{"abstract":"A long cue that started earlier is missed when a short cue ends between it and the query.","category":"Subtitle cue timing","checks":9,"contract":"cues [start,end] are sorted by start. Return the ascending indices of cues active at t (start <= t < end). The search bisects the starts and walks backwards while the running maximum end of the prefix exceeds t.","evaluation_group":"w2-subtitle-cue-timing-active-cue-lookup","failed_approach":"Requiring both conditions still stops at the first inactive cue.","family":"w2-subtitle-cue-timing-active-cue-lookup-backward-walk-pruning","id":"FA-78301","implementations":{"attempt":{"sha256":"9b5123209f0fd1418b6c1f7c18aa8fbf51dc0e4356efdc900928a6b55f4634e1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport bisect\nN = 1\nobservations = []\ndef solve(cues, t):\n    starts=[c[0] for c in cues]\n    hi=bisect.bisect_right(starts,t)\n    maxend=[]\n    run=0\n    for c in cues:\n        run=max(run,c[1])\n        maxend.append(run)\n    res=[]\n    i=hi-1\n    while i>=0 and maxend[i]>t and cues[i][1]>t:\n        if cues[i][1]>t:\n            res.append(i)\n        i-=1\n    return sorted(res)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: backward walk pruning', [[[0, 1000], [100, 200], [300, 400]], 350], [0, 2]), ('regression variant: backward walk pruning', [[[300, 600], [300, 350], [400, 700], [400, 500]], 600], [2]), ('partial repair probe: backward walk pruning', [[[0, 100], [0, 1000], [400, 500], [500, 600], [500, 1500], [500, 550]], 600], [1, 4]), ('partial repair variant: backward walk pruning', [[[200, 250], [200, 1200], [300, 350], [300, 350], [400, 700]], 600], [1, 4]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 100], [100, 1100], [200, 300], [200, 500]], 50], [0]), ('normal control', [[[100, 400], [300, 400], [300, 400], [300, 350], [400, 500], [500, 800]], 300], [0, 1, 2, 3]), ('normal control', [[[100, 400], [300, 400], [400, 450], [400, 700], [400, 1400]], 0], [])], [('regression: backward walk pruning', [[[0, 50], [0, 500], [200, 250]], 300], [1]), ('regression variant: backward walk pruning', [[[100, 1100], [100, 1100], [400, 500], [400, 700], [400, 450], [500, 550]], 500], [0, 1, 3, 5]), ('partial repair probe: backward walk pruning', [[[0, 100], [200, 1200], [300, 400], [500, 800]], 600], [1, 3]), ('partial repair variant: backward walk pruning', [[[0, 1000], [0, 300], [100, 1100], [100, 150], [100, 150], [400, 500]], 200], [0, 1, 2]), ('boundary control', [[[0, 100]], 100], []), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('normal control', [[[0, 1000], [200, 300], [400, 450]], 200], [0, 1]), ('normal control', [[[100, 200], [200, 250]], 0], []), ('normal control', [[[0, 50], [100, 400], [200, 500], [400, 450], [500, 1500]], 1200], [4])], [('regression: backward walk pruning', [[[0, 100], [0, 1000], [400, 500], [500, 600], [500, 1500], [500, 550]], 600], [1, 4]), ('regression variant: backward walk pruning', [[[0, 300], [0, 100], [200, 1200], [200, 300], [300, 600], [300, 1300]], 400], [2, 4, 5]), ('partial repair probe: backward walk pruning', [[[0, 1000], [100, 150], [100, 400], [200, 1200]], 300], [0, 2, 3]), ('partial repair variant: backward walk pruning', [[[100, 1100], [200, 250], [400, 450]], 300], [0]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 1000], [100, 1100], [200, 500], [200, 500]], 200], [0, 1, 2, 3]), ('normal control', [[[0, 50]], 400], []), ('normal control', [[[0, 100], [0, 100], [100, 150]], 200], [])], [('regression: backward walk pruning', [[[0, 100], [200, 1200], [300, 400], [500, 800]], 600], [1, 3]), ('regression variant: backward walk pruning', [[[0, 300], [0, 50], [100, 200], [200, 500], [400, 500]], 100], [0, 2]), ('partial repair probe: backward walk pruning', [[[300, 600], [300, 350], [400, 700], [400, 500]], 600], [2]), ('partial repair variant: backward walk pruning', [[[0, 100], [200, 1200], [200, 250], [200, 1200], [500, 600]], 500], [1, 3, 4]), ('boundary control', [[[0, 100]], 100], []), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('normal control', [[[0, 100], [0, 100], [400, 700], [500, 1500]], 150], []), ('normal control', [[[200, 500]], 500], []), ('normal control', [[[500, 550]], 50], [])], [('regression: backward walk pruning', [[[0, 1000], [100, 150], [100, 400], [200, 1200]], 300], [0, 2, 3]), ('regression variant: backward walk pruning', [[[200, 250], [200, 1200], [300, 350], [300, 350], [400, 700]], 600], [1, 4]), ('partial repair probe: backward walk pruning', [[[100, 1100], [100, 1100], [400, 500], [400, 700], [400, 450], [500, 550]], 500], [0, 1, 3, 5]), ('partial repair variant: backward walk pruning', [[[0, 1000], [300, 350], [400, 1400]], 400], [0, 2]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 300], [100, 1100], [200, 250], [500, 550]], 100], [0, 1]), ('normal control', [[[400, 450]], 150], []), ('normal control', [[[200, 300]], 150], [])]]\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":"00cdadbffe79a40c69057d97828b3ab961358e2945d4aa6144b042804e63f5c8","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport bisect\nN = 1\nobservations = []\ndef solve(cues, t):\n    starts=[c[0] for c in cues]\n    hi=bisect.bisect_right(starts,t)\n    maxend=[]\n    run=0\n    for c in cues:\n        run=max(run,c[1])\n        maxend.append(run)\n    res=[]\n    i=hi-1\n    while i>=0 and cues[i][1]>t:\n        if cues[i][1]>t:\n            res.append(i)\n        i-=1\n    return sorted(res)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: backward walk pruning', [[[0, 1000], [100, 200], [300, 400]], 350], [0, 2]), ('regression variant: backward walk pruning', [[[300, 600], [300, 350], [400, 700], [400, 500]], 600], [2]), ('partial repair probe: backward walk pruning', [[[0, 100], [0, 1000], [400, 500], [500, 600], [500, 1500], [500, 550]], 600], [1, 4]), ('partial repair variant: backward walk pruning', [[[200, 250], [200, 1200], [300, 350], [300, 350], [400, 700]], 600], [1, 4]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 100], [100, 1100], [200, 300], [200, 500]], 50], [0]), ('normal control', [[[100, 400], [300, 400], [300, 400], [300, 350], [400, 500], [500, 800]], 300], [0, 1, 2, 3]), ('normal control', [[[100, 400], [300, 400], [400, 450], [400, 700], [400, 1400]], 0], [])], [('regression: backward walk pruning', [[[0, 50], [0, 500], [200, 250]], 300], [1]), ('regression variant: backward walk pruning', [[[100, 1100], [100, 1100], [400, 500], [400, 700], [400, 450], [500, 550]], 500], [0, 1, 3, 5]), ('partial repair probe: backward walk pruning', [[[0, 100], [200, 1200], [300, 400], [500, 800]], 600], [1, 3]), ('partial repair variant: backward walk pruning', [[[0, 1000], [0, 300], [100, 1100], [100, 150], [100, 150], [400, 500]], 200], [0, 1, 2]), ('boundary control', [[[0, 100]], 100], []), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('normal control', [[[0, 1000], [200, 300], [400, 450]], 200], [0, 1]), ('normal control', [[[100, 200], [200, 250]], 0], []), ('normal control', [[[0, 50], [100, 400], [200, 500], [400, 450], [500, 1500]], 1200], [4])], [('regression: backward walk pruning', [[[0, 100], [0, 1000], [400, 500], [500, 600], [500, 1500], [500, 550]], 600], [1, 4]), ('regression variant: backward walk pruning', [[[0, 300], [0, 100], [200, 1200], [200, 300], [300, 600], [300, 1300]], 400], [2, 4, 5]), ('partial repair probe: backward walk pruning', [[[0, 1000], [100, 150], [100, 400], [200, 1200]], 300], [0, 2, 3]), ('partial repair variant: backward walk pruning', [[[100, 1100], [200, 250], [400, 450]], 300], [0]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 1000], [100, 1100], [200, 500], [200, 500]], 200], [0, 1, 2, 3]), ('normal control', [[[0, 50]], 400], []), ('normal control', [[[0, 100], [0, 100], [100, 150]], 200], [])], [('regression: backward walk pruning', [[[0, 100], [200, 1200], [300, 400], [500, 800]], 600], [1, 3]), ('regression variant: backward walk pruning', [[[0, 300], [0, 50], [100, 200], [200, 500], [400, 500]], 100], [0, 2]), ('partial repair probe: backward walk pruning', [[[300, 600], [300, 350], [400, 700], [400, 500]], 600], [2]), ('partial repair variant: backward walk pruning', [[[0, 100], [200, 1200], [200, 250], [200, 1200], [500, 600]], 500], [1, 3, 4]), ('boundary control', [[[0, 100]], 100], []), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('normal control', [[[0, 100], [0, 100], [400, 700], [500, 1500]], 150], []), ('normal control', [[[200, 500]], 500], []), ('normal control', [[[500, 550]], 50], [])], [('regression: backward walk pruning', [[[0, 1000], [100, 150], [100, 400], [200, 1200]], 300], [0, 2, 3]), ('regression variant: backward walk pruning', [[[200, 250], [200, 1200], [300, 350], [300, 350], [400, 700]], 600], [1, 4]), ('partial repair probe: backward walk pruning', [[[100, 1100], [100, 1100], [400, 500], [400, 700], [400, 450], [500, 550]], 500], [0, 1, 3, 5]), ('partial repair variant: backward walk pruning', [[[0, 1000], [300, 350], [400, 1400]], 400], [0, 2]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 300], [100, 1100], [200, 250], [500, 550]], 100], [0, 1]), ('normal control', [[[400, 450]], 150], []), ('normal control', [[[200, 300]], 150], [])]]\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":"b90509b9f80916f424bc974bb8204e8fdde569162d0027124726327f6f9c3fa6","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport bisect\nN = 1\nobservations = []\ndef solve(cues, t):\n    starts=[c[0] for c in cues]\n    hi=bisect.bisect_right(starts,t)\n    maxend=[]\n    run=0\n    for c in cues:\n        run=max(run,c[1])\n        maxend.append(run)\n    res=[]\n    i=hi-1\n    while i>=0 and maxend[i]>t:\n        if cues[i][1]>t:\n            res.append(i)\n        i-=1\n    return sorted(res)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: backward walk pruning', [[[0, 1000], [100, 200], [300, 400]], 350], [0, 2]), ('regression variant: backward walk pruning', [[[300, 600], [300, 350], [400, 700], [400, 500]], 600], [2]), ('partial repair probe: backward walk pruning', [[[0, 100], [0, 1000], [400, 500], [500, 600], [500, 1500], [500, 550]], 600], [1, 4]), ('partial repair variant: backward walk pruning', [[[200, 250], [200, 1200], [300, 350], [300, 350], [400, 700]], 600], [1, 4]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 100], [100, 1100], [200, 300], [200, 500]], 50], [0]), ('normal control', [[[100, 400], [300, 400], [300, 400], [300, 350], [400, 500], [500, 800]], 300], [0, 1, 2, 3]), ('normal control', [[[100, 400], [300, 400], [400, 450], [400, 700], [400, 1400]], 0], [])], [('regression: backward walk pruning', [[[0, 50], [0, 500], [200, 250]], 300], [1]), ('regression variant: backward walk pruning', [[[100, 1100], [100, 1100], [400, 500], [400, 700], [400, 450], [500, 550]], 500], [0, 1, 3, 5]), ('partial repair probe: backward walk pruning', [[[0, 100], [200, 1200], [300, 400], [500, 800]], 600], [1, 3]), ('partial repair variant: backward walk pruning', [[[0, 1000], [0, 300], [100, 1100], [100, 150], [100, 150], [400, 500]], 200], [0, 1, 2]), ('boundary control', [[[0, 100]], 100], []), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('normal control', [[[0, 1000], [200, 300], [400, 450]], 200], [0, 1]), ('normal control', [[[100, 200], [200, 250]], 0], []), ('normal control', [[[0, 50], [100, 400], [200, 500], [400, 450], [500, 1500]], 1200], [4])], [('regression: backward walk pruning', [[[0, 100], [0, 1000], [400, 500], [500, 600], [500, 1500], [500, 550]], 600], [1, 4]), ('regression variant: backward walk pruning', [[[0, 300], [0, 100], [200, 1200], [200, 300], [300, 600], [300, 1300]], 400], [2, 4, 5]), ('partial repair probe: backward walk pruning', [[[0, 1000], [100, 150], [100, 400], [200, 1200]], 300], [0, 2, 3]), ('partial repair variant: backward walk pruning', [[[100, 1100], [200, 250], [400, 450]], 300], [0]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 1000], [100, 1100], [200, 500], [200, 500]], 200], [0, 1, 2, 3]), ('normal control', [[[0, 50]], 400], []), ('normal control', [[[0, 100], [0, 100], [100, 150]], 200], [])], [('regression: backward walk pruning', [[[0, 100], [200, 1200], [300, 400], [500, 800]], 600], [1, 3]), ('regression variant: backward walk pruning', [[[0, 300], [0, 50], [100, 200], [200, 500], [400, 500]], 100], [0, 2]), ('partial repair probe: backward walk pruning', [[[300, 600], [300, 350], [400, 700], [400, 500]], 600], [2]), ('partial repair variant: backward walk pruning', [[[0, 100], [200, 1200], [200, 250], [200, 1200], [500, 600]], 500], [1, 3, 4]), ('boundary control', [[[0, 100]], 100], []), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('normal control', [[[0, 100], [0, 100], [400, 700], [500, 1500]], 150], []), ('normal control', [[[200, 500]], 500], []), ('normal control', [[[500, 550]], 50], [])], [('regression: backward walk pruning', [[[0, 1000], [100, 150], [100, 400], [200, 1200]], 300], [0, 2, 3]), ('regression variant: backward walk pruning', [[[200, 250], [200, 1200], [300, 350], [300, 350], [400, 700]], 600], [1, 4]), ('partial repair probe: backward walk pruning', [[[100, 1100], [100, 1100], [400, 500], [400, 700], [400, 450], [500, 550]], 500], [0, 1, 3, 5]), ('partial repair variant: backward walk pruning', [[[0, 1000], [300, 350], [400, 1400]], 400], [0, 2]), ('boundary control', [[[0, 100], [100, 200]], 100], [1]), ('boundary control', [[[0, 100]], 100], []), ('normal control', [[[0, 300], [100, 1100], [200, 250], [500, 550]], 100], [0, 1]), ('normal control', [[[400, 450]], 150], []), ('normal control', [[[200, 300]], 150], [])]]\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 bounded teaching model with a stipulated toy contract; it does not claim conformance to any subtitle standard. 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-subtitle-cue-timing-active-cue-lookup-backward-walk-pruning","generated_at":"2026-09-29T14:49:34.030820+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Subtitle timing defects shift, hide or overlap captions that viewers depend on for comprehension and accessibility.","repair":"Continue while the prefix maximum end exceeds t.","root_cause":"The walk stops at the first inactive cue instead of using the prefix maximum end.","sha256":"faa287690b08a90232cce41ad7067190f715737341bcc43b65b9c565120313bb","title":"Active cue lookup with prefix maximum ends: backward walk pruning · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":44.558,"exit_code":1,"observations":[{"actual":[2],"check":"regression: backward walk pruning","expected":[0,2],"passed":false},{"actual":[],"check":"regression variant: backward walk pruning","expected":[2],"passed":false},{"actual":[],"check":"partial repair probe: backward walk pruning","expected":[1,4],"passed":false},{"actual":[4],"check":"partial repair variant: backward walk pruning","expected":[1,4],"passed":false},{"actual":[1],"check":"boundary control","expected":[1],"passed":true},{"actual":[],"check":"boundary control","expected":[],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true},{"actual":[0,1,2,3],"check":"normal control","expected":[0,1,2,3],"passed":true},{"actual":[],"check":"normal control","expected":[],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: backward walk pruning\", \"actual\": [2], \"expected\": [0, 2], \"passed\": false}, {\"check\": \"regression variant: backward walk pruning\", \"actual\": [], \"expected\": [2], \"passed\": false}, {\"check\": \"partial repair probe: backward walk pruning\", \"actual\": [], \"expected\": [1, 4], \"passed\": false}, {\"check\": \"partial repair variant: backward walk pruning\", \"actual\": [4], \"expected\": [1, 4], \"passed\": false}, {\"check\": \"boundary control\", \"actual\": [1], \"expected\": [1], \"passed\": true}, {\"check\": \"boundary control\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0, 1, 2, 3], \"expected\": [0, 1, 2, 3], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [], \"expected\": [], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":42.598,"exit_code":1,"observations":[{"actual":[2],"check":"regression: backward walk pruning","expected":[0,2],"passed":false},{"actual":[],"check":"regression variant: backward walk pruning","expected":[2],"passed":false},{"actual":[],"check":"partial repair probe: backward walk pruning","expected":[1,4],"passed":false},{"actual":[4],"check":"partial repair variant: backward walk pruning","expected":[1,4],"passed":false},{"actual":[1],"check":"boundary control","expected":[1],"passed":true},{"actual":[],"check":"boundary control","expected":[],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true},{"actual":[0,1,2,3],"check":"normal control","expected":[0,1,2,3],"passed":true},{"actual":[],"check":"normal control","expected":[],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: backward walk pruning\", \"actual\": [2], \"expected\": [0, 2], \"passed\": false}, {\"check\": \"regression variant: backward walk pruning\", \"actual\": [], \"expected\": [2], \"passed\": false}, {\"check\": \"partial repair probe: backward walk pruning\", \"actual\": [], \"expected\": [1, 4], \"passed\": false}, {\"check\": \"partial repair variant: backward walk pruning\", \"actual\": [4], \"expected\": [1, 4], \"passed\": false}, {\"check\": \"boundary control\", \"actual\": [1], \"expected\": [1], \"passed\": true}, {\"check\": \"boundary control\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0, 1, 2, 3], \"expected\": [0, 1, 2, 3], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [], \"expected\": [], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":44.295,"exit_code":0,"observations":[{"actual":[0,2],"check":"regression: backward walk pruning","expected":[0,2],"passed":true},{"actual":[2],"check":"regression variant: backward walk pruning","expected":[2],"passed":true},{"actual":[1,4],"check":"partial repair probe: backward walk pruning","expected":[1,4],"passed":true},{"actual":[1,4],"check":"partial repair variant: backward walk pruning","expected":[1,4],"passed":true},{"actual":[1],"check":"boundary control","expected":[1],"passed":true},{"actual":[],"check":"boundary control","expected":[],"passed":true},{"actual":[0],"check":"normal control","expected":[0],"passed":true},{"actual":[0,1,2,3],"check":"normal control","expected":[0,1,2,3],"passed":true},{"actual":[],"check":"normal control","expected":[],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: backward walk pruning\", \"actual\": [0, 2], \"expected\": [0, 2], \"passed\": true}, {\"check\": \"regression variant: backward walk pruning\", \"actual\": [2], \"expected\": [2], \"passed\": true}, {\"check\": \"partial repair probe: backward walk pruning\", \"actual\": [1, 4], \"expected\": [1, 4], \"passed\": true}, {\"check\": \"partial repair variant: backward walk pruning\", \"actual\": [1, 4], \"expected\": [1, 4], \"passed\": true}, {\"check\": \"boundary control\", \"actual\": [1], \"expected\": [1], \"passed\": true}, {\"check\": \"boundary control\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0], \"expected\": [0], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [0, 1, 2, 3], \"expected\": [0, 1, 2, 3], \"passed\": true}, {\"check\": \"normal control\", \"actual\": [], \"expected\": [], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}