{"abstract":"In a low-scoring match a player with index 0 is awarded 1 bonus.","category":"Fantasy sports scoring","checks":7,"contract":"Allocate bonus points from a per-match performance index: rank players by index descending; positions 1, 2, 3 earn 3, 2, 1. Tied players all receive the award of the first position their group occupies and consume as many positions as the group size, so a two-way tie for first earns 3 each and the next player 1. Players whose index is 0 or below never receive bonus. Return name -> bonus for awarded players.","evaluation_group":"w2-fantasy-sports-scoring-bonus-point-ranks","failed_approach":"Accepting zero still awards bonus to players with no contribution.","family":"w2-fantasy-sports-scoring-bonus-point-ranks-non-positive-index","id":"FA-85271","implementations":{"attempt":{"sha256":"1b438a483442df7ad396a9830a1949a5b81d6d4cc3bdc2b1a35992d1a61e1843","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(bps):\n    ordered = sorted(bps.items(), key=lambda kv: (-kv[1], kv[0]))\n    award = [3, 2, 1]\n    out = {}\n    pos = 0\n    i = 0\n    while i < len(ordered) and pos < 3:\n        j = i\n        while j < len(ordered) and ordered[j][1] == ordered[i][1]:\n            j += 1\n        for name, v in ordered[i:j]:\n            if v >= 0:\n                out[name] = award[pos]\n        pos += j - i\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: non-positive index', [{'ax': 0, 'bo': -2, 'ce': 30, 'gi': 12}], {'ce': 3, 'gi': 2}),\n  ('partial repair probe: non-positive index', [{'bo': 12, 'di': 0, 'fu': 30}], {'bo': 2, 'fu': 3}),\n  ('second regression', [{'ax': 19, 'gi': 30, 'ho': 0}], {'ax': 2, 'gi': 3}),\n  ('normal control 1', [{'ce': 12, 'di': 2, 'fu': 34, 'gi': 0, 'ho': 34}], {'ce': 1, 'fu': 3, 'ho': 3}),\n  ('normal control 2', [{'ce': 30, 'ek': 12, 'fu': 0, 'gi': 38}], {'ce': 2, 'ek': 1, 'gi': 3}),\n  ('normal control 3', [{'bo': 34, 'ce': 0, 'di': 30, 'ek': 22, 'ho': 0}], {'bo': 3, 'di': 2, 'ek': 1}),\n  ('normal control 4', [{'ax': 24, 'bo': -2, 'ce': 34, 'ek': 30, 'fu': 34, 'gi': 34, 'ho': -2}],\n   {'ce': 3, 'fu': 3, 'gi': 3})],\n [('regression: non-positive index', [{'di': 0, 'ek': -2, 'gi': 27}], {'gi': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 27, 'di': 0, 'fu': 0}], {'bo': 3}),\n  ('second regression', [{'bo': 34, 'ce': 0, 'di': 30, 'gi': -2, 'ho': 0}], {'bo': 3, 'di': 2}),\n  ('normal control 1', [{'bo': 34, 'fu': 30, 'ho': 30}], {'bo': 3, 'fu': 2, 'ho': 2}),\n  ('normal control 2', [{'bo': 30, 'di': 30, 'fu': 12, 'gi': 13, 'ho': 12}], {'bo': 3, 'di': 3, 'gi': 1}),\n  ('normal control 3', [{'bo': 10, 'ce': 34, 'di': 30, 'ek': -2, 'fu': 34, 'gi': 34, 'ho': -2}],\n   {'ce': 3, 'fu': 3, 'gi': 3}),\n  ('normal control 4', [{'bo': 4, 'ce': 27, 'di': 12, 'gi': 34}], {'ce': 2, 'di': 1, 'gi': 3})],\n [('regression: non-positive index', [{'ax': 12, 'ek': 27, 'ho': -2}], {'ax': 2, 'ek': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 12, 'fu': 0, 'ho': 27}], {'bo': 2, 'ho': 3}),\n  ('second regression', [{'ax': 30, 'ek': -2, 'gi': 6}], {'ax': 3, 'gi': 2}),\n  ('normal control 1', [{'ax': 34, 'bo': 30, 'ce': -2, 'di': 30, 'ek': -2, 'fu': 34, 'gi': 34, 'ho': 34}],\n   {'ax': 3, 'fu': 3, 'gi': 3, 'ho': 3}),\n  ('normal control 2', [{'ax': 30, 'bo': 30, 'ce': 34, 'ek': 12, 'ho': 30}],\n   {'ax': 2, 'bo': 2, 'ce': 3, 'ho': 2}),\n  ('normal control 3', [{'ax': 27, 'di': 30, 'fu': 32, 'gi': 30}], {'di': 2, 'fu': 3, 'gi': 2}),\n  ('normal control 4', [{'ax': 0, 'bo': -2, 'ce': 0, 'di': 27, 'ek': 12, 'fu': 30, 'gi': 1}],\n   {'di': 2, 'ek': 1, 'fu': 3})],\n [('regression: non-positive index', [{'bo': -1, 'di': 34, 'ho': 0}], {'di': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 0, 'ce': 34, 'fu': 34}], {'ce': 3, 'fu': 3}),\n  ('second regression', [{'ax': -2, 'bo': 0, 'di': 30, 'ek': 34, 'fu': 0, 'ho': 0}], {'di': 2, 'ek': 3}),\n  ('normal control 1', [{'ax': 30, 'ce': 30, 'di': 30, 'fu': 34, 'gi': 27}],\n   {'ax': 2, 'ce': 2, 'di': 2, 'fu': 3}),\n  ('normal control 2', [{'ax': 30, 'ce': 30, 'di': 30, 'ek': 34, 'fu': 27, 'gi': 18, 'ho': 27}],\n   {'ax': 2, 'ce': 2, 'di': 2, 'ek': 3}),\n  ('normal control 3', [{'ax': 34, 'ce': 0, 'ek': 30, 'ho': 12}], {'ax': 3, 'ek': 2, 'ho': 1}),\n  ('normal control 4', [{'ax': 30, 'ce': 30, 'di': 12, 'ek': 30, 'gi': 34, 'ho': 34}],\n   {'ax': 1, 'ce': 1, 'ek': 1, 'gi': 3, 'ho': 3})],\n [('regression: non-positive index', [{'bo': 0, 'fu': 6, 'gi': 30}], {'fu': 2, 'gi': 3}),\n  ('partial repair probe: non-positive index', [{'ce': 0, 'di': 30, 'fu': 0, 'gi': 30, 'ho': -2}],\n   {'di': 3, 'gi': 3}),\n  ('second regression', [{'fu': 30, 'gi': 0, 'ho': 34}], {'fu': 2, 'ho': 3}),\n  ('normal control 1', [{'ax': 27, 'bo': 34, 'ek': 30, 'fu': 27, 'gi': 40, 'ho': 30}],\n   {'bo': 2, 'ek': 1, 'gi': 3, 'ho': 1}),\n  ('normal control 2', [{'ax': 34, 'ce': 30, 'fu': -2, 'gi': 30, 'ho': 34}],\n   {'ax': 3, 'ce': 1, 'gi': 1, 'ho': 3}),\n  ('normal control 3', [{'ek': 34, 'fu': 30, 'gi': 34, 'ho': 27}], {'ek': 3, 'fu': 1, 'gi': 3}),\n  ('normal control 4', [{'ax': 30, 'bo': 16, 'ce': 34, 'di': 12, 'gi': 34, 'ho': 26}],\n   {'ax': 1, 'ce': 3, 'gi': 3})]]\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":"b3d9c24b69559c5382bca4e0365fb0e3a73fccc82e4db2be92a40854c6e8aa9e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(bps):\n    ordered = sorted(bps.items(), key=lambda kv: (-kv[1], kv[0]))\n    award = [3, 2, 1]\n    out = {}\n    pos = 0\n    i = 0\n    while i < len(ordered) and pos < 3:\n        j = i\n        while j < len(ordered) and ordered[j][1] == ordered[i][1]:\n            j += 1\n        for name, v in ordered[i:j]:\n            if v is not None:\n                out[name] = award[pos]\n        pos += j - i\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: non-positive index', [{'ax': 0, 'bo': -2, 'ce': 30, 'gi': 12}], {'ce': 3, 'gi': 2}),\n  ('partial repair probe: non-positive index', [{'bo': 12, 'di': 0, 'fu': 30}], {'bo': 2, 'fu': 3}),\n  ('second regression', [{'ax': 19, 'gi': 30, 'ho': 0}], {'ax': 2, 'gi': 3}),\n  ('normal control 1', [{'ce': 12, 'di': 2, 'fu': 34, 'gi': 0, 'ho': 34}], {'ce': 1, 'fu': 3, 'ho': 3}),\n  ('normal control 2', [{'ce': 30, 'ek': 12, 'fu': 0, 'gi': 38}], {'ce': 2, 'ek': 1, 'gi': 3}),\n  ('normal control 3', [{'bo': 34, 'ce': 0, 'di': 30, 'ek': 22, 'ho': 0}], {'bo': 3, 'di': 2, 'ek': 1}),\n  ('normal control 4', [{'ax': 24, 'bo': -2, 'ce': 34, 'ek': 30, 'fu': 34, 'gi': 34, 'ho': -2}],\n   {'ce': 3, 'fu': 3, 'gi': 3})],\n [('regression: non-positive index', [{'di': 0, 'ek': -2, 'gi': 27}], {'gi': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 27, 'di': 0, 'fu': 0}], {'bo': 3}),\n  ('second regression', [{'bo': 34, 'ce': 0, 'di': 30, 'gi': -2, 'ho': 0}], {'bo': 3, 'di': 2}),\n  ('normal control 1', [{'bo': 34, 'fu': 30, 'ho': 30}], {'bo': 3, 'fu': 2, 'ho': 2}),\n  ('normal control 2', [{'bo': 30, 'di': 30, 'fu': 12, 'gi': 13, 'ho': 12}], {'bo': 3, 'di': 3, 'gi': 1}),\n  ('normal control 3', [{'bo': 10, 'ce': 34, 'di': 30, 'ek': -2, 'fu': 34, 'gi': 34, 'ho': -2}],\n   {'ce': 3, 'fu': 3, 'gi': 3}),\n  ('normal control 4', [{'bo': 4, 'ce': 27, 'di': 12, 'gi': 34}], {'ce': 2, 'di': 1, 'gi': 3})],\n [('regression: non-positive index', [{'ax': 12, 'ek': 27, 'ho': -2}], {'ax': 2, 'ek': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 12, 'fu': 0, 'ho': 27}], {'bo': 2, 'ho': 3}),\n  ('second regression', [{'ax': 30, 'ek': -2, 'gi': 6}], {'ax': 3, 'gi': 2}),\n  ('normal control 1', [{'ax': 34, 'bo': 30, 'ce': -2, 'di': 30, 'ek': -2, 'fu': 34, 'gi': 34, 'ho': 34}],\n   {'ax': 3, 'fu': 3, 'gi': 3, 'ho': 3}),\n  ('normal control 2', [{'ax': 30, 'bo': 30, 'ce': 34, 'ek': 12, 'ho': 30}],\n   {'ax': 2, 'bo': 2, 'ce': 3, 'ho': 2}),\n  ('normal control 3', [{'ax': 27, 'di': 30, 'fu': 32, 'gi': 30}], {'di': 2, 'fu': 3, 'gi': 2}),\n  ('normal control 4', [{'ax': 0, 'bo': -2, 'ce': 0, 'di': 27, 'ek': 12, 'fu': 30, 'gi': 1}],\n   {'di': 2, 'ek': 1, 'fu': 3})],\n [('regression: non-positive index', [{'bo': -1, 'di': 34, 'ho': 0}], {'di': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 0, 'ce': 34, 'fu': 34}], {'ce': 3, 'fu': 3}),\n  ('second regression', [{'ax': -2, 'bo': 0, 'di': 30, 'ek': 34, 'fu': 0, 'ho': 0}], {'di': 2, 'ek': 3}),\n  ('normal control 1', [{'ax': 30, 'ce': 30, 'di': 30, 'fu': 34, 'gi': 27}],\n   {'ax': 2, 'ce': 2, 'di': 2, 'fu': 3}),\n  ('normal control 2', [{'ax': 30, 'ce': 30, 'di': 30, 'ek': 34, 'fu': 27, 'gi': 18, 'ho': 27}],\n   {'ax': 2, 'ce': 2, 'di': 2, 'ek': 3}),\n  ('normal control 3', [{'ax': 34, 'ce': 0, 'ek': 30, 'ho': 12}], {'ax': 3, 'ek': 2, 'ho': 1}),\n  ('normal control 4', [{'ax': 30, 'ce': 30, 'di': 12, 'ek': 30, 'gi': 34, 'ho': 34}],\n   {'ax': 1, 'ce': 1, 'ek': 1, 'gi': 3, 'ho': 3})],\n [('regression: non-positive index', [{'bo': 0, 'fu': 6, 'gi': 30}], {'fu': 2, 'gi': 3}),\n  ('partial repair probe: non-positive index', [{'ce': 0, 'di': 30, 'fu': 0, 'gi': 30, 'ho': -2}],\n   {'di': 3, 'gi': 3}),\n  ('second regression', [{'fu': 30, 'gi': 0, 'ho': 34}], {'fu': 2, 'ho': 3}),\n  ('normal control 1', [{'ax': 27, 'bo': 34, 'ek': 30, 'fu': 27, 'gi': 40, 'ho': 30}],\n   {'bo': 2, 'ek': 1, 'gi': 3, 'ho': 1}),\n  ('normal control 2', [{'ax': 34, 'ce': 30, 'fu': -2, 'gi': 30, 'ho': 34}],\n   {'ax': 3, 'ce': 1, 'gi': 1, 'ho': 3}),\n  ('normal control 3', [{'ek': 34, 'fu': 30, 'gi': 34, 'ho': 27}], {'ek': 3, 'fu': 1, 'gi': 3}),\n  ('normal control 4', [{'ax': 30, 'bo': 16, 'ce': 34, 'di': 12, 'gi': 34, 'ho': 26}],\n   {'ax': 1, 'ce': 3, 'gi': 3})]]\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":"ee4b932ac1418c174af9918fd40d81a17c888deaa7659468735bf1bd6ea0bb22","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(bps):\n    ordered = sorted(bps.items(), key=lambda kv: (-kv[1], kv[0]))\n    award = [3, 2, 1]\n    out = {}\n    pos = 0\n    i = 0\n    while i < len(ordered) and pos < 3:\n        j = i\n        while j < len(ordered) and ordered[j][1] == ordered[i][1]:\n            j += 1\n        for name, v in ordered[i:j]:\n            if v > 0:\n                out[name] = award[pos]\n        pos += j - i\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: non-positive index', [{'ax': 0, 'bo': -2, 'ce': 30, 'gi': 12}], {'ce': 3, 'gi': 2}),\n  ('partial repair probe: non-positive index', [{'bo': 12, 'di': 0, 'fu': 30}], {'bo': 2, 'fu': 3}),\n  ('second regression', [{'ax': 19, 'gi': 30, 'ho': 0}], {'ax': 2, 'gi': 3}),\n  ('normal control 1', [{'ce': 12, 'di': 2, 'fu': 34, 'gi': 0, 'ho': 34}], {'ce': 1, 'fu': 3, 'ho': 3}),\n  ('normal control 2', [{'ce': 30, 'ek': 12, 'fu': 0, 'gi': 38}], {'ce': 2, 'ek': 1, 'gi': 3}),\n  ('normal control 3', [{'bo': 34, 'ce': 0, 'di': 30, 'ek': 22, 'ho': 0}], {'bo': 3, 'di': 2, 'ek': 1}),\n  ('normal control 4', [{'ax': 24, 'bo': -2, 'ce': 34, 'ek': 30, 'fu': 34, 'gi': 34, 'ho': -2}],\n   {'ce': 3, 'fu': 3, 'gi': 3})],\n [('regression: non-positive index', [{'di': 0, 'ek': -2, 'gi': 27}], {'gi': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 27, 'di': 0, 'fu': 0}], {'bo': 3}),\n  ('second regression', [{'bo': 34, 'ce': 0, 'di': 30, 'gi': -2, 'ho': 0}], {'bo': 3, 'di': 2}),\n  ('normal control 1', [{'bo': 34, 'fu': 30, 'ho': 30}], {'bo': 3, 'fu': 2, 'ho': 2}),\n  ('normal control 2', [{'bo': 30, 'di': 30, 'fu': 12, 'gi': 13, 'ho': 12}], {'bo': 3, 'di': 3, 'gi': 1}),\n  ('normal control 3', [{'bo': 10, 'ce': 34, 'di': 30, 'ek': -2, 'fu': 34, 'gi': 34, 'ho': -2}],\n   {'ce': 3, 'fu': 3, 'gi': 3}),\n  ('normal control 4', [{'bo': 4, 'ce': 27, 'di': 12, 'gi': 34}], {'ce': 2, 'di': 1, 'gi': 3})],\n [('regression: non-positive index', [{'ax': 12, 'ek': 27, 'ho': -2}], {'ax': 2, 'ek': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 12, 'fu': 0, 'ho': 27}], {'bo': 2, 'ho': 3}),\n  ('second regression', [{'ax': 30, 'ek': -2, 'gi': 6}], {'ax': 3, 'gi': 2}),\n  ('normal control 1', [{'ax': 34, 'bo': 30, 'ce': -2, 'di': 30, 'ek': -2, 'fu': 34, 'gi': 34, 'ho': 34}],\n   {'ax': 3, 'fu': 3, 'gi': 3, 'ho': 3}),\n  ('normal control 2', [{'ax': 30, 'bo': 30, 'ce': 34, 'ek': 12, 'ho': 30}],\n   {'ax': 2, 'bo': 2, 'ce': 3, 'ho': 2}),\n  ('normal control 3', [{'ax': 27, 'di': 30, 'fu': 32, 'gi': 30}], {'di': 2, 'fu': 3, 'gi': 2}),\n  ('normal control 4', [{'ax': 0, 'bo': -2, 'ce': 0, 'di': 27, 'ek': 12, 'fu': 30, 'gi': 1}],\n   {'di': 2, 'ek': 1, 'fu': 3})],\n [('regression: non-positive index', [{'bo': -1, 'di': 34, 'ho': 0}], {'di': 3}),\n  ('partial repair probe: non-positive index', [{'bo': 0, 'ce': 34, 'fu': 34}], {'ce': 3, 'fu': 3}),\n  ('second regression', [{'ax': -2, 'bo': 0, 'di': 30, 'ek': 34, 'fu': 0, 'ho': 0}], {'di': 2, 'ek': 3}),\n  ('normal control 1', [{'ax': 30, 'ce': 30, 'di': 30, 'fu': 34, 'gi': 27}],\n   {'ax': 2, 'ce': 2, 'di': 2, 'fu': 3}),\n  ('normal control 2', [{'ax': 30, 'ce': 30, 'di': 30, 'ek': 34, 'fu': 27, 'gi': 18, 'ho': 27}],\n   {'ax': 2, 'ce': 2, 'di': 2, 'ek': 3}),\n  ('normal control 3', [{'ax': 34, 'ce': 0, 'ek': 30, 'ho': 12}], {'ax': 3, 'ek': 2, 'ho': 1}),\n  ('normal control 4', [{'ax': 30, 'ce': 30, 'di': 12, 'ek': 30, 'gi': 34, 'ho': 34}],\n   {'ax': 1, 'ce': 1, 'ek': 1, 'gi': 3, 'ho': 3})],\n [('regression: non-positive index', [{'bo': 0, 'fu': 6, 'gi': 30}], {'fu': 2, 'gi': 3}),\n  ('partial repair probe: non-positive index', [{'ce': 0, 'di': 30, 'fu': 0, 'gi': 30, 'ho': -2}],\n   {'di': 3, 'gi': 3}),\n  ('second regression', [{'fu': 30, 'gi': 0, 'ho': 34}], {'fu': 2, 'ho': 3}),\n  ('normal control 1', [{'ax': 27, 'bo': 34, 'ek': 30, 'fu': 27, 'gi': 40, 'ho': 30}],\n   {'bo': 2, 'ek': 1, 'gi': 3, 'ho': 1}),\n  ('normal control 2', [{'ax': 34, 'ce': 30, 'fu': -2, 'gi': 30, 'ho': 34}],\n   {'ax': 3, 'ce': 1, 'gi': 1, 'ho': 3}),\n  ('normal control 3', [{'ek': 34, 'fu': 30, 'gi': 34, 'ho': 27}], {'ek': 3, 'fu': 1, 'gi': 3}),\n  ('normal control 4', [{'ax': 30, 'bo': 16, 'ce': 34, 'di': 12, 'gi': 34, 'ho': 26}],\n   {'ax': 1, 'ce': 3, 'gi': 3})]]\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 scoring contract stipulated for this example; it is not the rulebook of any real fantasy platform. 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-fantasy-sports-scoring-bonus-point-ranks-non-positive-index","generated_at":"2026-09-29T14:50:38.952193+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Bonus allocation with ties is a compact but contested rule in football fantasy games.","repair":"Award bonus only to positive indices.","root_cause":"The award guard never excludes non-positive indices.","sha256":"184296f747212d4c5102cfeb22697c58da1fc86940abc1a0e25ed7e9082a3656","title":"Players with no performance index receive bonus · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":39.328,"exit_code":1,"observations":[{"actual":{"ax":1,"ce":3,"gi":2},"check":"regression: non-positive index","expected":{"ce":3,"gi":2},"passed":false},{"actual":{"bo":2,"di":1,"fu":3},"check":"partial repair probe: non-positive index","expected":{"bo":2,"fu":3},"passed":false},{"actual":{"ax":2,"gi":3,"ho":1},"check":"second regression","expected":{"ax":2,"gi":3},"passed":false},{"actual":{"ce":1,"fu":3,"ho":3},"check":"normal control 1","expected":{"ce":1,"fu":3,"ho":3},"passed":true},{"actual":{"ce":2,"ek":1,"gi":3},"check":"normal control 2","expected":{"ce":2,"ek":1,"gi":3},"passed":true},{"actual":{"bo":3,"di":2,"ek":1},"check":"normal control 3","expected":{"bo":3,"di":2,"ek":1},"passed":true},{"actual":{"ce":3,"fu":3,"gi":3},"check":"normal control 4","expected":{"ce":3,"fu":3,"gi":3},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: non-positive index\", \"actual\": {\"ce\": 3, \"gi\": 2, \"ax\": 1}, \"expected\": {\"ce\": 3, \"gi\": 2}, \"passed\": false}, {\"check\": \"partial repair probe: non-positive index\", \"actual\": {\"fu\": 3, \"bo\": 2, \"di\": 1}, \"expected\": {\"bo\": 2, \"fu\": 3}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"gi\": 3, \"ax\": 2, \"ho\": 1}, \"expected\": {\"ax\": 2, \"gi\": 3}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"fu\": 3, \"ho\": 3, \"ce\": 1}, \"expected\": {\"ce\": 1, \"fu\": 3, \"ho\": 3}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"gi\": 3, \"ce\": 2, \"ek\": 1}, \"expected\": {\"ce\": 2, \"ek\": 1, \"gi\": 3}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"bo\": 3, \"di\": 2, \"ek\": 1}, \"expected\": {\"bo\": 3, \"di\": 2, \"ek\": 1}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"ce\": 3, \"fu\": 3, \"gi\": 3}, \"expected\": {\"ce\": 3, \"fu\": 3, \"gi\": 3}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":36.174,"exit_code":1,"observations":[{"actual":{"ax":1,"ce":3,"gi":2},"check":"regression: non-positive index","expected":{"ce":3,"gi":2},"passed":false},{"actual":{"bo":2,"di":1,"fu":3},"check":"partial repair probe: non-positive index","expected":{"bo":2,"fu":3},"passed":false},{"actual":{"ax":2,"gi":3,"ho":1},"check":"second regression","expected":{"ax":2,"gi":3},"passed":false},{"actual":{"ce":1,"fu":3,"ho":3},"check":"normal control 1","expected":{"ce":1,"fu":3,"ho":3},"passed":true},{"actual":{"ce":2,"ek":1,"gi":3},"check":"normal control 2","expected":{"ce":2,"ek":1,"gi":3},"passed":true},{"actual":{"bo":3,"di":2,"ek":1},"check":"normal control 3","expected":{"bo":3,"di":2,"ek":1},"passed":true},{"actual":{"ce":3,"fu":3,"gi":3},"check":"normal control 4","expected":{"ce":3,"fu":3,"gi":3},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: non-positive index\", \"actual\": {\"ce\": 3, \"gi\": 2, \"ax\": 1}, \"expected\": {\"ce\": 3, \"gi\": 2}, \"passed\": false}, {\"check\": \"partial repair probe: non-positive index\", \"actual\": {\"fu\": 3, \"bo\": 2, \"di\": 1}, \"expected\": {\"bo\": 2, \"fu\": 3}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"gi\": 3, \"ax\": 2, \"ho\": 1}, \"expected\": {\"ax\": 2, \"gi\": 3}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"fu\": 3, \"ho\": 3, \"ce\": 1}, \"expected\": {\"ce\": 1, \"fu\": 3, \"ho\": 3}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"gi\": 3, \"ce\": 2, \"ek\": 1}, \"expected\": {\"ce\": 2, \"ek\": 1, \"gi\": 3}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"bo\": 3, \"di\": 2, \"ek\": 1}, \"expected\": {\"bo\": 3, \"di\": 2, \"ek\": 1}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"ce\": 3, \"fu\": 3, \"gi\": 3}, \"expected\": {\"ce\": 3, \"fu\": 3, \"gi\": 3}, \"passed\": true}], \"passed\": 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