{"abstract":"Clean prices exceed dirty prices.","category":"Bond day-count conventions","checks":8,"contract":"Inputs issue, maturity and settlement dates, coupon and yield (decimals). The instrument pays 100*(1 + coupon*T/360) at maturity where T = days(issue, maturity). Dirty = redemption/(1 + y*R/360) with R = days(settle, maturity); accrued = 100*coupon*A/360 with A = days(issue, settle). Return [dirty, dirty-accrued] rounded to 6.","evaluation_group":"w2-bond_day_count_conventions-cd-interest-at-maturity","failed_approach":"Discounting accrued before subtracting mixes present and nominal amounts.","family":"w2-bond_day_count_conventions-cd-interest-at-maturity-clean-price-derivation","id":"FA-61256","implementations":{"attempt":{"sha256":"072fde944986588192f6512b14f5ff46f66cb37f1ad629bfe4a99f63d6414cd1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nN = 1\nobservations = []\ndef solve(issue, maturity, settle, coupon, y):\n    I = datetime.date(*issue)\n    M = datetime.date(*maturity)\n    S = datetime.date(*settle)\n    T = (M - I).days\n    R = (M - S).days\n    A = (S - I).days\n    redemption = 100 * (1 + coupon * T / 360)\n    dirty = redemption / (1 + y * R / 360)\n    accrued = 100 * coupon * A / 360\n    return [round(dirty, 6), round(dirty - accrued / (1 + y * R / 360), 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression clean price derivation 1', [[2010, 5, 30], [2010, 12, 7], [2010, 8, 2], 0.05, 0.03], [101.577747, 100.688858]], ['regression clean price derivation 2', [[2040, 1, 6], [2040, 4, 5], [2040, 3, 28], 0.065, 0.01], [101.602422, 100.121866]], ['partial repair probe 1', [[2030, 7, 31], [2031, 8, 4], [2030, 12, 3], 0.05, 0.09], [99.081056, 97.344944]], ['partial repair probe 2', [[2022, 6, 30], [2022, 9, 2], [2022, 7, 16], 0.0375, 0.09], [99.472991, 99.306324]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2019, 1, 11], [2019, 12, 22], [2019, 9, 2], 0.0, 0.03], [99.083478, 99.083478]], ['normal control 2', [[2011, 6, 26], [2012, 3, 11], [2011, 9, 14], 0.0, 0.045], [97.811468, 97.811468]], ['normal control 3', [[2015, 9, 30], [2016, 8, 26], [2015, 12, 13], 0.0, 0.06], [95.8926, 95.8926]]], [['regression clean price derivation 1', [[2035, 7, 28], [2036, 3, 28], [2036, 3, 10], 0.065, 0.09], [103.937835, 99.85728]], ['regression clean price derivation 2', [[2019, 11, 20], [2020, 3, 24], [2020, 1, 11], 0.0375, 0.09], [99.486456, 98.944789]], ['partial repair probe 1', [[2020, 11, 4], [2021, 11, 26], [2021, 8, 4], 0.0375, 0.09], [101.148517, 98.304767]], ['partial repair probe 2', [[2025, 5, 24], [2026, 7, 8], [2026, 4, 29], 0.0375, 0.09], [102.477477, 98.935811]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2026, 9, 26], [2028, 6, 12], [2028, 6, 11], 0.0, 0.045], [99.987502, 99.987502]], ['normal control 2', [[2021, 2, 20], [2023, 1, 9], [2021, 7, 11], 0.0, 0.045], [93.600094, 93.600094]], ['normal control 3', [[2015, 3, 18], [2015, 10, 12], [2015, 4, 10], 0.0, 0.06], [97.008892, 97.008892]]], [['regression clean price derivation 1', [[2018, 1, 30], [2018, 12, 27], [2018, 10, 23], 0.065, 0.01], [105.785387, 100.98261]], ['regression clean price derivation 2', [[2021, 2, 16], [2021, 6, 9], [2021, 3, 4], 0.065, 0.01], [101.766075, 101.477186]], ['partial repair probe 1', [[2016, 7, 31], [2018, 6, 4], [2017, 10, 13], 0.02, 0.045], [100.790759, 98.35187]], ['partial repair probe 2', [[2030, 10, 7], [2031, 12, 3], [2031, 8, 31], 0.02, 0.01], [102.077908, 100.255685]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2030, 7, 29], [2031, 7, 21], [2031, 5, 21], 0.0, 0.06], [98.993565, 98.993565]], ['normal control 2', [[2038, 5, 1], [2039, 3, 24], [2039, 3, 19], 0.0, 0.01], [99.986113, 99.986113]], ['normal control 3', [[2020, 9, 30], [2021, 9, 18], [2020, 12, 24], 0.0, 0.045], [96.758587, 96.758587]]], [['regression clean price derivation 1', [[2021, 7, 27], [2022, 5, 27], [2021, 11, 10], 0.05, 0.01], [103.652135, 102.179913]], ['regression clean price derivation 2', [[2015, 5, 31], [2017, 3, 15], [2016, 5, 21], 0.05, 0.06], [103.92188, 98.977436]], ['partial repair probe 1', [[2035, 10, 18], [2036, 3, 3], [2035, 11, 9], 0.02, 0.01], [100.44026, 100.318038]], ['partial repair probe 2', [[2029, 1, 6], [2030, 1, 13], [2029, 11, 27], 0.05, 0.045], [104.552421, 100.038532]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2036, 9, 30], [2037, 10, 4], [2037, 3, 27], 0.0, 0.01], [99.472244, 99.472244]], ['normal control 2', [[2023, 1, 31], [2023, 11, 21], [2023, 9, 12], 0.0, 0.01], [99.805933, 99.805933]], ['normal control 3', [[2037, 12, 22], [2038, 12, 29], [2038, 11, 14], 0.0, 0.03], [99.626401, 99.626401]]], [['regression clean price derivation 1', [[2024, 12, 30], [2025, 8, 22], [2025, 3, 12], 0.02, 0.06], [98.62621, 98.22621]], ['regression clean price derivation 2', [[2023, 6, 28], [2025, 1, 13], [2024, 3, 13], 0.05, 0.01], [106.938247, 103.341025]], ['partial repair probe 1', [[2026, 10, 12], [2027, 9, 21], [2026, 11, 10], 0.065, 0.06], [100.91317, 100.389559]], ['partial repair probe 2', [[2028, 2, 29], [2029, 3, 19], [2028, 3, 12], 0.0375, 0.01], [102.936325, 102.811325]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2019, 9, 17], [2021, 1, 6], [2020, 4, 29], 0.0, 0.03], [97.943193, 97.943193]], ['normal control 2', [[2019, 10, 31], [2020, 5, 13], [2019, 11, 25], 0.0, 0.09], [95.923261, 95.923261]], ['normal control 3', [[2018, 9, 30], [2019, 1, 16], [2018, 11, 22], 0.0, 0.03], [99.543758, 99.543758]]]]\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":"aac59a57a93debfeff8c55b33abb0ee1399a878a614a96afa6ef00cd07c87b99","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nN = 1\nobservations = []\ndef solve(issue, maturity, settle, coupon, y):\n    I = datetime.date(*issue)\n    M = datetime.date(*maturity)\n    S = datetime.date(*settle)\n    T = (M - I).days\n    R = (M - S).days\n    A = (S - I).days\n    redemption = 100 * (1 + coupon * T / 360)\n    dirty = redemption / (1 + y * R / 360)\n    accrued = 100 * coupon * A / 360\n    return [round(dirty, 6), round(dirty + accrued, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression clean price derivation 1', [[2010, 5, 30], [2010, 12, 7], [2010, 8, 2], 0.05, 0.03], [101.577747, 100.688858]], ['regression clean price derivation 2', [[2040, 1, 6], [2040, 4, 5], [2040, 3, 28], 0.065, 0.01], [101.602422, 100.121866]], ['partial repair probe 1', [[2030, 7, 31], [2031, 8, 4], [2030, 12, 3], 0.05, 0.09], [99.081056, 97.344944]], ['partial repair probe 2', [[2022, 6, 30], [2022, 9, 2], [2022, 7, 16], 0.0375, 0.09], [99.472991, 99.306324]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2019, 1, 11], [2019, 12, 22], [2019, 9, 2], 0.0, 0.03], [99.083478, 99.083478]], ['normal control 2', [[2011, 6, 26], [2012, 3, 11], [2011, 9, 14], 0.0, 0.045], [97.811468, 97.811468]], ['normal control 3', [[2015, 9, 30], [2016, 8, 26], [2015, 12, 13], 0.0, 0.06], [95.8926, 95.8926]]], [['regression clean price derivation 1', [[2035, 7, 28], [2036, 3, 28], [2036, 3, 10], 0.065, 0.09], [103.937835, 99.85728]], ['regression clean price derivation 2', [[2019, 11, 20], [2020, 3, 24], [2020, 1, 11], 0.0375, 0.09], [99.486456, 98.944789]], ['partial repair probe 1', [[2020, 11, 4], [2021, 11, 26], [2021, 8, 4], 0.0375, 0.09], [101.148517, 98.304767]], ['partial repair probe 2', [[2025, 5, 24], [2026, 7, 8], [2026, 4, 29], 0.0375, 0.09], [102.477477, 98.935811]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2026, 9, 26], [2028, 6, 12], [2028, 6, 11], 0.0, 0.045], [99.987502, 99.987502]], ['normal control 2', [[2021, 2, 20], [2023, 1, 9], [2021, 7, 11], 0.0, 0.045], [93.600094, 93.600094]], ['normal control 3', [[2015, 3, 18], [2015, 10, 12], [2015, 4, 10], 0.0, 0.06], [97.008892, 97.008892]]], [['regression clean price derivation 1', [[2018, 1, 30], [2018, 12, 27], [2018, 10, 23], 0.065, 0.01], [105.785387, 100.98261]], ['regression clean price derivation 2', [[2021, 2, 16], [2021, 6, 9], [2021, 3, 4], 0.065, 0.01], [101.766075, 101.477186]], ['partial repair probe 1', [[2016, 7, 31], [2018, 6, 4], [2017, 10, 13], 0.02, 0.045], [100.790759, 98.35187]], ['partial repair probe 2', [[2030, 10, 7], [2031, 12, 3], [2031, 8, 31], 0.02, 0.01], [102.077908, 100.255685]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2030, 7, 29], [2031, 7, 21], [2031, 5, 21], 0.0, 0.06], [98.993565, 98.993565]], ['normal control 2', [[2038, 5, 1], [2039, 3, 24], [2039, 3, 19], 0.0, 0.01], [99.986113, 99.986113]], ['normal control 3', [[2020, 9, 30], [2021, 9, 18], [2020, 12, 24], 0.0, 0.045], [96.758587, 96.758587]]], [['regression clean price derivation 1', [[2021, 7, 27], [2022, 5, 27], [2021, 11, 10], 0.05, 0.01], [103.652135, 102.179913]], ['regression clean price derivation 2', [[2015, 5, 31], [2017, 3, 15], [2016, 5, 21], 0.05, 0.06], [103.92188, 98.977436]], ['partial repair probe 1', [[2035, 10, 18], [2036, 3, 3], [2035, 11, 9], 0.02, 0.01], [100.44026, 100.318038]], ['partial repair probe 2', [[2029, 1, 6], [2030, 1, 13], [2029, 11, 27], 0.05, 0.045], [104.552421, 100.038532]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2036, 9, 30], [2037, 10, 4], [2037, 3, 27], 0.0, 0.01], [99.472244, 99.472244]], ['normal control 2', [[2023, 1, 31], [2023, 11, 21], [2023, 9, 12], 0.0, 0.01], [99.805933, 99.805933]], ['normal control 3', [[2037, 12, 22], [2038, 12, 29], [2038, 11, 14], 0.0, 0.03], [99.626401, 99.626401]]], [['regression clean price derivation 1', [[2024, 12, 30], [2025, 8, 22], [2025, 3, 12], 0.02, 0.06], [98.62621, 98.22621]], ['regression clean price derivation 2', [[2023, 6, 28], [2025, 1, 13], [2024, 3, 13], 0.05, 0.01], [106.938247, 103.341025]], ['partial repair probe 1', [[2026, 10, 12], [2027, 9, 21], [2026, 11, 10], 0.065, 0.06], [100.91317, 100.389559]], ['partial repair probe 2', [[2028, 2, 29], [2029, 3, 19], [2028, 3, 12], 0.0375, 0.01], [102.936325, 102.811325]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2019, 9, 17], [2021, 1, 6], [2020, 4, 29], 0.0, 0.03], [97.943193, 97.943193]], ['normal control 2', [[2019, 10, 31], [2020, 5, 13], [2019, 11, 25], 0.0, 0.09], [95.923261, 95.923261]], ['normal control 3', [[2018, 9, 30], [2019, 1, 16], [2018, 11, 22], 0.0, 0.03], [99.543758, 99.543758]]]]\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":"64967027a1a77cba76f735bb1a557727f241e461b574e45bc0aed403cdd9dd81","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nN = 1\nobservations = []\ndef solve(issue, maturity, settle, coupon, y):\n    I = datetime.date(*issue)\n    M = datetime.date(*maturity)\n    S = datetime.date(*settle)\n    T = (M - I).days\n    R = (M - S).days\n    A = (S - I).days\n    redemption = 100 * (1 + coupon * T / 360)\n    dirty = redemption / (1 + y * R / 360)\n    accrued = 100 * coupon * A / 360\n    return [round(dirty, 6), round(dirty - accrued, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression clean price derivation 1', [[2010, 5, 30], [2010, 12, 7], [2010, 8, 2], 0.05, 0.03], [101.577747, 100.688858]], ['regression clean price derivation 2', [[2040, 1, 6], [2040, 4, 5], [2040, 3, 28], 0.065, 0.01], [101.602422, 100.121866]], ['partial repair probe 1', [[2030, 7, 31], [2031, 8, 4], [2030, 12, 3], 0.05, 0.09], [99.081056, 97.344944]], ['partial repair probe 2', [[2022, 6, 30], [2022, 9, 2], [2022, 7, 16], 0.0375, 0.09], [99.472991, 99.306324]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2019, 1, 11], [2019, 12, 22], [2019, 9, 2], 0.0, 0.03], [99.083478, 99.083478]], ['normal control 2', [[2011, 6, 26], [2012, 3, 11], [2011, 9, 14], 0.0, 0.045], [97.811468, 97.811468]], ['normal control 3', [[2015, 9, 30], [2016, 8, 26], [2015, 12, 13], 0.0, 0.06], [95.8926, 95.8926]]], [['regression clean price derivation 1', [[2035, 7, 28], [2036, 3, 28], [2036, 3, 10], 0.065, 0.09], [103.937835, 99.85728]], ['regression clean price derivation 2', [[2019, 11, 20], [2020, 3, 24], [2020, 1, 11], 0.0375, 0.09], [99.486456, 98.944789]], ['partial repair probe 1', [[2020, 11, 4], [2021, 11, 26], [2021, 8, 4], 0.0375, 0.09], [101.148517, 98.304767]], ['partial repair probe 2', [[2025, 5, 24], [2026, 7, 8], [2026, 4, 29], 0.0375, 0.09], [102.477477, 98.935811]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2026, 9, 26], [2028, 6, 12], [2028, 6, 11], 0.0, 0.045], [99.987502, 99.987502]], ['normal control 2', [[2021, 2, 20], [2023, 1, 9], [2021, 7, 11], 0.0, 0.045], [93.600094, 93.600094]], ['normal control 3', [[2015, 3, 18], [2015, 10, 12], [2015, 4, 10], 0.0, 0.06], [97.008892, 97.008892]]], [['regression clean price derivation 1', [[2018, 1, 30], [2018, 12, 27], [2018, 10, 23], 0.065, 0.01], [105.785387, 100.98261]], ['regression clean price derivation 2', [[2021, 2, 16], [2021, 6, 9], [2021, 3, 4], 0.065, 0.01], [101.766075, 101.477186]], ['partial repair probe 1', [[2016, 7, 31], [2018, 6, 4], [2017, 10, 13], 0.02, 0.045], [100.790759, 98.35187]], ['partial repair probe 2', [[2030, 10, 7], [2031, 12, 3], [2031, 8, 31], 0.02, 0.01], [102.077908, 100.255685]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2030, 7, 29], [2031, 7, 21], [2031, 5, 21], 0.0, 0.06], [98.993565, 98.993565]], ['normal control 2', [[2038, 5, 1], [2039, 3, 24], [2039, 3, 19], 0.0, 0.01], [99.986113, 99.986113]], ['normal control 3', [[2020, 9, 30], [2021, 9, 18], [2020, 12, 24], 0.0, 0.045], [96.758587, 96.758587]]], [['regression clean price derivation 1', [[2021, 7, 27], [2022, 5, 27], [2021, 11, 10], 0.05, 0.01], [103.652135, 102.179913]], ['regression clean price derivation 2', [[2015, 5, 31], [2017, 3, 15], [2016, 5, 21], 0.05, 0.06], [103.92188, 98.977436]], ['partial repair probe 1', [[2035, 10, 18], [2036, 3, 3], [2035, 11, 9], 0.02, 0.01], [100.44026, 100.318038]], ['partial repair probe 2', [[2029, 1, 6], [2030, 1, 13], [2029, 11, 27], 0.05, 0.045], [104.552421, 100.038532]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2036, 9, 30], [2037, 10, 4], [2037, 3, 27], 0.0, 0.01], [99.472244, 99.472244]], ['normal control 2', [[2023, 1, 31], [2023, 11, 21], [2023, 9, 12], 0.0, 0.01], [99.805933, 99.805933]], ['normal control 3', [[2037, 12, 22], [2038, 12, 29], [2038, 11, 14], 0.0, 0.03], [99.626401, 99.626401]]], [['regression clean price derivation 1', [[2024, 12, 30], [2025, 8, 22], [2025, 3, 12], 0.02, 0.06], [98.62621, 98.22621]], ['regression clean price derivation 2', [[2023, 6, 28], [2025, 1, 13], [2024, 3, 13], 0.05, 0.01], [106.938247, 103.341025]], ['partial repair probe 1', [[2026, 10, 12], [2027, 9, 21], [2026, 11, 10], 0.065, 0.06], [100.91317, 100.389559]], ['partial repair probe 2', [[2028, 2, 29], [2029, 3, 19], [2028, 3, 12], 0.0375, 0.01], [102.936325, 102.811325]], ['boundary control 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2019, 9, 17], [2021, 1, 6], [2020, 4, 29], 0.0, 0.03], [97.943193, 97.943193]], ['normal control 2', [[2019, 10, 31], [2020, 5, 13], [2019, 11, 25], 0.0, 0.09], [95.923261, 95.923261]], ['normal control 3', [[2018, 9, 30], [2019, 1, 16], [2018, 11, 22], 0.0, 0.03], [99.543758, 99.543758]]]]\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 published convention text. 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-bond_day_count_conventions-cd-interest-at-maturity-clean-price-derivation","generated_at":"2026-09-29T14:46:53.543173+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.","repair":"Clean equals dirty minus accrued.","root_cause":"The clean price adds accrued instead of subtracting it.","sha256":"124d4f4b1a4dae92825a5edb83a4a0fad43c9efaf5edbc88615d26f543c02fdd","title":"Money-market certificate priced from yield: accrued interest is added to reach the clean price · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":45.57,"exit_code":1,"observations":[{"actual":[101.577747,100.698167],"check":"regression clean price derivation 1","expected":[101.577747,100.688858],"passed":false},{"actual":[101.602422,100.122195],"check":"regression clean price derivation 2","expected":[101.602422,100.121866],"passed":false},{"actual":[99.081056,97.444759],"check":"partial repair probe 1","expected":[99.081056,97.344944],"passed":false},{"actual":[99.472991,99.3083],"check":"partial repair probe 2","expected":[99.472991,99.306324],"passed":false},{"actual":[100.0,100.0],"check":"boundary control 1","expected":[100.0,100.0],"passed":true},{"actual":[99.083478,99.083478],"check":"normal control 1","expected":[99.083478,99.083478],"passed":true},{"actual":[97.811468,97.811468],"check":"normal control 2","expected":[97.811468,97.811468],"passed":true},{"actual":[95.8926,95.8926],"check":"normal control 3","expected":[95.8926,95.8926],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression clean price derivation 1\", \"actual\": [101.577747, 100.698167], \"expected\": [101.577747, 100.688858], \"passed\": false}, {\"check\": \"regression clean price derivation 2\", \"actual\": [101.602422, 100.122195], \"expected\": [101.602422, 100.121866], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [99.081056, 97.444759], \"expected\": [99.081056, 97.344944], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [99.472991, 99.3083], \"expected\": [99.472991, 99.306324], \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": [100.0, 100.0], \"expected\": [100.0, 100.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [99.083478, 99.083478], \"expected\": [99.083478, 99.083478], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [97.811468, 97.811468], \"expected\": [97.811468, 97.811468], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [95.8926, 95.8926], \"expected\": [95.8926, 95.8926], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":49.219,"exit_code":1,"observations":[{"actual":[101.577747,102.466636],"check":"regression clean price derivation 1","expected":[101.577747,100.688858],"passed":false},{"actual":[101.602422,103.082977],"check":"regression clean price derivation 2","expected":[101.602422,100.121866],"passed":false},{"actual":[99.081056,100.817167],"check":"partial repair probe 1","expected":[99.081056,97.344944],"passed":false},{"actual":[99.472991,99.639657],"check":"partial repair probe 2","expected":[99.472991,99.306324],"passed":false},{"actual":[100.0,100.0],"check":"boundary control 1","expected":[100.0,100.0],"passed":true},{"actual":[99.083478,99.083478],"check":"normal control 1","expected":[99.083478,99.083478],"passed":true},{"actual":[97.811468,97.811468],"check":"normal control 2","expected":[97.811468,97.811468],"passed":true},{"actual":[95.8926,95.8926],"check":"normal control 3","expected":[95.8926,95.8926],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression clean price derivation 1\", \"actual\": [101.577747, 102.466636], \"expected\": [101.577747, 100.688858], \"passed\": false}, {\"check\": \"regression clean price derivation 2\", \"actual\": [101.602422, 103.082977], \"expected\": [101.602422, 100.121866], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [99.081056, 100.817167], \"expected\": [99.081056, 97.344944], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [99.472991, 99.639657], \"expected\": [99.472991, 99.306324], \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": [100.0, 100.0], \"expected\": [100.0, 100.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [99.083478, 99.083478], \"expected\": [99.083478, 99.083478], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [97.811468, 97.811468], \"expected\": [97.811468, 97.811468], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [95.8926, 95.8926], \"expected\": [95.8926, 95.8926], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":44.215,"exit_code":0,"observations":[{"actual":[101.577747,100.688858],"check":"regression clean price derivation 1","expected":[101.577747,100.688858],"passed":true},{"actual":[101.602422,100.121866],"check":"regression clean price derivation 2","expected":[101.602422,100.121866],"passed":true},{"actual":[99.081056,97.344944],"check":"partial repair probe 1","expected":[99.081056,97.344944],"passed":true},{"actual":[99.472991,99.306324],"check":"partial repair probe 2","expected":[99.472991,99.306324],"passed":true},{"actual":[100.0,100.0],"check":"boundary control 1","expected":[100.0,100.0],"passed":true},{"actual":[99.083478,99.083478],"check":"normal control 1","expected":[99.083478,99.083478],"passed":true},{"actual":[97.811468,97.811468],"check":"normal control 2","expected":[97.811468,97.811468],"passed":true},{"actual":[95.8926,95.8926],"check":"normal control 3","expected":[95.8926,95.8926],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression clean price derivation 1\", \"actual\": [101.577747, 100.688858], \"expected\": [101.577747, 100.688858], \"passed\": true}, {\"check\": \"regression clean price derivation 2\", \"actual\": [101.602422, 100.121866], \"expected\": [101.602422, 100.121866], \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": [99.081056, 97.344944], \"expected\": [99.081056, 97.344944], \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": [99.472991, 99.306324], \"expected\": [99.472991, 99.306324], \"passed\": true}, {\"check\": \"boundary control 1\", \"actual\": [100.0, 100.0], \"expected\": [100.0, 100.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [99.083478, 99.083478], \"expected\": [99.083478, 99.083478], \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": [97.811468, 97.811468], \"expected\": [97.811468, 97.811468], \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": [95.8926, 95.8926], \"expected\": [95.8926, 95.8926], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}