{"abstract":"Codewords generated with roots alpha^0, alpha^1 fail the check.","category":"Error-correcting codes","checks":8,"contract":"Reed-Solomon syndromes over GF(2^8) (0x11D, alpha = 2, first consecutive root alpha^0). msg lists coefficients from the highest degree down. S_j = msg(alpha^j) for j = 0..nsym-1 by Horner evaluation. Return [syndromes, whether any syndrome is nonzero].","evaluation_group":"w2-error_correcting_codes-rs-syndromes","failed_approach":"Listing the correct roots in reverse order permutes the syndrome vector.","family":"w2-error_correcting_codes-rs-syndromes-first-root","id":"FA-71996","implementations":{"attempt":{"sha256":"82a6d8b2a2bd5fe4a0745b10bd7d53cc9fc65db724c541419cc3e4be84364967","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(msg, nsym):\n    EXP, LOG = [0] * 512, [0] * 256\n    x = 1\n    for i in range(255):\n        EXP[i] = EXP[i + 255] = x\n        LOG[x] = i\n        x <<= 1\n        if x & 0x100:\n            x ^= 0x11D\n    def mul(a, b):\n        return 0 if a == 0 or b == 0 else EXP[LOG[a] + LOG[b]]\n    \n    synd = []\n    for j in range(nsym):\n        root = EXP[nsym - 1 - j]\n        v = 0\n        for coef in msg:\n            v = mul(v, root) ^ coef\n        synd.append(v)\n    return [synd, any(synd)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression [[23, 252, 61, 84, 130], 2]', [[23, 252, 61, 84, 130], 2], [[0, 0], False]], ['regression [[23, 252, 61, 64, 130], 2]', [[23, 252, 61, 64, 130], 2], [[20, 40], True]], ['partial-repair [[23, 252, 61, 64, 130], 4]', [[23, 252, 61, 64, 130], 4], [[20, 40, 7, 141], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[176, 102, 222, 44, 125, 89], 2]', [[176, 102, 222, 44, 125, 89], 2], [[0, 0], False]], ['control [[176, 102, 222, 61, 125, 89], 2]', [[176, 102, 222, 61, 125, 89], 2], [[17, 68], True]]], [['regression [[176, 102, 222, 44, 125, 89], 2]', [[176, 102, 222, 44, 125, 89], 2], [[0, 0], False]], ['regression [[176, 102, 222, 61, 125, 89], 2]', [[176, 102, 222, 61, 125, 89], 2], [[17, 68], True]], ['partial-repair [[176, 102, 222, 61, 125, 89], 4]', [[176, 102, 222, 61, 125, 89], 4], [[17, 68, 169, 244], True]], ['partial-repair [[123, 214, 103, 174, 192, 161, 31], 2]', [[123, 214, 103, 174, 192, 161, 31], 2], [[26, 52], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[123, 214, 103, 174, 192, 161, 31], 4]', [[123, 214, 103, 174, 192, 161, 31], 4], [[26, 52, 69, 105], True]]], [['regression [[123, 214, 103, 174, 192, 187, 31], 2]', [[123, 214, 103, 174, 192, 187, 31], 2], [[0, 0], False]], ['regression [[123, 214, 103, 174, 192, 161, 31], 2]', [[123, 214, 103, 174, 192, 161, 31], 2], [[26, 52], True]], ['partial-repair [[131, 154, 183, 135, 93, 136, 102, 233], 2]', [[131, 154, 183, 135, 93, 136, 102, 233], 2], [[115, 209], True]], ['partial-repair [[131, 154, 183, 135, 93, 136, 102, 233], 4]', [[131, 154, 183, 135, 93, 136, 102, 233], 4], [[115, 209, 164, 73], True]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2], [[133, 184], True]]], [['regression [[131, 154, 183, 135, 93, 251, 102, 233], 2]', [[131, 154, 183, 135, 93, 251, 102, 233], 2], [[0, 0], False]], ['regression [[131, 154, 183, 135, 93, 136, 102, 233], 2]', [[131, 154, 183, 135, 93, 136, 102, 233], 2], [[115, 209], True]], ['partial-repair [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 4]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 4], [[133, 184, 215, 167], True]], ['partial-repair [[178, 65, 204, 132, 27, 119, 170, 156, 109, 21, 94, 12], 2]', [[178, 65, 204, 132, 27, 119, 170, 156, 109, 21, 94, 12], 2], [[203, 88], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[1, 5, 4], 3]', [[1, 5, 4], 3], [[0, 10, 0], True]]], [['regression [[70, 25, 237, 118, 90, 165, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 165, 35, 253, 93, 184], 2], [[0, 0], False]], ['regression [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2], [[133, 184], True]], ['partial-repair [[1, 2, 3], 3]', [[1, 2, 3], 3], [[0, 3, 27], True]], ['partial-repair [[1, 0, 0, 0], 3]', [[1, 0, 0, 0], 3], [[1, 8, 64], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[9, 9], 2]', [[9, 9], 2], [[0, 27], True]]]]\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":"26b5c33db5a976cf98307612321b5e9bbda412014cd038ac9c4c4e4e092c66aa","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(msg, nsym):\n    EXP, LOG = [0] * 512, [0] * 256\n    x = 1\n    for i in range(255):\n        EXP[i] = EXP[i + 255] = x\n        LOG[x] = i\n        x <<= 1\n        if x & 0x100:\n            x ^= 0x11D\n    def mul(a, b):\n        return 0 if a == 0 or b == 0 else EXP[LOG[a] + LOG[b]]\n    \n    synd = []\n    for j in range(nsym):\n        root = EXP[j + 1]\n        v = 0\n        for coef in msg:\n            v = mul(v, root) ^ coef\n        synd.append(v)\n    return [synd, any(synd)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression [[23, 252, 61, 84, 130], 2]', [[23, 252, 61, 84, 130], 2], [[0, 0], False]], ['regression [[23, 252, 61, 64, 130], 2]', [[23, 252, 61, 64, 130], 2], [[20, 40], True]], ['partial-repair [[23, 252, 61, 64, 130], 4]', [[23, 252, 61, 64, 130], 4], [[20, 40, 7, 141], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[176, 102, 222, 44, 125, 89], 2]', [[176, 102, 222, 44, 125, 89], 2], [[0, 0], False]], ['control [[176, 102, 222, 61, 125, 89], 2]', [[176, 102, 222, 61, 125, 89], 2], [[17, 68], True]]], [['regression [[176, 102, 222, 44, 125, 89], 2]', [[176, 102, 222, 44, 125, 89], 2], [[0, 0], False]], ['regression [[176, 102, 222, 61, 125, 89], 2]', [[176, 102, 222, 61, 125, 89], 2], [[17, 68], True]], ['partial-repair [[176, 102, 222, 61, 125, 89], 4]', [[176, 102, 222, 61, 125, 89], 4], [[17, 68, 169, 244], True]], ['partial-repair [[123, 214, 103, 174, 192, 161, 31], 2]', [[123, 214, 103, 174, 192, 161, 31], 2], [[26, 52], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[123, 214, 103, 174, 192, 161, 31], 4]', [[123, 214, 103, 174, 192, 161, 31], 4], [[26, 52, 69, 105], True]]], [['regression [[123, 214, 103, 174, 192, 187, 31], 2]', [[123, 214, 103, 174, 192, 187, 31], 2], [[0, 0], False]], ['regression [[123, 214, 103, 174, 192, 161, 31], 2]', [[123, 214, 103, 174, 192, 161, 31], 2], [[26, 52], True]], ['partial-repair [[131, 154, 183, 135, 93, 136, 102, 233], 2]', [[131, 154, 183, 135, 93, 136, 102, 233], 2], [[115, 209], True]], ['partial-repair [[131, 154, 183, 135, 93, 136, 102, 233], 4]', [[131, 154, 183, 135, 93, 136, 102, 233], 4], [[115, 209, 164, 73], True]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2], [[133, 184], True]]], [['regression [[131, 154, 183, 135, 93, 251, 102, 233], 2]', [[131, 154, 183, 135, 93, 251, 102, 233], 2], [[0, 0], False]], ['regression [[131, 154, 183, 135, 93, 136, 102, 233], 2]', [[131, 154, 183, 135, 93, 136, 102, 233], 2], [[115, 209], True]], ['partial-repair [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 4]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 4], [[133, 184, 215, 167], True]], ['partial-repair [[178, 65, 204, 132, 27, 119, 170, 156, 109, 21, 94, 12], 2]', [[178, 65, 204, 132, 27, 119, 170, 156, 109, 21, 94, 12], 2], [[203, 88], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[1, 5, 4], 3]', [[1, 5, 4], 3], [[0, 10, 0], True]]], [['regression [[70, 25, 237, 118, 90, 165, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 165, 35, 253, 93, 184], 2], [[0, 0], False]], ['regression [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2], [[133, 184], True]], ['partial-repair [[1, 2, 3], 3]', [[1, 2, 3], 3], [[0, 3, 27], True]], ['partial-repair [[1, 0, 0, 0], 3]', [[1, 0, 0, 0], 3], [[1, 8, 64], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[9, 9], 2]', [[9, 9], 2], [[0, 27], True]]]]\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":"d74f96165ae9080342c4f67b63bd79b458738b0d1f5f6be5f763eff1a7307810","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(msg, nsym):\n    EXP, LOG = [0] * 512, [0] * 256\n    x = 1\n    for i in range(255):\n        EXP[i] = EXP[i + 255] = x\n        LOG[x] = i\n        x <<= 1\n        if x & 0x100:\n            x ^= 0x11D\n    def mul(a, b):\n        return 0 if a == 0 or b == 0 else EXP[LOG[a] + LOG[b]]\n    \n    synd = []\n    for j in range(nsym):\n        root = EXP[j]\n        v = 0\n        for coef in msg:\n            v = mul(v, root) ^ coef\n        synd.append(v)\n    return [synd, any(synd)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression [[23, 252, 61, 84, 130], 2]', [[23, 252, 61, 84, 130], 2], [[0, 0], False]], ['regression [[23, 252, 61, 64, 130], 2]', [[23, 252, 61, 64, 130], 2], [[20, 40], True]], ['partial-repair [[23, 252, 61, 64, 130], 4]', [[23, 252, 61, 64, 130], 4], [[20, 40, 7, 141], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[176, 102, 222, 44, 125, 89], 2]', [[176, 102, 222, 44, 125, 89], 2], [[0, 0], False]], ['control [[176, 102, 222, 61, 125, 89], 2]', [[176, 102, 222, 61, 125, 89], 2], [[17, 68], True]]], [['regression [[176, 102, 222, 44, 125, 89], 2]', [[176, 102, 222, 44, 125, 89], 2], [[0, 0], False]], ['regression [[176, 102, 222, 61, 125, 89], 2]', [[176, 102, 222, 61, 125, 89], 2], [[17, 68], True]], ['partial-repair [[176, 102, 222, 61, 125, 89], 4]', [[176, 102, 222, 61, 125, 89], 4], [[17, 68, 169, 244], True]], ['partial-repair [[123, 214, 103, 174, 192, 161, 31], 2]', [[123, 214, 103, 174, 192, 161, 31], 2], [[26, 52], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[123, 214, 103, 174, 192, 161, 31], 4]', [[123, 214, 103, 174, 192, 161, 31], 4], [[26, 52, 69, 105], True]]], [['regression [[123, 214, 103, 174, 192, 187, 31], 2]', [[123, 214, 103, 174, 192, 187, 31], 2], [[0, 0], False]], ['regression [[123, 214, 103, 174, 192, 161, 31], 2]', [[123, 214, 103, 174, 192, 161, 31], 2], [[26, 52], True]], ['partial-repair [[131, 154, 183, 135, 93, 136, 102, 233], 2]', [[131, 154, 183, 135, 93, 136, 102, 233], 2], [[115, 209], True]], ['partial-repair [[131, 154, 183, 135, 93, 136, 102, 233], 4]', [[131, 154, 183, 135, 93, 136, 102, 233], 4], [[115, 209, 164, 73], True]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2], [[133, 184], True]]], [['regression [[131, 154, 183, 135, 93, 251, 102, 233], 2]', [[131, 154, 183, 135, 93, 251, 102, 233], 2], [[0, 0], False]], ['regression [[131, 154, 183, 135, 93, 136, 102, 233], 2]', [[131, 154, 183, 135, 93, 136, 102, 233], 2], [[115, 209], True]], ['partial-repair [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 4]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 4], [[133, 184, 215, 167], True]], ['partial-repair [[178, 65, 204, 132, 27, 119, 170, 156, 109, 21, 94, 12], 2]', [[178, 65, 204, 132, 27, 119, 170, 156, 109, 21, 94, 12], 2], [[203, 88], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[1, 5, 4], 3]', [[1, 5, 4], 3], [[0, 10, 0], True]]], [['regression [[70, 25, 237, 118, 90, 165, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 165, 35, 253, 93, 184], 2], [[0, 0], False]], ['regression [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2]', [[70, 25, 237, 118, 90, 32, 35, 253, 93, 184], 2], [[133, 184], True]], ['partial-repair [[1, 2, 3], 3]', [[1, 2, 3], 3], [[0, 3, 27], True]], ['partial-repair [[1, 0, 0, 0], 3]', [[1, 0, 0, 0], 3], [[1, 8, 64], True]], ['control [[0, 0, 7], 2]', [[0, 0, 7], 2], [[7, 7], True]], ['control [[0, 0, 0, 0], 2]', [[0, 0, 0, 0], 2], [[0, 0], False]], ['control [[5], 1]', [[5], 1], [[5], True]], ['control [[9, 9], 2]', [[9, 9], 2], [[0, 27], True]]]]\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 of the named code under the stated contract; not a production codec. 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-error_correcting_codes-rs-syndromes-first-root","generated_at":"2026-09-29T14:48:34.638772+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Storage and broadcast decoders start every Reed-Solomon block with a syndrome check.","repair":"Evaluate S_j at alpha^j (first consecutive root 0).","root_cause":"Syndromes are evaluated at alpha^(j+1), the fcr = 1 convention.","sha256":"06951c065f3d8d42904b82e296d6c8d01b98cd2c20d14d743153955b2185c0a6","title":"RS syndromes start at alpha to the first power · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":38.625,"exit_code":1,"observations":[{"actual":[[0,0],false],"check":"regression [[23, 252, 61, 84, 130], 2]","expected":[[0,0],false],"passed":true},{"actual":[[40,20],true],"check":"regression [[23, 252, 61, 64, 130], 2]","expected":[[20,40],true],"passed":false},{"actual":[[141,7,40,20],true],"check":"partial-repair [[23, 252, 61, 64, 130], 4]","expected":[[20,40,7,141],true],"passed":false},{"actual":[[0,0],false],"check":"control [[0, 0, 0, 0], 2]","expected":[[0,0],false],"passed":true},{"actual":[[5],true],"check":"control [[5], 1]","expected":[[5],true],"passed":true},{"actual":[[7,7],true],"check":"control [[0, 0, 7], 2]","expected":[[7,7],true],"passed":true},{"actual":[[0,0],false],"check":"control [[176, 102, 222, 44, 125, 89], 2]","expected":[[0,0],false],"passed":true},{"actual":[[68,17],true],"check":"control [[176, 102, 222, 61, 125, 89], 2]","expected":[[17,68],true],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression [[23, 252, 61, 84, 130], 2]\", \"actual\": [[0, 0], false], \"expected\": [[0, 0], false], \"passed\": true}, {\"check\": \"regression [[23, 252, 61, 64, 130], 2]\", \"actual\": [[40, 20], true], \"expected\": [[20, 40], true], \"passed\": false}, {\"check\": \"partial-repair [[23, 252, 61, 64, 130], 4]\", \"actual\": [[141, 7, 40, 20], true], \"expected\": [[20, 40, 7, 141], true], \"passed\": false}, {\"check\": \"control [[0, 0, 0, 0], 2]\", \"actual\": [[0, 0], false], \"expected\": [[0, 0], false], \"passed\": true}, {\"check\": \"control [[5], 1]\", \"actual\": [[5], true], \"expected\": [[5], true], \"passed\": true}, {\"check\": \"control [[0, 0, 7], 2]\", \"actual\": [[7, 7], true], \"expected\": [[7, 7], true], \"passed\": true}, {\"check\": \"control [[176, 102, 222, 44, 125, 89], 2]\", \"actual\": [[0, 0], false], \"expected\": [[0, 0], false], \"passed\": true}, {\"check\": \"control [[176, 102, 222, 61, 125, 89], 2]\", \"actual\": [[68, 17], true], \"expected\": [[17, 68], true], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.846,"exit_code":1,"observations":[{"actual":[[0,87],true],"check":"regression [[23, 252, 61, 84, 130], 2]","expected":[[0,0],false],"passed":false},{"actual":[[40,7],true],"check":"regression [[23, 252, 61, 64, 130], 2]","expected":[[20,40],true],"passed":false},{"actual":[[40,7,141,230],true],"check":"partial-repair [[23, 252, 61, 64, 130], 4]","expected":[[20,40,7,141],true],"passed":false},{"actual":[[0,0],false],"check":"control [[0, 0, 0, 0], 2]","expected":[[0,0],false],"passed":true},{"actual":[[5],true],"check":"control [[5], 1]","expected":[[5],true],"passed":true},{"actual":[[7,7],true],"check":"control [[0, 0, 7], 2]","expected":[[7,7],true],"passed":true},{"actual":[[0,164],true],"check":"control [[176, 102, 222, 44, 125, 89], 2]","expected":[[0,0],false],"passed":false},{"actual":[[68,169],true],"check":"control [[176, 102, 222, 61, 125, 89], 2]","expected":[[17,68],true],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression [[23, 252, 61, 84, 130], 2]\", \"actual\": [[0, 87], true], \"expected\": [[0, 0], false], \"passed\": false}, {\"check\": \"regression [[23, 252, 61, 64, 130], 2]\", \"actual\": [[40, 7], true], \"expected\": [[20, 40], true], \"passed\": false}, {\"check\": \"partial-repair [[23, 252, 61, 64, 130], 4]\", \"actual\": [[40, 7, 141, 230], true], \"expected\": [[20, 40, 7, 141], true], \"passed\": false}, {\"check\": \"control [[0, 0, 0, 0], 2]\", \"actual\": [[0, 0], false], \"expected\": [[0, 0], false], \"passed\": true}, {\"check\": \"control [[5], 1]\", \"actual\": [[5], true], \"expected\": [[5], true], \"passed\": true}, {\"check\": \"control [[0, 0, 7], 2]\", \"actual\": [[7, 7], true], \"expected\": [[7, 7], true], \"passed\": true}, {\"check\": \"control [[176, 102, 222, 44, 125, 89], 2]\", \"actual\": [[0, 164], true], \"expected\": [[0, 0], false], \"passed\": false}, {\"check\": \"control [[176, 102, 222, 61, 125, 89], 2]\", \"actual\": [[68, 169], true], \"expected\": [[17, 68], true], \"passed\": false}], \"passed\": false}\n"},"fixed":{"elapsed_ms":38.212,"exit_code":0,"observations":[{"actual":[[0,0],false],"check":"regression [[23, 252, 61, 84, 130], 2]","expected":[[0,0],false],"passed":true},{"actual":[[20,40],true],"check":"regression [[23, 252, 61, 64, 130], 2]","expected":[[20,40],true],"passed":true},{"actual":[[20,40,7,141],true],"check":"partial-repair [[23, 252, 61, 64, 130], 4]","expected":[[20,40,7,141],true],"passed":true},{"actual":[[0,0],false],"check":"control [[0, 0, 0, 0], 2]","expected":[[0,0],false],"passed":true},{"actual":[[5],true],"check":"control [[5], 1]","expected":[[5],true],"passed":true},{"actual":[[7,7],true],"check":"control [[0, 0, 7], 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{\"check\": \"control [[0, 0, 7], 2]\", \"actual\": [[7, 7], true], \"expected\": [[7, 7], true], \"passed\": true}, {\"check\": \"control [[176, 102, 222, 44, 125, 89], 2]\", \"actual\": [[0, 0], false], \"expected\": [[0, 0], false], \"passed\": true}, {\"check\": \"control [[176, 102, 222, 61, 125, 89], 2]\", \"actual\": [[17, 68], true], \"expected\": [[17, 68], true], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}