Clean-code smell scorecard for a method

The exact vocabulary the automated check enforces: keys, tokens, entry points, and worked examples. Generated from the verification source.

{
 "constants": {
  "_SMELL_RULES": [
   [
    "loc",
    "max_loc",
    "LongMethod"
   ],
   [
    "args",
    "max_args",
    "TooManyArguments"
   ],
   [
    "attributes",
    "max_attributes",
    "TooManyAttributes"
   ],
   [
    "nesting",
    "max_nesting",
    "DeepNesting"
   ],
   [
    "comment_ratio",
    "max_comment_ratio",
    "OverCommented"
   ]
  ]
 },
 "entryName": "compute_code_smell_scorecard",
 "expectNote": "expect is the comparison-space value (your return value goes through normalize first when one is published)",
 "hiddenCaseCount": 5,
 "hiddenCaseNames": [
  "all_five_fail",
  "strict_boundary_no_flag",
  "custom_thresholds",
  "custom_gates",
  "multi_method_order"
 ],
 "inputKeys": [
  "fail_at_smell_count",
  "max_args",
  "max_loc",
  "methods",
  "metrics",
  "name",
  "warn_at_smell_count"
 ],
 "normalizeSource": "def _code_smell_normalize(r):\n    if isinstance(r, str):\n        return r  # sentinel (invalid_metrics / invalid_thresholds) passes through\n    try:\n        return {\"rows\": [{\"name\": str(x[\"name\"]), \"smells\": [str(s) for s in x[\"smells\"]],\n                          \"smell_count\": int(x[\"smell_count\"]), \"score\": int(round(float(x[\"score\"]))),\n                          \"verdict\": str(x[\"verdict\"])} for x in r[\"rows\"]]}\n    except Exception:\n        return repr(r)\n",
 "returnShapes": [
  [
   "rows"
  ],
  "invalid_thresholds",
  "invalid_metrics"
 ],
 "signature": "def compute_code_smell_scorecard(inp):",
 "submission": "python exposing the entry function; inp is one input object; graded on held-out cases",
 "tier": "calculator",
 "visibleCases": [
  {
   "expect": {
    "rows": [
     {
      "name": "f",
      "score": 60,
      "smell_count": 2,
      "smells": [
       "LongMethod",
       "TooManyArguments"
      ],
      "verdict": "warn"
     }
    ]
   },
   "input": {
    "methods": [
     {
      "metrics": {
       "args": 5,
       "attributes": 4,
       "comment_ratio": 0.1,
       "loc": 45,
       "nesting": 2
      },
      "name": "f"
     }
    ]
   },
   "name": "two_smells_warn"
  },
  {
   "expect": {
    "rows": [
     {
      "name": "g",
      "score": 100,
      "smell_count": 0,
      "smells": [],
      "verdict": "pass"
     }
    ]
   },
   "input": {
    "methods": [
     {
      "metrics": {
       "args": 2,
       "attributes": 4,
       "comment_ratio": 0.2,
       "loc": 10,
       "nesting": 1
      },
      "name": "g"
     }
    ]
   },
   "name": "clean_pass"
  }
 ],
 "vocabulary": [
  "fail",
  "fail_at_smell_count",
  "invalid_metrics",
  "invalid_thresholds",
  "max_args",
  "max_attributes",
  "max_comment_ratio",
  "max_loc",
  "max_nesting",
  "methods",
  "metrics",
  "name",
  "pass",
  "rows",
  "score",
  "smell_count",
  "smells",
  "verdict",
  "warn",
  "warn_at_smell_count"
 ]
}