{"abstract":"Discounted prices are a cent higher than quoted whenever the discount leaves a fraction.","category":"Ride-hailing fare and surge pricing","checks":6,"contract":"In a shared ride, a rider counts as matched if they shared at least min_overlap_s seconds with another rider; a match needs at least two such riders, otherwise nobody is matched. Matched riders pay solo x (100 - discount_pct)% and unmatched riders pay solo x 95%, both floored to the cent in the rider's favor. Return id -> price.","contract_signature":"riders, discount_pct, min_overlap_s","evaluation_group":"w2-ride-hailing-fare-surge-shared-ride-discount","failed_approach":"Nearest-cent rounding still rounds some prices up.","family":"w2-ride-hailing-fare-surge-shared-ride-discount-rider-favorable-rounding","id":"FA-85556","implementations":{"attempt":{"sha256":"db9afc529771dbe59129bfc326527ce8ce322cb08ae45bf7b90eda118efc0541","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(riders, discount_pct, min_overlap_s):\n    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]\n    if len(matched) < 2:\n        matched = []\n    out = {}\n    for r in riders:\n        pct = discount_pct if r['id'] in matched else 5\n        out[r['id']] = round(r['solo'] * (100 - pct) / 100)\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],\n    30, 180],\n   {'r0': 833, 'r1': 2379, 'r2': 833}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 3, 'solo': 1234}],\n    40, 180],\n   {'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}),\n  ('second regression',\n   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],\n    40, 180],\n   {'r0': 526, 'r1': 526, 'r2': 1199}),\n  ('extra case 1',\n   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],\n    25, 180],\n   {'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}),\n  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 25, 180], {'r0': 2379}),\n  ('extra case 3',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 25, 180],\n   {'r0': 1899, 'r1': 2379})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 25, 180],\n   {'r0': 657, 'r1': 1499}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],\n    30, 180],\n   {'r0': 1399, 'r1': 1753, 'r2': 613}),\n  ('second regression',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999}], 25, 180],\n   {'r0': 1172, 'r1': 1899}),\n  ('extra case 1',\n   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],\n    30, 180],\n   {'r0': 613, 'r1': 1899, 'r2': 1399, 'r3': 833}),\n  ('extra case 2',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}],\n    30, 180],\n   {'r0': 1172, 'r1': 1399, 'r2': 1753}),\n  ('extra case 3',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 30, 180],\n   {'r0': 1899, 'r1': 2379})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 30, 180],\n   {'r0': 1172, 'r1': 833}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},\n     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],\n    40, 180],\n   {'r0': 833, 'r1': 1172, 'r2': 2379, 'r3': 1899}),\n  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 40, 180], {'r0': 1899}),\n  ('normal control 1',\n   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 40, 180],\n   {'r0': 1503, 'r1': 1503}),\n  ('extra case 1', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}], 40, 180], {'r0': 1172}),\n  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 30, 180], {'r0': 2379})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1234},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}],\n    30, 180],\n   {'r0': 833, 'r1': 1172, 'r2': 2379}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 3, 'solo': 2505}],\n    40, 180],\n   {'r0': 1172, 'r1': 1899, 'r2': 1899, 'r3': 2379}),\n  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),\n  ('extra case 1',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],\n    40, 180],\n   {'r0': 833, 'r1': 1899, 'r2': 833}),\n  ('extra case 2', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 40, 180], {'r0': 1899}),\n  ('extra case 3',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 877},\n     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],\n    30, 180],\n   {'r0': 2379, 'r1': 833, 'r2': 1899, 'r3': 1899})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},\n     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],\n    25, 180],\n   {'r0': 1899, 'r1': 1172, 'r2': 2379}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}],\n    25, 180],\n   {'r0': 1172, 'r1': 2379, 'r2': 1899}),\n  ('second regression', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 25, 180], {'r0': 1899}),\n  ('normal control 1',\n   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505}], 40, 180],\n   {'r0': 1503, 'r1': 1503}),\n  ('extra case 1', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 25, 180], {'r0': 1172}),\n  ('extra case 2',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],\n    25, 180],\n   {'r0': 1172, 'r1': 1878, 'r2': 925, 'r3': 925})]]\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":"1c3125c773f594a71d9a3895285163dfb4ce54c7a564568b83ee44799a5c921b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(riders, discount_pct, min_overlap_s):\n    matched = [r['id'] for r in riders if r['overlap_s'] >= min_overlap_s]\n    if len(matched) < 2:\n        matched = []\n    out = {}\n    for r in riders:\n        pct = discount_pct if r['id'] in matched else 5\n        out[r['id']] = -(-r['solo'] * (100 - pct) // 100)\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],\n    30, 180],\n   {'r0': 833, 'r1': 2379, 'r2': 833}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}, {'id': 'r3', 'overlap_s': 3, 'solo': 1234}],\n    40, 180],\n   {'r0': 526, 'r1': 1503, 'r2': 2379, 'r3': 1172}),\n  ('second regression',\n   [[{'id': 'r0', 'overlap_s': 180, 'solo': 877}, {'id': 'r1', 'overlap_s': 600, 'solo': 877},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 1999}],\n    40, 180],\n   {'r0': 526, 'r1': 526, 'r2': 1199}),\n  ('extra case 1',\n   [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 3, 'solo': 1999}],\n    25, 180],\n   {'r0': 925, 'r1': 2379, 'r2': 925, 'r3': 1899}),\n  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 25, 180], {'r0': 2379}),\n  ('extra case 3',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 25, 180],\n   {'r0': 1899, 'r1': 2379})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 600, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999}], 25, 180],\n   {'r0': 657, 'r1': 1499}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}, {'id': 'r1', 'overlap_s': 600, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 600, 'solo': 877}],\n    30, 180],\n   {'r0': 1399, 'r1': 1753, 'r2': 613}),\n  ('second regression',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999}], 25, 180],\n   {'r0': 1172, 'r1': 1899}),\n  ('extra case 1',\n   [[{'id': 'r0', 'overlap_s': 181, 'solo': 877}, {'id': 'r1', 'overlap_s': 179, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 600, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 877}],\n    30, 180],\n   {'r0': 613, 'r1': 1899, 'r2': 1399, 'r3': 833}),\n  ('extra case 2',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 180, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}],\n    30, 180],\n   {'r0': 1172, 'r1': 1399, 'r2': 1753}),\n  ('extra case 3',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 0, 'solo': 2505}], 30, 180],\n   {'r0': 1899, 'r1': 2379})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 180, 'solo': 1234}, {'id': 'r1', 'overlap_s': 3, 'solo': 877}], 30, 180],\n   {'r0': 1172, 'r1': 833}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 3, 'solo': 1234},\n     {'id': 'r2', 'overlap_s': 181, 'solo': 2505}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],\n    40, 180],\n   {'r0': 833, 'r1': 1172, 'r2': 2379, 'r3': 1899}),\n  ('second regression', [[{'id': 'r0', 'overlap_s': 180, 'solo': 1999}], 40, 180], {'r0': 1899}),\n  ('normal control 1',\n   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505}], 40, 180],\n   {'r0': 1503, 'r1': 1503}),\n  ('extra case 1', [[{'id': 'r0', 'overlap_s': 600, 'solo': 1234}], 40, 180], {'r0': 1172}),\n  ('extra case 2', [[{'id': 'r0', 'overlap_s': 0, 'solo': 2505}], 30, 180], {'r0': 2379})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 3, 'solo': 877}, {'id': 'r1', 'overlap_s': 0, 'solo': 1234},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 2505}],\n    30, 180],\n   {'r0': 833, 'r1': 1172, 'r2': 2379}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 0, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 181, 'solo': 1999}, {'id': 'r3', 'overlap_s': 3, 'solo': 2505}],\n    40, 180],\n   {'r0': 1172, 'r1': 1899, 'r2': 1899, 'r3': 2379}),\n  ('second regression', [[{'id': 'r0', 'overlap_s': 181, 'solo': 1234}], 40, 180], {'r0': 1172}),\n  ('extra case 1',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 877}, {'id': 'r1', 'overlap_s': 181, 'solo': 1999},\n     {'id': 'r2', 'overlap_s': 3, 'solo': 877}],\n    40, 180],\n   {'r0': 833, 'r1': 1899, 'r2': 833}),\n  ('extra case 2', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1999}], 40, 180], {'r0': 1899}),\n  ('extra case 3',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 2505}, {'id': 'r1', 'overlap_s': 0, 'solo': 877},\n     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}, {'id': 'r3', 'overlap_s': 0, 'solo': 1999}],\n    30, 180],\n   {'r0': 2379, 'r1': 833, 'r2': 1899, 'r3': 1899})],\n [('regression: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1999}, {'id': 'r1', 'overlap_s': 181, 'solo': 1234},\n     {'id': 'r2', 'overlap_s': 0, 'solo': 2505}],\n    25, 180],\n   {'r0': 1899, 'r1': 1172, 'r2': 2379}),\n  ('partial repair probe: rider-favorable rounding',\n   [[{'id': 'r0', 'overlap_s': 0, 'solo': 1234}, {'id': 'r1', 'overlap_s': 179, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 179, 'solo': 1999}],\n    25, 180],\n   {'r0': 1172, 'r1': 2379, 'r2': 1899}),\n  ('second regression', [[{'id': 'r0', 'overlap_s': 179, 'solo': 1999}], 25, 180], {'r0': 1899}),\n  ('normal control 1',\n   [[{'id': 'r0', 'overlap_s': 181, 'solo': 2505}, {'id': 'r1', 'overlap_s': 180, 'solo': 2505}], 40, 180],\n   {'r0': 1503, 'r1': 1503}),\n  ('extra case 1', [[{'id': 'r0', 'overlap_s': 3, 'solo': 1234}], 25, 180], {'r0': 1172}),\n  ('extra case 2',\n   [[{'id': 'r0', 'overlap_s': 179, 'solo': 1234}, {'id': 'r1', 'overlap_s': 181, 'solo': 2505},\n     {'id': 'r2', 'overlap_s': 180, 'solo': 1234}, {'id': 'r3', 'overlap_s': 600, 'solo': 1234}],\n    25, 180],\n   {'r0': 1172, 'r1': 1878, 'r2': 925, 'r3': 925})]]\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-shared-ride-discount-rider-favorable-rounding","generated_at":"2026-09-29T14:50:41.552786+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.","root_cause":"The discounted price is rounded up.","sha256":"36fe9eb91250510fee08d8e7aaa11605194fa39fcb5750ec468fc365bccbdff0","title":"Shared price rounded up against the rider · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":43.291,"exit_code":1,"observations":[{"actual":{"r0":833,"r1":2380,"r2":833},"check":"regression: rider-favorable rounding","expected":{"r0":833,"r1":2379,"r2":833},"passed":false},{"actual":{"r0":526,"r1":1503,"r2":2380,"r3":1172},"check":"partial repair probe: rider-favorable rounding","expected":{"r0":526,"r1":1503,"r2":2379,"r3":1172},"passed":false},{"actual":{"r0":526,"r1":526,"r2":1199},"check":"second regression","expected":{"r0":526,"r1":526,"r2":1199},"passed":true},{"actual":{"r0":926,"r1":2380,"r2":926,"r3":1899},"check":"extra case 1","expected":{"r0":925,"r1":2379,"r2":925,"r3":1899},"passed":false},{"actual":{"r0":2380},"check":"extra case 2","expected":{"r0":2379},"passed":false},{"actual":{"r0":1899,"r1":2380},"check":"extra case 3","expected":{"r0":1899,"r1":2379},"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: rider-favorable rounding\", \"actual\": {\"r0\": 833, \"r1\": 2380, \"r2\": 833}, \"expected\": {\"r0\": 833, \"r1\": 2379, \"r2\": 833}, \"passed\": false}, {\"check\": \"partial repair probe: rider-favorable rounding\", \"actual\": {\"r0\": 526, \"r1\": 1503, \"r2\": 2380, \"r3\": 1172}, \"expected\": {\"r0\": 526, \"r1\": 1503, \"r2\": 2379, \"r3\": 1172}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"r0\": 526, \"r1\": 526, \"r2\": 1199}, \"expected\": {\"r0\": 526, \"r1\": 526, \"r2\": 1199}, \"passed\": true}, {\"check\": \"extra case 1\", \"actual\": {\"r0\": 926, \"r1\": 2380, \"r2\": 926, \"r3\": 1899}, \"expected\": {\"r0\": 925, \"r1\": 2379, \"r2\": 925, \"r3\": 1899}, \"passed\": false}, {\"check\": \"extra case 2\", \"actual\": {\"r0\": 2380}, \"expected\": {\"r0\": 2379}, \"passed\": false}, {\"check\": \"extra case 3\", \"actual\": {\"r0\": 1899, \"r1\": 2380}, \"expected\": {\"r0\": 1899, \"r1\": 2379}, \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.849,"exit_code":1,"observations":[{"actual":{"r0":834,"r1":2380,"r2":834},"check":"regression: rider-favorable rounding","expected":{"r0":833,"r1":2379,"r2":833},"passed":false},{"actual":{"r0":527,"r1":1503,"r2":2380,"r3":1173},"check":"partial repair probe: rider-favorable rounding","expected":{"r0":526,"r1":1503,"r2":2379,"r3":1172},"passed":false},{"actual":{"r0":527,"r1":527,"r2":1200},"check":"second regression","expected":{"r0":526,"r1":526,"r2":1199},"passed":false},{"actual":{"r0":926,"r1":2380,"r2":926,"r3":1900},"check":"extra case 1","expected":{"r0":925,"r1":2379,"r2":925,"r3":1899},"passed":false},{"actual":{"r0":2380},"check":"extra case 2","expected":{"r0":2379},"passed":false},{"actual":{"r0":1900,"r1":2380},"check":"extra case 3","expected":{"r0":1899,"r1":2379},"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: rider-favorable rounding\", \"actual\": {\"r0\": 834, \"r1\": 2380, \"r2\": 834}, \"expected\": {\"r0\": 833, \"r1\": 2379, \"r2\": 833}, \"passed\": false}, {\"check\": \"partial repair probe: rider-favorable rounding\", \"actual\": {\"r0\": 527, \"r1\": 1503, \"r2\": 2380, \"r3\": 1173}, \"expected\": {\"r0\": 526, \"r1\": 1503, \"r2\": 2379, \"r3\": 1172}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"r0\": 527, \"r1\": 527, \"r2\": 1200}, \"expected\": {\"r0\": 526, \"r1\": 526, \"r2\": 1199}, \"passed\": false}, {\"check\": \"extra case 1\", \"actual\": {\"r0\": 926, \"r1\": 2380, \"r2\": 926, \"r3\": 1900}, \"expected\": {\"r0\": 925, \"r1\": 2379, \"r2\": 925, \"r3\": 1899}, \"passed\": false}, {\"check\": \"extra case 2\", \"actual\": {\"r0\": 2380}, \"expected\": {\"r0\": 2379}, \"passed\": false}, {\"check\": \"extra case 3\", \"actual\": {\"r0\": 1900, \"r1\": 2380}, \"expected\": {\"r0\": 1899, \"r1\": 2379}, \"passed\": false}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}