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Codemetry

Example Output

Table Output

Running php artisan codemetry:analyze --days=5 produces:

+------------+--------+-------+------------+------------------------------------------+
| Date       | Mood   | Score | Confidence | Top Reasons                              |
+------------+--------+-------+------------+------------------------------------------+
| 2024-01-15 | good   | 82    | 0.80       | Low churn; stable patterns               |
| 2024-01-14 | good   | 75    | 0.70       | Minimal follow-up fixes                  |
| 2024-01-13 | medium | 58    | 0.75       | Elevated scatter; fix density p85        |
| 2024-01-12 | bad    | 38    | 0.80       | High churn p95; 3 reverts; scattered     |
| 2024-01-11 | medium | 62    | 0.60       | Moderate churn; limited data             |
+------------+--------+-------+------------+------------------------------------------+

JSON Output

Running php artisan codemetry:analyze --days=3 --format=json produces:

{
  "schema_version": "1.0",
  "repo_id": "5d41402abc4b2a76b9719d911017c592",
  "analyzed_at": "2024-01-15T14:30:00+00:00",
  "request_summary": {
    "since": null,
    "until": null,
    "days": 3,
    "author": null,
    "branch": null,
    "timezone": null,
    "baseline_days": 56,
    "follow_up_horizon_days": 3,
    "ai_enabled": false,
    "ai_engine": "openai",
    "output_format": "json"
  },
  "windows": [
    {
      "window_label": "2024-01-15",
      "mood_label": "good",
      "mood_score": 82,
      "confidence": 0.8,
      "reasons": [
        {
          "signal_key": "change.churn",
          "direction": "positive",
          "magnitude": 8.5,
          "summary": "Churn well below p25 baseline"
        },
        {
          "signal_key": "followup.fix_density",
          "direction": "positive",
          "magnitude": 5.0,
          "summary": "Low follow-up fix density"
        }
      ],
      "confounders": [],
      "raw_signals": {
        "signals": {
          "change.added": {
            "key": "change.added",
            "type": "numeric",
            "value": 45,
            "description": "Lines added"
          },
          "change.deleted": {
            "key": "change.deleted",
            "type": "numeric",
            "value": 12,
            "description": "Lines deleted"
          },
          "change.churn": {
            "key": "change.churn",
            "type": "numeric",
            "value": 57,
            "description": "Total lines changed (added + deleted)"
          },
          "change.commits_count": {
            "key": "change.commits_count",
            "type": "numeric",
            "value": 4,
            "description": "Number of commits"
          },
          "change.files_touched": {
            "key": "change.files_touched",
            "type": "numeric",
            "value": 6,
            "description": "Unique files modified"
          },
          "change.scatter": {
            "key": "change.scatter",
            "type": "numeric",
            "value": 2,
            "description": "Unique directories touched"
          },
          "msg.fix_keyword_count": {
            "key": "msg.fix_keyword_count",
            "type": "numeric",
            "value": 0,
            "description": "Commits with fix keywords"
          },
          "msg.revert_count": {
            "key": "msg.revert_count",
            "type": "numeric",
            "value": 0,
            "description": "Revert commits"
          },
          "followup.fix_commits": {
            "key": "followup.fix_commits",
            "type": "numeric",
            "value": 0,
            "description": "Follow-up fix commits in horizon"
          },
          "followup.fix_density": {
            "key": "followup.fix_density",
            "type": "numeric",
            "value": 0,
            "description": "Fix commits per churn unit"
          }
        }
      },
      "normalized": {
        "norm.change.churn.z": -1.2,
        "norm.change.churn.pctl": 15.0,
        "norm.change.commits_count.z": 0.3,
        "norm.change.commits_count.pctl": 55.0,
        "norm.change.scatter.z": -0.5,
        "norm.change.scatter.pctl": 35.0,
        "norm.followup.fix_density.z": -0.8,
        "norm.followup.fix_density.pctl": 22.0
      }
    },
    {
      "window_label": "2024-01-14",
      "mood_label": "medium",
      "mood_score": 58,
      "confidence": 0.75,
      "reasons": [
        {
          "signal_key": "change.scatter",
          "direction": "negative",
          "magnitude": 10.0,
          "summary": "High file scatter at p90"
        },
        {
          "signal_key": "followup.fix_density",
          "direction": "negative",
          "magnitude": 8.0,
          "summary": "Elevated follow-up fix density"
        }
      ],
      "confounders": [],
      "raw_signals": { "...": "..." },
      "normalized": { "...": "..." }
    },
    {
      "window_label": "2024-01-13",
      "mood_label": "bad",
      "mood_score": 35,
      "confidence": 0.8,
      "reasons": [
        {
          "signal_key": "change.churn",
          "direction": "negative",
          "magnitude": 20.0,
          "summary": "Churn at p95+ percentile"
        },
        {
          "signal_key": "followup.fix_density",
          "direction": "negative",
          "magnitude": 15.0,
          "summary": "High follow-up fix density p90+"
        },
        {
          "signal_key": "msg.revert_count",
          "direction": "negative",
          "magnitude": 15.0,
          "summary": "Revert commits detected"
        }
      ],
      "confounders": [
        "large_refactor_suspected"
      ],
      "raw_signals": { "...": "..." },
      "normalized": { "...": "..." }
    }
  ]
}

With AI Explanations

When AI is enabled (--ai=1), each window includes an ai_summary:

{
  "window_label": "2024-01-13",
  "mood_label": "bad",
  "mood_score": 35,
  "confidence": 0.8,
  "ai_summary": {
    "explanation_bullets": [
      "This day shows significantly elevated code churn (95th percentile), indicating major changes or refactoring activity.",
      "The presence of revert commits suggests some changes needed to be rolled back, possibly due to issues discovered post-merge.",
      "High follow-up fix density indicates that files touched on this day required subsequent patches.",
      "The 'large_refactor_suspected' confounder suggests this may be intentional restructuring rather than quality issues.",
      "Consider reviewing the commit history to distinguish planned refactoring from rushed or problematic changes."
    ],
    "score_delta": 0,
    "confidence_delta": 0.0,
    "label_override": null
  }
}

Understanding Confounders

Confounders are context flags that help interpret results:

ConfounderMeaning
large_refactor_suspectedHigh churn but low follow-up fixes—likely intentional restructuring
formatting_or_rename_suspectedHigh churn with many files but few actual logic changes
ai_unavailableAI was requested but couldn’t run (missing API key, network error)
provider_skipped:*A signal provider failed and was skipped

Empty Windows

Days with no commits still appear in output:

{
  "window_label": "2024-01-14",
  "mood_label": "good",
  "mood_score": 70,
  "confidence": 0.4,
  "reasons": [],
  "confounders": [],
  "raw_signals": {
    "signals": {
      "change.commits_count": { "value": 0 }
    }
  }
}

Empty days get:

  • Base score of 70 (neutral)
  • Low confidence (limited data)
  • No reasons (nothing to report)