{"abstract":"Allocation results list states with zero cents allocated.","category":"Payroll withholding rules","checks":10,"contract":"Input {wage, days: {state: work days}, resident}. With zero total days all wages go to the resident state. Otherwise each state gets floor(wage*days/total); leftover cents go one each to states ranked by largest fractional remainder, ties by the base order (days descending, then state name). States allocated 0 cents are omitted. Return {state: cents}.","contract_signature":"x","evaluation_group":"w2-payroll-withholding-workday-wage-allocation","failed_approach":"The attempt filters on days rather than cents, keeping worked states that got no cents.","family":"w2-payroll-withholding-workday-wage-allocation-zero-allocation-filter","id":"FA-59216","implementations":{"attempt":{"sha256":"69d781525499d6244359455dacf62f7b9d277c5583fc846959f97edf9d5f19e8","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    days = x['days']\n    total = sum(days.values())\n    if total == 0:\n        return {x['resident']: x['wage']}\n    order = sorted(days, key=lambda s: (-days[s], s))\n    alloc = {s: x['wage'] * days[s] // total for s in order}\n    rema = sorted(order, key=lambda s: (-(x['wage'] * days[s] % total), order.index(s)))\n    left = x['wage'] - sum(alloc.values())\n    for s in rema[:left]:\n        alloc[s] += 1\n    return {s: v for s, v in alloc.items() if days[s]}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'wage': 2, 'days': {'NJ': 2, 'PA': 2, 'NY': 0, 'MA': 1}, 'resident': 'CT'}, {'NJ': 1, 'PA': 1}), ('regression', {'wage': 61318, 'days': {'CT': 0, 'MA': 0, 'PA': 12, 'NJ': 19}, 'resident': 'NY'}, {'NJ': 37582, 'PA': 23736}), ('partial-repair probe', {'wage': 5, 'days': {'MA': 17, 'NY': 2, 'PA': 9}, 'resident': 'NJ'}, {'MA': 3, 'PA': 2}), ('partial-repair probe', {'wage': 3, 'days': {'NJ': 2, 'MA': 2, 'PA': 13, 'CT': 2}, 'resident': 'NJ'}, {'PA': 2, 'CT': 1}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 410311, 'days': {'PA': 10}, 'resident': 'CT'}, {'PA': 410311}), ('normal control', {'wage': 9, 'days': {'PA': 1, 'CT': 8}, 'resident': 'NY'}, {'CT': 8, 'PA': 1}), ('normal control', {'wage': 17, 'days': {'NJ': 1, 'PA': 3, 'NY': 3}, 'resident': 'NY'}, {'NY': 7, 'PA': 7, 'NJ': 3}), ('normal control', {'wage': 22, 'days': {'CT': 0}, 'resident': 'NY'}, {'NY': 22})], [('regression', {'wage': 5, 'days': {'MA': 17, 'NY': 2, 'PA': 9}, 'resident': 'NJ'}, {'MA': 3, 'PA': 2}), ('regression', {'wage': 273177, 'days': {'PA': 0, 'NY': 1, 'NJ': 3}, 'resident': 'NJ'}, {'NJ': 204883, 'NY': 68294}), ('partial-repair probe', {'wage': 3, 'days': {'NJ': 2, 'MA': 2, 'PA': 13, 'CT': 2}, 'resident': 'NJ'}, {'PA': 2, 'CT': 1}), ('partial-repair probe', {'wage': 4, 'days': {'NJ': 3, 'CT': 1, 'MA': 3}, 'resident': 'CT'}, {'MA': 2, 'NJ': 2}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 231015, 'days': {'PA': 0}, 'resident': 'CT'}, {'CT': 231015}), ('normal control', {'wage': 874754, 'days': {'PA': 19}, 'resident': 'CT'}, {'PA': 874754}), ('normal control', {'wage': 315271, 'days': {'PA': 2, 'NY': 3, 'CT': 1}, 'resident': 'NJ'}, {'NY': 157636, 'PA': 105090, 'CT': 52545}), ('normal control', {'wage': 761711, 'days': {'MA': 0}, 'resident': 'NJ'}, {'NJ': 761711})], [('regression', {'wage': 3, 'days': {'NJ': 2, 'MA': 2, 'PA': 13, 'CT': 2}, 'resident': 'NJ'}, {'PA': 2, 'CT': 1}), ('regression', {'wage': 424602, 'days': {'PA': 2, 'CT': 0, 'NJ': 7, 'NY': 0}, 'resident': 'CT'}, {'NJ': 330246, 'PA': 94356}), ('partial-repair probe', {'wage': 1, 'days': {'NJ': 1, 'MA': 11}, 'resident': 'CT'}, {'MA': 1}), ('partial-repair probe', {'wage': 4, 'days': {'NY': 21, 'PA': 0, 'CT': 0, 'MA': 2}, 'resident': 'CT'}, {'NY': 4}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 1, 'days': {'NJ': 15}, 'resident': 'NY'}, {'NJ': 1}), ('normal control', {'wage': 399573, 'days': {'CT': 0}, 'resident': 'CT'}, {'CT': 399573}), ('normal control', {'wage': 172162, 'days': {'MA': 6, 'CT': 3, 'PA': 11}, 'resident': 'NJ'}, {'PA': 94689, 'MA': 51649, 'CT': 25824}), ('normal control', {'wage': 2, 'days': {'NJ': 0}, 'resident': 'NJ'}, {'NJ': 2})], [('regression', {'wage': 4, 'days': {'NJ': 3, 'CT': 1, 'MA': 3}, 'resident': 'CT'}, {'MA': 2, 'NJ': 2}), ('regression', {'wage': 179374, 'days': {'PA': 20, 'NJ': 2, 'MA': 0}, 'resident': 'NY'}, {'PA': 163067, 'NJ': 16307}), ('partial-repair probe', {'wage': 0, 'days': {'MA': 15, 'NY': 9, 'PA': 0, 'CT': 3}, 'resident': 'CT'}, {}), ('partial-repair probe', {'wage': 1, 'days': {'CT': 20, 'NJ': 3, 'PA': 3}, 'resident': 'NY'}, {'CT': 1}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 525270, 'days': {'MA': 2, 'PA': 11, 'NJ': 22}, 'resident': 'NJ'}, {'NJ': 330170, 'PA': 165085, 'MA': 30015}), ('normal control', {'wage': 16, 'days': {'CT': 3, 'PA': 1}, 'resident': 'CT'}, {'CT': 12, 'PA': 4}), ('normal control', {'wage': 27, 'days': {'NY': 0}, 'resident': 'NY'}, {'NY': 27}), ('normal control', {'wage': 29, 'days': {'PA': 2}, 'resident': 'NJ'}, {'PA': 29})], [('regression', {'wage': 1, 'days': {'NJ': 1, 'MA': 11}, 'resident': 'CT'}, {'MA': 1}), ('regression', {'wage': 14, 'days': {'PA': 0, 'MA': 2}, 'resident': 'CT'}, {'MA': 14}), ('partial-repair probe', {'wage': 0, 'days': {'NJ': 0, 'CT': 8}, 'resident': 'NY'}, {}), ('partial-repair probe', {'wage': 1, 'days': {'CT': 15, 'PA': 21, 'NY': 0, 'MA': 2}, 'resident': 'CT'}, {'PA': 1}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 646558, 'days': {'NY': 15, 'CT': 3, 'MA': 3, 'NJ': 2}, 'resident': 'NJ'}, {'NY': 421668, 'CT': 84334, 'MA': 84334, 'NJ': 56222}), ('normal control', {'wage': 455522, 'days': {'MA': 0, 'CT': 0}, 'resident': 'NY'}, {'NY': 455522}), ('normal control', {'wage': 794041, 'days': {'NY': 20, 'PA': 1}, 'resident': 'NY'}, {'NY': 756230, 'PA': 37811}), ('normal control', {'wage': 402912, 'days': {'CT': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 402912})]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), 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":"2f465ec65026876b68f0c50bbf4fa5d447732b943e58d8095a8ed2c735de68e4","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    days = x['days']\n    total = sum(days.values())\n    if total == 0:\n        return {x['resident']: x['wage']}\n    order = sorted(days, key=lambda s: (-days[s], s))\n    alloc = {s: x['wage'] * days[s] // total for s in order}\n    rema = sorted(order, key=lambda s: (-(x['wage'] * days[s] % total), order.index(s)))\n    left = x['wage'] - sum(alloc.values())\n    for s in rema[:left]:\n        alloc[s] += 1\n    return {s: v for s, v in alloc.items() }\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'wage': 2, 'days': {'NJ': 2, 'PA': 2, 'NY': 0, 'MA': 1}, 'resident': 'CT'}, {'NJ': 1, 'PA': 1}), ('regression', {'wage': 61318, 'days': {'CT': 0, 'MA': 0, 'PA': 12, 'NJ': 19}, 'resident': 'NY'}, {'NJ': 37582, 'PA': 23736}), ('partial-repair probe', {'wage': 5, 'days': {'MA': 17, 'NY': 2, 'PA': 9}, 'resident': 'NJ'}, {'MA': 3, 'PA': 2}), ('partial-repair probe', {'wage': 3, 'days': {'NJ': 2, 'MA': 2, 'PA': 13, 'CT': 2}, 'resident': 'NJ'}, {'PA': 2, 'CT': 1}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 410311, 'days': {'PA': 10}, 'resident': 'CT'}, {'PA': 410311}), ('normal control', {'wage': 9, 'days': {'PA': 1, 'CT': 8}, 'resident': 'NY'}, {'CT': 8, 'PA': 1}), ('normal control', {'wage': 17, 'days': {'NJ': 1, 'PA': 3, 'NY': 3}, 'resident': 'NY'}, {'NY': 7, 'PA': 7, 'NJ': 3}), ('normal control', {'wage': 22, 'days': {'CT': 0}, 'resident': 'NY'}, {'NY': 22})], [('regression', {'wage': 5, 'days': {'MA': 17, 'NY': 2, 'PA': 9}, 'resident': 'NJ'}, {'MA': 3, 'PA': 2}), ('regression', {'wage': 273177, 'days': {'PA': 0, 'NY': 1, 'NJ': 3}, 'resident': 'NJ'}, {'NJ': 204883, 'NY': 68294}), ('partial-repair probe', {'wage': 3, 'days': {'NJ': 2, 'MA': 2, 'PA': 13, 'CT': 2}, 'resident': 'NJ'}, {'PA': 2, 'CT': 1}), ('partial-repair probe', {'wage': 4, 'days': {'NJ': 3, 'CT': 1, 'MA': 3}, 'resident': 'CT'}, {'MA': 2, 'NJ': 2}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 231015, 'days': {'PA': 0}, 'resident': 'CT'}, {'CT': 231015}), ('normal control', {'wage': 874754, 'days': {'PA': 19}, 'resident': 'CT'}, {'PA': 874754}), ('normal control', {'wage': 315271, 'days': {'PA': 2, 'NY': 3, 'CT': 1}, 'resident': 'NJ'}, {'NY': 157636, 'PA': 105090, 'CT': 52545}), ('normal control', {'wage': 761711, 'days': {'MA': 0}, 'resident': 'NJ'}, {'NJ': 761711})], [('regression', {'wage': 3, 'days': {'NJ': 2, 'MA': 2, 'PA': 13, 'CT': 2}, 'resident': 'NJ'}, {'PA': 2, 'CT': 1}), ('regression', {'wage': 424602, 'days': {'PA': 2, 'CT': 0, 'NJ': 7, 'NY': 0}, 'resident': 'CT'}, {'NJ': 330246, 'PA': 94356}), ('partial-repair probe', {'wage': 1, 'days': {'NJ': 1, 'MA': 11}, 'resident': 'CT'}, {'MA': 1}), ('partial-repair probe', {'wage': 4, 'days': {'NY': 21, 'PA': 0, 'CT': 0, 'MA': 2}, 'resident': 'CT'}, {'NY': 4}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 1, 'days': {'NJ': 15}, 'resident': 'NY'}, {'NJ': 1}), ('normal control', {'wage': 399573, 'days': {'CT': 0}, 'resident': 'CT'}, {'CT': 399573}), ('normal control', {'wage': 172162, 'days': {'MA': 6, 'CT': 3, 'PA': 11}, 'resident': 'NJ'}, {'PA': 94689, 'MA': 51649, 'CT': 25824}), ('normal control', {'wage': 2, 'days': {'NJ': 0}, 'resident': 'NJ'}, {'NJ': 2})], [('regression', {'wage': 4, 'days': {'NJ': 3, 'CT': 1, 'MA': 3}, 'resident': 'CT'}, {'MA': 2, 'NJ': 2}), ('regression', {'wage': 179374, 'days': {'PA': 20, 'NJ': 2, 'MA': 0}, 'resident': 'NY'}, {'PA': 163067, 'NJ': 16307}), ('partial-repair probe', {'wage': 0, 'days': {'MA': 15, 'NY': 9, 'PA': 0, 'CT': 3}, 'resident': 'CT'}, {}), ('partial-repair probe', {'wage': 1, 'days': {'CT': 20, 'NJ': 3, 'PA': 3}, 'resident': 'NY'}, {'CT': 1}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 525270, 'days': {'MA': 2, 'PA': 11, 'NJ': 22}, 'resident': 'NJ'}, {'NJ': 330170, 'PA': 165085, 'MA': 30015}), ('normal control', {'wage': 16, 'days': {'CT': 3, 'PA': 1}, 'resident': 'CT'}, {'CT': 12, 'PA': 4}), ('normal control', {'wage': 27, 'days': {'NY': 0}, 'resident': 'NY'}, {'NY': 27}), ('normal control', {'wage': 29, 'days': {'PA': 2}, 'resident': 'NJ'}, {'PA': 29})], [('regression', {'wage': 1, 'days': {'NJ': 1, 'MA': 11}, 'resident': 'CT'}, {'MA': 1}), ('regression', {'wage': 14, 'days': {'PA': 0, 'MA': 2}, 'resident': 'CT'}, {'MA': 14}), ('partial-repair probe', {'wage': 0, 'days': {'NJ': 0, 'CT': 8}, 'resident': 'NY'}, {}), ('partial-repair probe', {'wage': 1, 'days': {'CT': 15, 'PA': 21, 'NY': 0, 'MA': 2}, 'resident': 'CT'}, {'PA': 1}), ('boundary control', {'wage': 100, 'days': {'NY': 1, 'NJ': 1, 'CT': 1}, 'resident': 'NJ'}, {'CT': 34, 'NJ': 33, 'NY': 33}), ('boundary control', {'wage': 5000, 'days': {'NY': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 5000}), ('normal control', {'wage': 646558, 'days': {'NY': 15, 'CT': 3, 'MA': 3, 'NJ': 2}, 'resident': 'NJ'}, {'NY': 421668, 'CT': 84334, 'MA': 84334, 'NJ': 56222}), ('normal control', {'wage': 455522, 'days': {'MA': 0, 'CT': 0}, 'resident': 'NY'}, {'NY': 455522}), ('normal control', {'wage': 794041, 'days': {'NY': 20, 'PA': 1}, 'resident': 'NY'}, {'NY': 756230, 'PA': 37811}), ('normal control', {'wage': 402912, 'days': {'CT': 0, 'NJ': 0}, 'resident': 'NJ'}, {'NJ': 402912})]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), 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 teaching model of a stipulated payroll rule with toy thresholds and rates. It makes no claim of conformance to any tax authority, statute or jurisdiction and is not payroll software. 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-payroll-withholding-workday-wage-allocation-zero-allocation-filter","generated_at":"2026-09-29T14:46:34.172066+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"State wage allocation must sum exactly to the paycheck with a deterministic remainder rule.","root_cause":"The final mapping keeps states whose allocation is zero.","sha256":"cdca334c70f99f86c6926f4644b142c8e5b4a2261d72919201c70532a549ca5d","title":"Multi-state wage allocation by work days: zero allocation filter · 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":47.096,"exit_code":1,"observations":[{"actual":{"MA":0,"NJ":1,"PA":1},"check":"regression 0","expected":{"NJ":1,"PA":1},"passed":false},{"actual":{"NJ":37582,"PA":23736},"check":"regression 1","expected":{"NJ":37582,"PA":23736},"passed":true},{"actual":{"MA":3,"NY":0,"PA":2},"check":"partial-repair probe 2","expected":{"MA":3,"PA":2},"passed":false},{"actual":{"CT":1,"MA":0,"NJ":0,"PA":2},"check":"partial-repair probe 3","expected":{"CT":1,"PA":2},"passed":false},{"actual":{"CT":34,"NJ":33,"NY":33},"check":"boundary control 4","expected":{"CT":34,"NJ":33,"NY":33},"passed":true},{"actual":{"NJ":5000},"check":"boundary control 5","expected":{"NJ":5000},"passed":true},{"actual":{"PA":410311},"check":"normal control 6","expected":{"PA":410311},"passed":true},{"actual":{"CT":8,"PA":1},"check":"normal control 7","expected":{"CT":8,"PA":1},"passed":true},{"actual":{"NJ":3,"NY":7,"PA":7},"check":"normal control 8","expected":{"NJ":3,"NY":7,"PA":7},"passed":true},{"actual":{"NY":22},"check":"normal control 9","expected":{"NY":22},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": {\"NJ\": 1, \"PA\": 1, \"MA\": 0}, \"expected\": {\"NJ\": 1, \"PA\": 1}, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": {\"NJ\": 37582, \"PA\": 23736}, \"expected\": {\"NJ\": 37582, \"PA\": 23736}, \"passed\": true}, {\"check\": \"partial-repair probe 2\", \"actual\": {\"MA\": 3, \"PA\": 2, \"NY\": 0}, \"expected\": {\"MA\": 3, \"PA\": 2}, \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": {\"PA\": 2, \"CT\": 1, \"MA\": 0, \"NJ\": 0}, \"expected\": {\"PA\": 2, \"CT\": 1}, \"passed\": false}, {\"check\": \"boundary control 4\", \"actual\": {\"CT\": 34, \"NJ\": 33, \"NY\": 33}, \"expected\": {\"CT\": 34, \"NJ\": 33, \"NY\": 33}, \"passed\": true}, {\"check\": \"boundary control 5\", \"actual\": {\"NJ\": 5000}, \"expected\": {\"NJ\": 5000}, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": {\"PA\": 410311}, \"expected\": {\"PA\": 410311}, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": {\"CT\": 8, \"PA\": 1}, \"expected\": {\"CT\": 8, \"PA\": 1}, \"passed\": true}, {\"check\": \"normal control 8\", \"actual\": {\"NY\": 7, \"PA\": 7, \"NJ\": 3}, \"expected\": {\"NY\": 7, \"PA\": 7, \"NJ\": 3}, \"passed\": true}, {\"check\": \"normal control 9\", \"actual\": {\"NY\": 22}, \"expected\": {\"NY\": 22}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.861,"exit_code":1,"observations":[{"actual":{"MA":0,"NJ":1,"NY":0,"PA":1},"check":"regression 0","expected":{"NJ":1,"PA":1},"passed":false},{"actual":{"CT":0,"MA":0,"NJ":37582,"PA":23736},"check":"regression 1","expected":{"NJ":37582,"PA":23736},"passed":false},{"actual":{"MA":3,"NY":0,"PA":2},"check":"partial-repair probe 2","expected":{"MA":3,"PA":2},"passed":false},{"actual":{"CT":1,"MA":0,"NJ":0,"PA":2},"check":"partial-repair probe 3","expected":{"CT":1,"PA":2},"passed":false},{"actual":{"CT":34,"NJ":33,"NY":33},"check":"boundary control 4","expected":{"CT":34,"NJ":33,"NY":33},"passed":true},{"actual":{"NJ":5000},"check":"boundary control 5","expected":{"NJ":5000},"passed":true},{"actual":{"PA":410311},"check":"normal control 6","expected":{"PA":410311},"passed":true},{"actual":{"CT":8,"PA":1},"check":"normal control 7","expected":{"CT":8,"PA":1},"passed":true},{"actual":{"NJ":3,"NY":7,"PA":7},"check":"normal control 8","expected":{"NJ":3,"NY":7,"PA":7},"passed":true},{"actual":{"NY":22},"check":"normal control 9","expected":{"NY":22},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": {\"NJ\": 1, \"PA\": 1, \"MA\": 0, \"NY\": 0}, \"expected\": {\"NJ\": 1, \"PA\": 1}, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": {\"NJ\": 37582, \"PA\": 23736, \"CT\": 0, \"MA\": 0}, \"expected\": {\"NJ\": 37582, \"PA\": 23736}, \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": {\"MA\": 3, \"PA\": 2, \"NY\": 0}, \"expected\": {\"MA\": 3, \"PA\": 2}, \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": {\"PA\": 2, \"CT\": 1, \"MA\": 0, \"NJ\": 0}, \"expected\": {\"PA\": 2, \"CT\": 1}, \"passed\": false}, {\"check\": \"boundary control 4\", \"actual\": {\"CT\": 34, \"NJ\": 33, \"NY\": 33}, \"expected\": {\"CT\": 34, \"NJ\": 33, \"NY\": 33}, \"passed\": true}, {\"check\": \"boundary control 5\", \"actual\": {\"NJ\": 5000}, \"expected\": {\"NJ\": 5000}, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": {\"PA\": 410311}, \"expected\": {\"PA\": 410311}, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": {\"CT\": 8, \"PA\": 1}, \"expected\": {\"CT\": 8, \"PA\": 1}, \"passed\": true}, {\"check\": \"normal control 8\", \"actual\": {\"NY\": 7, \"PA\": 7, \"NJ\": 3}, \"expected\": {\"NY\": 7, \"PA\": 7, \"NJ\": 3}, \"passed\": true}, {\"check\": \"normal control 9\", \"actual\": {\"NY\": 22}, \"expected\": {\"NY\": 22}, \"passed\": true}], \"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."}}