{"abstract":"Holders who instructed not to exercise are exercised automatically.","category":"Options payoff and settlement","checks":8,"contract":"Inputs kind C/P, strike, settlement price, signed contracts (+long, -short), multiplier, a minimum in-the-money amount and a long-holder instruction (auto, exercise, dnx). Work in integer cents. Intrinsic is max(0, S-K) for calls, max(0, K-S) for puts. The automatic rule exercises when intrinsic >= threshold. Longs follow their instruction (exercise always exercises, dnx never, auto uses the rule); shorts are assigned by the automatic rule. Cash = intrinsic*multiplier*|contracts| if exercised, negative for shorts. Return [exercised, cash in currency units].","evaluation_group":"w2-options_payoff_and_settlement-cash-settled-expiry","failed_approach":"Upper-casing the input but comparing with the lower-case code still never matches.","family":"w2-options_payoff_and_settlement-cash-settled-expiry-contrary-instruction-match","id":"FA-61351","implementations":{"attempt":{"sha256":"9befdab8cf1dbf1c67e1327024b766c434a124d4544813f67970b7f01ab91824","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(kind, strike, settle, contracts, multiplier, min_itm, instruction):\n    k = round(strike * 100)\n    s = round(settle * 100)\n    thr = round(min_itm * 100)\n    intrinsic = max(0, s - k) if kind == 'C' else max(0, k - s)\n    auto = intrinsic >= thr\n    if contracts > 0:\n        if instruction == 'exercise':\n            ex = True\n        elif instruction.upper() == 'dnx':\n            ex = False\n        else:\n            ex = auto\n    else:\n        ex = auto\n    cash = intrinsic * multiplier * abs(contracts) if ex else 0\n    if contracts < 0:\n        cash = -cash\n    return [ex, cash / 100]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression contrary instruction match 1', ['C', 50, 51.37, 1, 50, 0.05, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['P', 100, 97.5, 10, 100, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 101.5, 113.75, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 4500, 4497.5, 10, 100, 0.05, 'dnx'], [False, 0.0]], ['boundary control 1', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['boundary control 2', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['normal control 1', ['P', 50, 50.05, 3, 100, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['P', 50, 51.37, 3, 100, 0.01, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 4500, 4500.02, 3, 10, 0.01, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 100, 105, 1, 100, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 212.5, 212.54, 10, 10, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 25, 24.99, 3, 100, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 4500, 4500, -10, 10, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['P', 100, 101.37, -10, 100, 0.01, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 25, 37.25, 10, 10, 0.05, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 50, 50.02, 1, 10, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 150, 150.1, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['C', 100, 101.37, 1, 100, 0.02, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 50, 49.99, -1, 10, 0.02, 'auto'], [False, 0.0]], ['normal control 2', ['C', 25, 24.95, 10, 50, 0.01, 'dnx'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 100, 100.04, 10, 100, 0.01, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 100, 100.1, 3, 100, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 212.5, 212.54, 1, 10, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 4500, 4497.5, 3, 10, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 101.5, 101.49, 1, 100, 0.02, 'auto'], [False, 0.0]], ['normal control 2', ['P', 150, 150.1, -10, 10, 0.05, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['P', 4500, 4499.98, 3, 100, 0.02, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 50, 50.1, 10, 100, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['P', 50, 47.5, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['C', 150, 150.04, 10, 10, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['C', 100, 99.0, 2, 100, 0.01, 'exercise'], [True, 0.0]], ['normal control 1', ['C', 101.5, 99.0, -4, 10, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['C', 150, 150.02, 3, 10, 0.05, 'auto'], [False, 0.0]]]]\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":"d7108877ffe5cedc47e16d471f4b30f8bdec83452873db5a587f56800c37ddfb","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(kind, strike, settle, contracts, multiplier, min_itm, instruction):\n    k = round(strike * 100)\n    s = round(settle * 100)\n    thr = round(min_itm * 100)\n    intrinsic = max(0, s - k) if kind == 'C' else max(0, k - s)\n    auto = intrinsic >= thr\n    if contracts > 0:\n        if instruction == 'exercise':\n            ex = True\n        elif instruction == 'DNX':\n            ex = False\n        else:\n            ex = auto\n    else:\n        ex = auto\n    cash = intrinsic * multiplier * abs(contracts) if ex else 0\n    if contracts < 0:\n        cash = -cash\n    return [ex, cash / 100]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression contrary instruction match 1', ['C', 50, 51.37, 1, 50, 0.05, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['P', 100, 97.5, 10, 100, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 101.5, 113.75, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 4500, 4497.5, 10, 100, 0.05, 'dnx'], [False, 0.0]], ['boundary control 1', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['boundary control 2', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['normal control 1', ['P', 50, 50.05, 3, 100, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['P', 50, 51.37, 3, 100, 0.01, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 4500, 4500.02, 3, 10, 0.01, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 100, 105, 1, 100, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 212.5, 212.54, 10, 10, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 25, 24.99, 3, 100, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 4500, 4500, -10, 10, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['P', 100, 101.37, -10, 100, 0.01, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 25, 37.25, 10, 10, 0.05, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 50, 50.02, 1, 10, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 150, 150.1, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['C', 100, 101.37, 1, 100, 0.02, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 50, 49.99, -1, 10, 0.02, 'auto'], [False, 0.0]], ['normal control 2', ['C', 25, 24.95, 10, 50, 0.01, 'dnx'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 100, 100.04, 10, 100, 0.01, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 100, 100.1, 3, 100, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 212.5, 212.54, 1, 10, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 4500, 4497.5, 3, 10, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 101.5, 101.49, 1, 100, 0.02, 'auto'], [False, 0.0]], ['normal control 2', ['P', 150, 150.1, -10, 10, 0.05, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['P', 4500, 4499.98, 3, 100, 0.02, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 50, 50.1, 10, 100, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['P', 50, 47.5, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['C', 150, 150.04, 10, 10, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['C', 100, 99.0, 2, 100, 0.01, 'exercise'], [True, 0.0]], ['normal control 1', ['C', 101.5, 99.0, -4, 10, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['C', 150, 150.02, 3, 10, 0.05, 'auto'], [False, 0.0]]]]\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":"c7b66a8c8f44c62539b251215784091c5aad630320fb51f11b2751a0c078d2da","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(kind, strike, settle, contracts, multiplier, min_itm, instruction):\n    k = round(strike * 100)\n    s = round(settle * 100)\n    thr = round(min_itm * 100)\n    intrinsic = max(0, s - k) if kind == 'C' else max(0, k - s)\n    auto = intrinsic >= thr\n    if contracts > 0:\n        if instruction == 'exercise':\n            ex = True\n        elif instruction == 'dnx':\n            ex = False\n        else:\n            ex = auto\n    else:\n        ex = auto\n    cash = intrinsic * multiplier * abs(contracts) if ex else 0\n    if contracts < 0:\n        cash = -cash\n    return [ex, cash / 100]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression contrary instruction match 1', ['C', 50, 51.37, 1, 50, 0.05, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['P', 100, 97.5, 10, 100, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 101.5, 113.75, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 4500, 4497.5, 10, 100, 0.05, 'dnx'], [False, 0.0]], ['boundary control 1', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['boundary control 2', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['normal control 1', ['P', 50, 50.05, 3, 100, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['P', 50, 51.37, 3, 100, 0.01, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 4500, 4500.02, 3, 10, 0.01, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 100, 105, 1, 100, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 212.5, 212.54, 10, 10, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 25, 24.99, 3, 100, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 4500, 4500, -10, 10, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['P', 100, 101.37, -10, 100, 0.01, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 25, 37.25, 10, 10, 0.05, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 50, 50.02, 1, 10, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 150, 150.1, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['C', 100, 101.37, 1, 100, 0.02, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 50, 49.99, -1, 10, 0.02, 'auto'], [False, 0.0]], ['normal control 2', ['C', 25, 24.95, 10, 50, 0.01, 'dnx'], [False, 0.0]]], [['regression contrary instruction match 1', ['C', 100, 100.04, 10, 100, 0.01, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 100, 100.1, 3, 100, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['C', 212.5, 212.54, 1, 10, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['P', 4500, 4497.5, 3, 10, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['P', 100, 100, 1, 100, 0.01, 'auto'], [False, 0.0]], ['normal control 1', ['C', 101.5, 101.49, 1, 100, 0.02, 'auto'], [False, 0.0]], ['normal control 2', ['P', 150, 150.1, -10, 10, 0.05, 'auto'], [False, 0.0]]], [['regression contrary instruction match 1', ['P', 4500, 4499.98, 3, 100, 0.02, 'dnx'], [False, 0.0]], ['regression contrary instruction match 2', ['C', 50, 50.1, 10, 100, 0.02, 'dnx'], [False, 0.0]], ['partial repair probe 1', ['P', 50, 47.5, 1, 50, 0.01, 'dnx'], [False, 0.0]], ['partial repair probe 2', ['C', 150, 150.04, 10, 10, 0.01, 'dnx'], [False, 0.0]], ['boundary control 1', ['C', 100, 100.01, 1, 100, 0.01, 'auto'], [True, 1.0]], ['boundary control 2', ['C', 100, 99.0, 2, 100, 0.01, 'exercise'], [True, 0.0]], ['normal control 1', ['C', 101.5, 99.0, -4, 10, 0.05, 'auto'], [False, 0.0]], ['normal control 2', ['C', 150, 150.02, 3, 10, 0.05, 'auto'], [False, 0.0]]]]\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 contract stated explicitly in the contract field; no claim of conformance to any exchange or clearing rulebook. 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-options_payoff_and_settlement-cash-settled-expiry-contrary-instruction-match","generated_at":"2026-09-29T14:46:54.414792+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.","repair":"Compare against the contract code dnx exactly as supplied.","root_cause":"The instruction is compared against an upper-case code while instructions arrive lower case.","sha256":"a8b7c3b95ab67dc8cf0dc560a98a90044831f6450e0f894a0d69628c0a2c5487","title":"Cash-settled expiry with exercise by exception: do-not-exercise instructions are never recognized · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":40.443,"exit_code":1,"observations":[{"actual":[true,68.5],"check":"regression contrary instruction match 1","expected":[false,0.0],"passed":false},{"actual":[true,2500.0],"check":"regression contrary instruction match 2","expected":[false,0.0],"passed":false},{"actual":[true,612.5],"check":"partial repair probe 1","expected":[false,0.0],"passed":false},{"actual":[true,2500.0],"check":"partial repair probe 2","expected":[false,0.0],"passed":false},{"actual":[false,0.0],"check":"boundary control 1","expected":[false,0.0],"passed":true},{"actual":[true,1.0],"check":"boundary control 2","expected":[true,1.0],"passed":true},{"actual":[false,0.0],"check":"normal control 1","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"normal control 2","expected":[false,0.0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression contrary instruction match 1\", \"actual\": [true, 68.5], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"regression contrary instruction match 2\", \"actual\": [true, 2500.0], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [true, 612.5], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [true, 2500.0], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": [true, 1.0], \"expected\": [true, 1.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.313,"exit_code":1,"observations":[{"actual":[true,68.5],"check":"regression contrary instruction match 1","expected":[false,0.0],"passed":false},{"actual":[true,2500.0],"check":"regression contrary instruction match 2","expected":[false,0.0],"passed":false},{"actual":[true,612.5],"check":"partial repair probe 1","expected":[false,0.0],"passed":false},{"actual":[true,2500.0],"check":"partial repair probe 2","expected":[false,0.0],"passed":false},{"actual":[false,0.0],"check":"boundary control 1","expected":[false,0.0],"passed":true},{"actual":[true,1.0],"check":"boundary control 2","expected":[true,1.0],"passed":true},{"actual":[false,0.0],"check":"normal control 1","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"normal control 2","expected":[false,0.0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression contrary instruction match 1\", \"actual\": [true, 68.5], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"regression contrary instruction match 2\", \"actual\": [true, 2500.0], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [true, 612.5], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [true, 2500.0], \"expected\": [false, 0.0], \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": [true, 1.0], \"expected\": [true, 1.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":40.885,"exit_code":0,"observations":[{"actual":[false,0.0],"check":"regression contrary instruction match 1","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"regression contrary instruction match 2","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"partial repair probe 1","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"partial repair probe 2","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"boundary control 1","expected":[false,0.0],"passed":true},{"actual":[true,1.0],"check":"boundary control 2","expected":[true,1.0],"passed":true},{"actual":[false,0.0],"check":"normal control 1","expected":[false,0.0],"passed":true},{"actual":[false,0.0],"check":"normal control 2","expected":[false,0.0],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression contrary instruction match 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"regression contrary instruction match 2\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"boundary control 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": [true, 1.0], \"expected\": [true, 1.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [false, 0.0], \"expected\": [false, 0.0], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}