]], 15], [13, 11, 12]),\n  ('normal control 3', [[[13, 7], [0, 0], [33, 0], [0, 0], [59, 20]], 20], [18, 16, 30, 28, 26]),\n  ('normal control 4', [[[0, 0], [0, 0], [10, 7], [20, 20]], 10], [10, 10, 12, 10])],\n [('regression: tier scan order', [[[6, 5], [0, 0], [0, 0], [4, 10]], 12], [12, 10, 10, 10]),\n  ('partial repair probe: tier scan order', [[[31, 10], [29, 10], [30, 20], [28, 20], [5, 10], [17, 5]], 30],\n   [30, 28, 26, 24, 22, 30]),\n  ('second regression', [[[19, 20], [9, 20], [30, 10], [12, 10]], 30], [28, 26, 30, 28]),\n  ('normal control 1', [[[5, 10], [5, 10], [0, 0]], 15], [13, 11, 10]),\n  ('normal control 2', [[[5, 10], [0, 0]], 10], [10, 10]),\n  ('normal control 3', [[[11, 10], [11, 10], [22, 20]], 20], [18, 16, 14]),\n  ('normal control 4', [[[20, 20], [5, 10]], 15], [13, 11])],\n [('regression: tier scan order', [[[60, 20], [60, 20], [7, 7], [21, 0], [12, 10], [7, 7]], 20],\n   [30, 30, 28, 30, 28, 26]),\n  ('partial repair probe: tier scan order', [[[30, 20], [8, 7], [21, 7], [9, 10], [2, 5]], 15],\n   [15, 13, 30, 28, 26]),\n  ('second regression', [[[11, 10], [10, 10], [40, 10]], 30], [28, 26, 30]),\n  ('normal control 1', [[[11, 10], [27, 0], [11, 10], [6, 10], [4, 5]], 30], [28, 30, 28, 26, 24]),\n  ('normal control 2', [[[27, 20], [39, 20], [31, 0], [20, 0], [37, 20], [20, 10]], 20],\n   [18, 16, 30, 30, 28, 26]),\n  ('normal control 3', [[[24, 0], [7, 7]], 20], [30, 28]),\n  ('normal control 4', [[[29, 0], [3, 7]], 10], [30, 28])],\n [('regression: tier scan order', [[[15, 5], [0, 20], [6, 10], [37, 7]], 30], [30, 28, 26, 30]),\n  ('partial repair probe: tier scan order', [[[28, 0], [38, 10], [7, 5], [10, 20]], 20], [30, 30, 28, 26]),\n  ('second regression', [[[38, 20], [9, 20], [29, 20], [12, 5], [21, 20], [19, 0]], 10],\n   [15, 13, 12, 20, 18, 30]),\n  ('normal control 1', [[[10, 10], [10, 10]], 12], [10, 10]),\n  ('normal control 2', [[[11, 10], [11, 10]], 20], [18, 16]),\n  ('normal control 3', [[[0, 0], [4, 5], [21, 20]], 10], [10, 10, 10]),\n  ('normal control 4', [[[20, 0], [37, 20], [11, 10]], 20], [30, 28, 26])]]\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-surge-hysteresis-tier-scan-order","generated_at":"2026-09-29T14:50:39.810746+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":"A ratio equal to a threshold belongs to that tier.","root_cause":"Tier thresholds are compared with strict greater-than.","sha256":"1e2a889632541e307c21c8af4c97cb8decc951413de8abdb7ecccc2eb0f4331d","title":"Exact ratio at a tier edge falls to the lower tier · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":39.095,"exit_code":1,"observations":[{"actual":[13,11,12,30,28,26],"check":"regression: tier scan order","expected":[13,11,12,30,30,30],"passed":false},{"actual":[13,11,12],"check":"partial repair probe: tier scan order","expected":[30,28,30],"passed":false},{"actual":[28,30,28,26,24],"check":"second regression","expected":[30,30,28,26,24],"passed":false},{"actual":[10,30],"check":"normal control 1","expected":[10,30],"passed":true},{"actual":[28,26,24,22,20],"check":"normal control 2","expected":[28,26,24,22,20],"passed":true},{"actual":[18,16],"check":"normal control 3","expected":[18,16],"passed":true},{"actual":[10,10,10],"check":"normal control 4","expected":[10,10,10],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tier scan order\", \"actual\": [13, 11, 12, 30, 28, 26], \"expected\": [13, 11, 12, 30, 30, 30], \"passed\": false}, {\"check\": \"partial repair probe: tier scan order\", \"actual\": [13, 11, 12], \"expected\": [30, 28, 30], \"passed\": false}, {\"check\": \"second regression\", \"actual\": [28, 30, 28, 26, 24], \"expected\": [30, 30, 28, 26, 24], \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": [10, 30], \"expected\": [10, 30], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [28, 26, 24, 22, 20], \"expected\": [28, 26, 24, 22, 20], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [18, 16], \"expected\": [18, 16], \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": [10, 10, 10], \"expected\": [10, 10, 10], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":41.215,"exit_code":1,"observations":[{"actual":[13,11,12,30,28,26],"check":"regression: tier scan order","expected":[13,11,12,30,30,30],"passed":false},{"actual":[30,28,30],"check":"partial repair probe: tier scan order","expected":[30,28,30],"passed":true},{"actual":[30,30,28,26,24],"check":"second regression","expected":[30,30,28,26,24],"passed":true},{"actual":[10,30],"check":"normal control 1","expected":[10,30],"passed":true},{"actual":[28,26,24,22,20],"check":"normal control 2","expected":[28,26,24,22,20],"passed":true},{"actual":[18,16],"check":"normal control 3","expected":[18,16],"passed":true},{"actual":[10,10,10],"check":"normal control 4","expected":[10,10,10],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tier scan order\", \"actual\": [13, 11, 12, 30, 28, 26], \"expected\": [13, 11, 12, 30, 30, 30], \"passed\": false}, {\"check\": \"partial repair probe: tier scan order\", \"actual\": [30, 28, 30], \"expected\": [30, 28, 30], \"passed\": true}, {\"check\": \"second regression\", \"actual\": [30, 30, 28, 26, 24], \"expected\": [30, 30, 28, 26, 24], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [10, 30], \"expected\": [10, 30], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [28, 26, 24, 22, 20], \"expected\": [28, 26, 24, 22, 20], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [18, 16], \"expected\": [18, 16], \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": [10, 10, 10], \"expected\": [10, 10, 10], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":39.338,"exit_code":0,"observations":[{"actual":[13,11,12,30,30,30],"check":"regression: tier scan order","expected":[13,11,12,30,30,30],"passed":true},{"actual":[30,28,30],"check":"partial repair probe: tier scan order","expected":[30,28,30],"passed":true},{"actual":[30,30,28,26,24],"check":"second regression","expected":[30,30,28,26,24],"passed":true},{"actual":[10,30],"check":"normal control 1","expected":[10,30],"passed":true},{"actual":[28,26,24,22,20],"check":"normal control 2","expected":[28,26,24,22,20],"passed":true},{"actual":[18,16],"check":"normal control 3","expected":[18,16],"passed":true},{"actual":[10,10,10],"check":"normal control 4","expected":[10,10,10],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tier scan order\", \"actual\": [13, 11, 12, 30, 30, 30], \"expected\": [13, 11, 12, 30, 30, 30], \"passed\": true}, {\"check\": \"partial repair probe: tier scan order\", \"actual\": [30, 28, 30], \"expected\": [30, 28, 30], \"passed\": true}, {\"check\": \"second regression\", \"actual\": [30, 30, 28, 26, 24], \"expected\": [30, 30, 28, 26, 24], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [10, 30], \"expected\": [10, 30], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [28, 26, 24, 22, 20], \"expected\": [28, 26, 24, 22, 20], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [18, 16], \"expected\": [18, 16], \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": [10, 10, 10], \"expected\": [10, 10, 10], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}